3.4 Aerosol-radiation feedback loop in west Siberia
3.4.1 Impacts of daily median temperature, solar zenith an-
The comparison of the current temperature on aerosol particles and the past 24 hours median temperature on aerosol particles indicates there is no signif-icant difference between correlation coefficient of R against current tempera-ture and the past 24 hours median temperatempera-ture. Furthermore, as can be seen from Figure 3.6, which is coloured with the difference of days from summer solstice (21st June), changes in solar zenith angle and path length of arriving radiation to the surface does not affect diffuse radiation and consequently the relation between temperature and R. In this figure, negative values represent days before 21st June and positive values are days after 21st June.
FIGURE 3.6: Ratio of satellite-based diffuse radiation (Rd) to global radiation (Rg), as a function of re-analysis temperature for a 1x1 degree region around Hyytiälä station during day time (closest to 12PM) in June and July from 2000 to 2016. The solid line shows the least-squares exponential fit to measurements points. Colour scale indicates difference of days from 21st June.
Figure 3.7 shows satellite-based R as a function of re-analysis tem-perature, coloured by RH from 2000-2016 in June and July around noon in Hyytiälä station. As can be seen from Figure 3.7, the swelling effect can impact the relation between R and temperature as the high values of RH cor-respond to higher R values.
FIGURE 3.7: Ratio of satellite-based diffuse radiation (Rd) to global radiation (Rg), as a function of re-analysis temperature for a 1x1 degree region around Hyytiälä station during day time (closest to 12PM) in June and July from 2000 to 2016. The solid line shows the least-squares exponential fit to measurements
points. Colour scale indicates relative humidity.
Figure 3.8a shows the high values of RH are mostly located above the fitted line and the low values of RH are placed under the fitted line. As can be seen from Figure 3.8b normalizing R with RH improved the corre-lation coefficient between R and temperature in comparison with previous results in Figure 3.5. Same result was obtain for Siberia (Table 3.4).
(A)
(B)
FIGURE3.8: The ratio between satellite-based R (ratio of diffuse radiation to global radiation) and the fitted line equation (f(T1)) from satellite-based R vs re-analysis temperature (from Figure 3.5) as a function of relative humidity (RH) (A), (B) the ratio of satellite-based R to the fitted line equation (f(RH1)) from plot of satellite-based R/ f(T1) vs RH as function of temperature in
Hyytiälä station.
(A)
(B)
FIGURE 3.9: The ratio of satellite-based R (ratio of diffuse ra-diation to global rara-diation) to the fitted line equation (f(RH2)) from plot of satellite-based R/ f(T2) vs RH as function of tem-perature in (A) Hyytiälä station and (B) west Siberia (Hyytiälä
parameterization was used with Siberia data).
Table 3.4 shows the statistical analysis of the fitted line tions. There is a strong correlation between R and the product of parametriza-tion (fit(RH).fit(T)) in both Hyytiälä and Siberia data, which confirms that the parameterization works well here. In addition, there is a stronger corre-lation in Siberian data with the Hyytiälä parameterization than the Siberia
parameterization. A possible explanation for this might be that to obtain the Hyytiälä parametrization, in-situ RH was used, unlike the Siberia parame-terization where RH was calculated from re-analysis data.
TABLE 3.4: Correlation coefficient of fitted line parametriza-tions in Hyytiälä and Siberia for clear sky condiparametriza-tions at noon.
Where f(T1) and f(RH1) refers to first step of parameterization and f(T2) and f(RH2) refers to second step of parametrization, using iteration. This table shows exponential correlational anal-ysis of relation between ratio of satellite-based R (ratio of dif-fuse radiation to global radiation) to f(T) against RH, as well as ratio of satellite-based R to f(RH) against re-analysis tempera-ture. In addition, linear correlational analysis between
satellite-based R and product of parametrization is shown here.
Location of data r
Satellite-based R/f(T1) vs. RH Hyytiälä 0.51
Satellite-based R/f(RH1) vs. re-analysis
temperature Hyytiälä 0.56
Satellite-based R vs. f(RH1).f(T1) Hyytiälä 0.64
Satellite-based R/f(T2) vs. RH Hyytiälä 0.52
Satellite-based R vs. f(RH1).f(T2) Hyytiälä 0.65 Satellite-based R vs. f(RH2).f(T2) Hyytiälä 0.65
Satellite-based R/f(T1) vs. RH Siberia 0.31
Satellite-based R/f(RH1) vs. re-analysis
temperature Siberia 0.34
Satellite-based R vs. f(RH1).f(T1) Siberia 0.42
Satellite-based R/f(T2) vs. RH Siberia 0.32
Satellite-based R/f(RH2) vs. re-analysis
temperature Siberia 0.35
Satellite-based R vs. f(RH2).f(T2) Siberia 0.43 Satellite-based R/f(RH2) vs. re-analysis
temperature
Siberia with Hyytiälä parametrization 0.38 Satellite-based R vs. f(RH2).f(T2) Siberia with Hyytiälä
parametrization 0.44
4. Conclusions
The main goal of this study was to analyse the aerosol-radiation feedback loop associated with the COBACC feedback loop using satellite data. This feedback loop connects increasing atmosphericCO2concentration, rising tem-peratures, the formation of aerosol particles due to the emission of BVOCs, the change of ratio of diffuse to global radiation in the clear sky condition, and changes in GPP. The aerosol-radiation feedback loop is hypothesized to be negative for theCO2 concentration increase. The aerosol-radiation feed-back loop was investigated here by analysing in-situ atmospheric measure-ment data as well as satellite atmospheric data over Hyytiälä station and a small area in the western plain of Siberia for clear sky conditions in June and July around noon. Here, a region of 1x1 degree in both Hyytiälä and the west part of Siberia was considered.
The first aim of the present research was to investigate the existence of the aerosol-radiation feedback loop with utilizing in-situ data. For this purpose the relations between the components of aerosol-radiation feedback loop were studied, namely temperature and condensation sink (CS), CS and ratio of diffuse radiation to global radiation (R), and R and temperature.
The second aim of this study was to investigate whether it is possi-ble to study the aerosol-radiation feedback loop globally with using satellite data. To achieve this purpose, satellite data was used to investigate whether the same results can be obtained as results from in-situ data. Furthermore, in
this investigation, the impacts of solar zenith angle and RH on the relation-ship between R and temperature were studied.
One of the main findings to emerge from this study is that the aerosol-radiation feedback loop was confirmed due to finding positive correlation be-tween different components of the aerosol-radiation feedback loop by using in-situ data, namely CS and temperature, CS and R, and R and temperature in this study, whereas the connection between R and GPP has been shown in previous studies (for example, Kulmala et al. (2013)).
The second finding was that there is positive correlation between satellite-based data and in-situ data in Hyytiälä station, namely diffuse radi-ation, global radiradi-ation, and temperature. Furthermore, the investigation of the relation between CS versus temperature or R, as well as R versus tem-perature show better results with satellite data than in-situ data. Taken to-gether, these findings confirmed that satellite-based data compares well with ground-based data.
The results showed that there are very similar results for the relation between R and temperature in both Hyytiälä and the west plain of Siberia.
This major finding suggests that the aerosol-radiation feedback loop can be expanded globally for boreal forests by using satellite data.
The research has found that solar zenith angle has no influence on the relation between R and temperature during study period (June – July).
Whereas the investigation of the impact of RH on the relation between R and temperature showed that it is important to take into account the swelling effect in order to inspect this phenomenon.
Further research should focus on determining the relation between R and temperature by considering air-mass trajectories. It would be useful to investigate more about the properties of aerosol particles and divide them to clean and polluted origins.
Considerably more work will need to be done to quantify all the steps of aerosol-radiation feedback loop such as the evaluation of the the plant gross primary production (GPP), which measures photosynthesis, and ratio of diffuse radiation to global radiation step with satellite data. This step represents a change in R due to an increase in diffuse radiation possibly lead-ing to plants becomlead-ing more photosynthetically active and likely increaslead-ing GPP.
A. Appendix
(A)
(B)
FIGUREA.1: (A) Satellite-based global radiation (Rg) as func-tion of in-situ Rg (B) Satellite-based diffuse radiation (Rd) as function of in-situ Rd in June and July from 2000 to 2016 and
2000 to 2009, respectively, in Hyytiälä station
FIGUREA.2: Satellite-based ratio of diffuse radiation to global radiation (Rd)/(Rg) as function of in-situ (Rd)/(Rg) in June and
July from 2000 to 2009 in Hyytiälä station
FIGURE A.3: Re-analysis temperature as function of in-situ temperature in June and July from 2000 to 2009 in Hyytiälä
sta-tion
(A)
(B)
FIGURE A.4: Satellite ratio of diffuse radiation (Rd) to global radiation (Rg) as a function of the past 24 hours median re-analysis temperature for a 1x1 degree region around Hyytiälä station (A) and west Siberian plain (B) during day time (clos-est to 12PM) in June and July from 2000 to 2016. The solid line shows the least-squares exponential fit to measurements points.
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