By Richard Willoughby
Summary
This technical note examines the relationship between the measured surface temperature and the calculated solar electromagnetic radiation (EMR) available at the top of the atmosphere (ToA) that varies due to the change in the orbit of the Earth. The earth.
The four regions were examined and analyzed using monthly averages for temperature and solar EMRs available at ToA. Figure 1 shows the tested areas.
The areas are:
- Central US uses GHCN . temperature measurement
- Mediterranean Sea using NCEP / Reynolds Sea Surface (excluding land in the area)
- Nino34 area of Equatorial Pacific using NCEP / Reynolds
- Southern Ocean 55S to 65S using NCEP / Reynolds (NCEP assumes water under ice is -1.8C)
For each zone, monthly measured time-based data for temperature and calculated solar EMR are displayed as well as the correlation between the two regions. There are also time-shifted correlations that indicate the endothermic response of each region. Responses to solar coercion vary considerably but the correlations outside the tropics are high:
- Central US requires 13.5W/m^2 per Centigrade degree; Regression coefficient 97%
- Mediterranean requires 28.5W/m^2 per degree Celsius. 96% regression coefficient
- Nino34 region required minus 40W/m^2 per degree Celsius. 14% regression coefficient
- The South Sea requires 192W/m^2 for every degree Celsius. 93% regression coefficient.
Both the solar EMR and the surface temperature in the Nino34 region are subjected to frequency analysis to provide insights into other factors affecting the surface temperature beyond the solar EMR.
Southern Sea
December Solar EMR at 60S peaked 3,800 years ago. It has declined since then. Figure 2 shows average monthly temperatures for both solar EMR and surface temperatures from 55S to 65S for all longitudes.
Figure 3 looks at the correlation between temperature and EMR.
There is a relatively weak positive correlation but there are signs of phase difference.
Figure 4 shows how the correlation is significantly improved by delaying the EMR by two months from the measured surface temperature. It takes 192W/m^2 change in EMR to move the surface temperature 1C.
Mediterranean Sea
The average April solar EMR at 37N was at a minimum of 374W/m^2 5,000 years ago. Now it is 387W/m^2. April EMR will peak next April at 399W/m^2 3,500 years from now. The following Figures 5, 6 and 7 show the EMR and temperature analysis for the Mediterranean Sea.
The linear correlation has a regression coefficient of 96%. A change of 28.5W/m^2 is required to change the surface temperature by 1C.
Central America
Given the good correlations between ocean surfaces, it is educational to consider a land surface region with good coverage of reliable temperature records. Figures 8, 9 and 10 show the results.
The correlation between months is much better for the land surface than for the ocean, but there is a clear loop. The best correlation is achieved with only 1 month lag.
The correlation of 97% for land is better for the ocean although the annual temperature range is much wider for the same range in the solar EMR; requires only 13.5W/m^2 to move 1C surface temperature.
Nino area 34
The temperature response to EMR in the Nino34 region shown in Figure 11 is quite different from the higher latitudes so far considered.
There is obviously some annual cycle, but it is clearly poorly correlated with EMR change as shown in Figure 12.
Notably, the correlation is negative. In fact, the correlation improved by elevating the EMR.
Regression coefficient 14% shows high confidence, the correlation is negative. The fact is that the EMR conduction temperature matches the negative feedback based on the surface temperature rather than the actual temperature controlling the sun.
Temperature trends in the region display a definite long-term span, shown in the frequency analysis in Figure 14.
Annual peaks predominate but there are other broad peaks at about 10.7 years and 22.3 years using only temperatures from the satellite epoch.
Figure 15 shows the frequency analysis for the calculated solar EMR based on the orbital geometry alone.
It is clear that there is no consistent peak over a period of about 11 years so it can be concluded that the cadence in the temperature record is not related to the orbital change.
Figure 16 shows the time variation of temperature with the number of monthly sunspots.
There may be some correlation, but it’s not obvious in this graph. And frequency analysis of sunspots shows peaks at periods similar to those of temperature measurements.
The month-to-month correlation between temperature and the number of sunspots in Figure 18 is insignificant but becomes significant when the number of Sunspots is 31 months behind in Figure 19.
Conclusion
It has been shown that land and ocean surface temperatures are highly correlated with solar EMR at mid and higher latitudes; provide adequate allowances for the different thermal inertia of land and ocean.
It should be further noted that trends observed in reliable surface temperature records that can be forecasted by monthly solar EMRs of the region are changing. Figure 20 shows how solar EMR has changed over the past 520 years and will change over the next 80 years at 30N.
Over the past 520 years, April EMR experienced its highest monthly gain at 30N and will continue to do so, increasing by nearly 3W/m^2 over a 600-year timeframe. In contrast, September EMR is decreasing and will decrease by about 2.6W/m^2 over a 600 year period. Overall, there is a slight upward trend in average annual EMR at 30N that will continue.
It has also been observed that land surface temperature responds in half the time and twice the extent of latitude-restricted deep waters such as the Mediterranean Sea to the same EMR variation. Thus, it is predictable, as solar EMR increases in the Northern Hemisphere, while decreasing in the Southern Hemisphere, the global mean surface temperature will increase due to the higher ratio of land and water in NH compared to SH.
Average solar intensity over the Southern Ocean is steadily declining but the decline is most evident in November, reducing peak summer surface temperatures. Figure 21 shows how the solar EMR has changed during the satellite era from 1980 to 2022 and will continue to change through 2040.
The Nino34 region lies in the temperature-limited region where the temperature is negatively correlated with the solar EMR. It is highly likely that there are solar factors other than orbital changes affecting the temperature in the region.
Data sources
The data used to determine the atmospheric peak reflectance EMR was downloaded from the NASA Earth Observation website.
The GHCN and SST overland air temperature data measured by NCEP were downloaded from the Climate Explorer website.
http://climexp.knmi.nl/start.cgi
The orbital parameters to determine the changes in the ToA solar EMR come from the site Astropicxels:
Planetary meteorite data (astropixels.com)
Sunspot number data were obtained from the Royal Observatory Sunspot Index Website and the Long Term Solar Observation (SILSO) Website:
https://sidc.be/silso/datafiles
Although the solar EMR is calculated from orbital data, there is a useful reference for determining isolation at nominated latitudes and months using the insola IMCCE web model:
http://vo.imcce.fr/insola/earth/online/earth/online/index.php
Author
Richard Willoughby is a retired electrical engineer who has worked in the Australian mining and mineral processing industry for 30 years in roles in large-scale operations, corporate R&D and mine development. Another ten years were spent in the global insurance industry as a technical risk consultant, where he developed an enduring interest in natural disasters and climate change.
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