Data trends at meteoLCD: 1998 to 2022


Trends computed from yearly averages at meteoLCD, Diekirch, Luxembourg.
Graphs may be freely copied and used, under the condition to cite:
MASSEN, Francis: Data trends at meteoLCD, 1998 to 2022. https://meteo.lcd.lu

Older trends are here!

Attention: in all trend equations (y = a+b*x) the variable x represents the year, with x = 1 for the first year in the trend period (usually 1998 or 2002).

Most important conclusions from 1998 (2002) to 2022:

1. Overall increase in sunshine duration since 1998 by 45 hours*decade-1 , (but decrease in 2021).
2. Local temperatures show warming of 0.015C/y since 2002 (2018 was a strong El Nino year), a spectacular cooling during 2021 followed by strong warming in 2022
3. Diurnal temperature range (DTR) trend since 1998 is positive (= no anthropogenic warming fingerprint ).
4. The winter trend since 2002 shows a warming of +1.3 C*decade-1 ; this positive trend is also shown by the winter NAO index (+0.3 decade-1)
5. Since 1998 the ground O3 trend is positive (rise in 2022 is suspicious),
 from 2002 to 2022 the total thickness of the ozone layer slightly decreases by 8.1 DU*decade-1
6. Local CO2 mixing ratio continues to increase at about 5.4-5.5 ppmV per year; the asymptotic background is ca. 7 ppm higher to that measured at Mauna Loa (latitude adjusted by + 2ppmV).
7. The trend of the biologically effective yearly UVB dose is distinctly positive from 1998 to 2022 (like solar energy and UVA dose); note visible decrease in all solar parameters in 2021.
8. The trend of the UVA dose is distinctly positive from 1998 to 2022
9. Precipitation (rainfall) shows a sinusoidal pattern of close to a 5.5 years (66 months) period.
10. Energy content of moist air (enthalpy) shows a positive trend since 2002
11. The fine particles PM2.5 concentrations are low, and are well synchronized with those at Beidweiler.
 
NO/NOx measurements have been definitively stopped at the end of 2017.

Ground Ozone [ug/m3]
("bad ozone")

Mean +/- stdev of
2022: 66.2 +/- 36.6 ug/m3 
 

Mean +/- stdev (from yearly means)
1998 to 2022: 43.1 +/- 9.3 ug/m3
2002 to 2022: 43.1 +/- 10.1

Trends 1998 - 2022: 35.39+0.60*x (be careful: API400 values may be too low!)
             2018 - 2022: Cairsens sensors; no trend calculated as 2022 value seems too high.

Please note the succession of 3 different instruments
2002 - 2004: O341M (Environnement SA)
2005- 2014: API400 (Teledyne)
2015-2017: O341M
2018 - 2022: Cairsens O3&NO2 (Environnement SA). The sensor is replaced every year by a new one.
As NO2 values are below the minimum of this instrument, readings can be taken as O3 only (in ambient air: no correction for temperature and pressure!).
The official Beckerich  station data are used for calibrating the Cairsens readings (from time to time).

 

See [1] [2] [3] [17]

Total Ozone Column [DU]
("good ozone")

Mean and stdev of the year 2022: 320.8 +/- 33.6 DU (180 days)
minimum : 234.1 (27 Oct )
maximum: 448.6 (14 Mar)

The overall trend is positive, but the trends from 2002 to 2022 (~2 decades) and that from 2013 to 2022 (last decade) are negative.
The slopes of all these trends are very small, so don't be fooled by the plots!

Uccle DS readings, MKIII Brewer (or MKII when MKIII unavailable): 328.9 +/- 35.4 (291 days)
minimum : 249.7 (27 Oct )
maximum: 444.0 (14 Mar)

164 common day direct sun readings for 2022 at Uccle and Diekirch (see second plot, Diekirch measurements are excellent!):
Diekirch                    : 322.2 +/- 32.9
Uccle                         : 331.5 +/- 32.2


Uccle
 has a slight positive trend of +3.6 DU/decade for the 1998-2019 period, and a similar of +3.9 DU/decade from 2010 to 2019.(see also [16])

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Calibration multiplier to apply to the Diekirch DU data [55] and [56] if Uccle Brewer(s) are the reference: 1.02755 (R2 =0.87)

Once more, this year measurements show how good the Microtops II instrument is compared to the enormously more expensive Brewers!




See [4] [8] ([8] shows strong positive trend starting 1990 for latitudes 45-75 North, Europe): [27] give +1.32 DU/y at the Jungfraujoch for 1995-2004.
See also recent EGU2009 poster [16].


CO2 mixing ratio in ppmV 
 

Attention: The instrument for measuring CO2 (API Teledyne E600) has been replaced by a Vaisala GMP343 sensor the 27 Jun 2017. The jump from 2017 to 2018 seems implausible high, so a zero bias should be considered possible!

Mean and stdev of the year 2022: 441.95 +/- 8.76 ppmV (yearly average of the Airvisual Pro CO2 sensor = 430.5 ppmV)

The 1998-2001 data are too unreliable to be retained for the trend analysis.

Trends :
2002 to 2020: 0.5
ppmV*y-1
2018 to 2022: 5.5 ppmV*y-1


The sharp plunge in 2013 should be taken with caution; there also was a change in the calibration gas the 21 Jan. 2014.
 

 

 

 

 

 

 

The second picture zooms on the last 4 years, and gives the readings of Diekirch (DIK), Mauna Loa (MLO)  from 2018 to 2021; and Hohenpeissenberg (HPB) from 2018 to 2021 as 2022 data are not yet available for HPB. Note the very different elevations! Mauna Loa has no vegetation at all, Diekirch and HPB similar grass and forests.

The yearly trends are for this period are

Diekirch
219m asl
semi-rural
+5.8 (Vaisala GPM343)  ppmV/year  
Manua Loa
(MLO)
3397m asl, no vegetation
+ 2.51 ppmV/year
[34] [57]

Hohenpeissenberg (HPB)
977m asl, forests
+ 2.89 ppmV/year (no 2022 data yet available) [48] [57]

Be careful with the Vaisala readings, as the Vaisala GPM343 might not give the same accuracy as the former API! These readings also are given for local atm. pressure and non-dried air!

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The CO2 data (monthly averages) show the summer-time lows, which reflect the impact of variable seasonal photo-synthesis (see here). A simple 12 month periodic sinus pattern was also found in 2014, 2015, 2021. Actually, as shown in addendum 3, the CO2 lowering intensity of wind speed seems to be an important modifier of this pattern, possibly masking the effect (or better: the non-effect) of photosynthesis. This happened in 2016 and 2017.

This year 2022 the yearly amplitude of the sinus fit is 8.6 ppmV (a total swing of ~17.2 ppmV, to be compared to about 12.4 ppmV at the HPB station for 2019 [48]).
The R2 is not exceptional high, but the summer minimum clearly stands out.

Note the large difference between the maximum October and minimum August readings: 472.6 - 435.4 = 37.2ppmV
 

 

 

 

 

 

 

 

 

 

 

 

Asymptotic CO2 mixing ratio (Massen-Beck model)

Addendum 3  describes our model to calculate an asymptotic CO2 mixing ratio. The plot shows how these values vary since 2018 (Vaisala sensor), 
suggesting a yearly increase of 5.4 ppmV since 2018 (practically the same as given by the reading data).

 

 

.

 

Air temperature [C]

Attention: the change of temperature Pt100 sensor introduced a bias of +1.61, apparent since the 2017 series, and
visible when comparing the airtemp with dry bulb temperature readings.
The yearly average airtemp readings (as given in the data files like 2022_only.xls) have been corrected by
subtracting this bias starting 2017!

Mean of the year 2022 (from monthly averages):
Diekirch:  11.08   (corrected for bias)

Findel:      11.18


Mean Diekirch temperatures (+/- stdev) from yearly averages:

1998 to 2022 :  10.39 +/- 0.50 C      (corrected for bias)          
2002 to 2022 :  10.44 +/- 0.22  C     (corrected for bias)
 

The sensor location has not been moved since 2002! Sensor is a PT100 (see comments in 2015_only.xls); new 4-20mA amplifier (with calibration) installed the 4th May 2016.

Trends  from 2002 to 2022 (be aware that 2018 was a very strong El Nino year!), see 2nd plot:
meteoLCD:    +0.015 C/year (0.15 C/decade)
(corrected for bias)
Findel:            +0.046 C/year  (0.46 C/decade)

Note the sharp cooling in 2021 and the warming in 2022.         

Latest Global temperature anomaly trends for same period 2002-2022 (base:1991 - 2020):
UAH (satellite, LTT):          + 0.14C/decade (Global Land)   [62]
RSS (satellite, LTT)   :    
CRU (Hadcrut4krig v2):  


Highest decadal Central England warming trend from 1691 to 2009: +1.86C/decade for 1694-1703!
See also [15] (which may be obsolete)

Diurnal Temperature 
Range (DTR)  [C]

DTR = daily max - daily min temperature

Mean and stdev of the year 2022 (from monthly averages):
Diekirch: 9.84 +/- 3.56
Findel:    8.58 +/- 2.93

Mean DTR at Diekirch:

1998 to 2022:
 8.71
2002 to 2022:  8.32
 

For 1998 to 2022: all trends are positive, the 24hmin trend is lower than the 24hmax trend.

A fingerprint of anthropogenic climate warming is that daily minima increase more rapidly than daily maxima
so that the DTR trend should become negative. This fingerprint does not exist here (and neither at the Findel station, see 2nd plot).


 

The BEST observational data set for Luxembourg [29] stops at 2013. For our latitude of 50 North, BEST shows a positive DTR trend for the period 1988 to 2011, whereas theCMIP5 multi-model mean gives a similar but negative trend... so much for the concordance between climate models and observations!

See [5], [13] and [30]

Winter temperatures [C]
Values of winter DJF temperature of the year 2022:
(Dec2021, Jan & Feb2022), all corrected for bias

Diekirch: 3.83
Findel:     2.37
DE:          3.3 (avg. Germany)
NAO:       0.19
 NAO normalized index [47]

The trends show warming winters since 2002 to 2022, with the warming probably caused by the NAO; the cooling for winter 2021 and warming for following winter 2022 seems to confirm this.

Trends  from 2002 to 2022:
(2016 was a very strong El Nino year!):
Diekirch:    +0.13 C/year
Findel:       +0.11
Germany:  +0.10   [46]
NAO:         +0.03

The plot shows the mean temperatures from December (of previous year) to February. It also shows in magenta the NAO index for the months Dec to Feb (right y-axis)

The North Atlantic Oscillation clearly influences our winters (but note the exception for 2015 and 2019! [51]).

Enthalpy of moist air in kJ/kg

Mean moist enthalpy of 2022: 32.00 +/- 13.28 kJ/kg (corrected for Air_temp bias of 1.6C)

See [24] on how the energy content of moist air is calculated. Several authors, (e.g. Prof. Roger Pielke Sr.) insist that air temperature is a poor metric for global warming/cooling, and that the energy content of the moist air and/or the Ocean Heat Content (OHC) are better metrics.

Mean yearly moist enthalpy values are mostly very close, but they may change from zero up to 60 KJ/kg during a year. Moist enthalpy can not be calculated for temperatures <= 0 C.

mean +/- stdev from 2002 to 2022:  28.35 +/- 1.34 kJ/kg

Trend is positive from 2002 to 2022; the 2016 "monster" El Nino extended into 2017 . Note the sharp rise in 2022!

 

Total Yearly Rainfall [mm]

Values of rainfall (precipitation) of the year 2022:
Diekirch:  459.4.0 mm
Findel:      637.2 mm

1998 - 2022 mean +/- stdev: 680.6 +/- 130.8 mm 
2002 - 2022 mean +/-stdev:  646.4 +/-  104.0 mm
 

The negative trend from 1998 to 2018 seems spectacular: -85 mm/decade, but it is caused by the very high values of 2000 and 2001.
The 2002-2021 period has a much more small trend of -33 mm/decade.

Note that the very dry year 2022 is similar to 2005 !

 

 

 


 

 

 

 

 

Clearly precipitation shows an oscillation pattern, so linear trends should be taken with precaution (or simply seen as nonsense).

 A good model for the Diekirch data is a sinus function: the calculation (Levenberg-Marquart algorithm) suggests for the interval 2002 - 2022 a 5.55 years period (~66 months, R2 = 0.38); in the model x = 0  corresponds to 2002), with a mean value of 648 mm and an amplitude of 87 mm; the phase shift of 1.5 rad is close to 1/2 period. All these values are similar to those of the preceding  year. All 4 parameters are significant at the alpha = 0.95 level!
[6]
gives short term periods of 6 to 7 years for the Western European region.

The oscillatory rainfall pattern is a good example how foolish it is to apply linear regressions to data when these are harmonic, something the media, activist groups and many politicians often do without much thinking.

Solar energy on a horizontal plane

Values of total solar energy of the year 2021:
Diekirch: 1266.8 KWh/m2
 
(was 1113.5 in 2021)
 

1998 to 2022 mean +/- stdev: 1125.2 +/- 57.1 kWh*m-2*y-1
2002 to 2021 mean +/- stdev: 1126.9 +/- 61.4 kWh*m-2*y-1


Trends from 1998 to 2021 is small, and relative important (~7.2 kWh*m-2*y-1) for last 20 years. [52]

(see Addendum 1 for calculations of radiative forcing and solar sensitivity, addendum 2 for detecting a solar influence on temperature and moist enthalpy)

Helioclim satellite measurements show ongoing solar dimming over Luxembourg for 1985 to 2005  [33] (see graph)
[14] finds 0.7 Wm-2y-1 for West-Europe 1994-2003 , meteoLCD +1 Wm-2y-1 for 1998-2003.See also [9]

Sunshine hours
(meteoLCD values derived from pyranometer data by Olivieri's method)

Values of sunshine hours of the year 2022:


Diekirch:      2098 hours (215m asl) (from pyranometer)
Findel:          2234 hours (365m asl, (from Campbell- Stokes?, known to measure in excess)
Trier:            2119 hours (279m asl)
[40] [53]
(Petrisberg)
Maastricht: 2139 hours (114m asl) [53]

 Trends:
1998 to 2022:   +45.3 hours/decade
2002 to 2022:   +6.4 hours/decade !

The decline from 2015 to 2017 is clearly visible in the German PV electricity production, but the negative trend reverses for the very sunny year 2018 [58].

Note the sharp decline in 2021.

See paper [23] by F. Massen comparing 4 different methods to compute sunshine duration from pyranometer
 

 

 

 

 

 

 

The 2nd graph shows the plots of the four above-mentioned stations. It should be noted that meteoLCD (Diekirch) is located in a valley, Findel, Trier and Maastricht airport on top of a plateau. The Findel totals are much higher than those of the other stations, which certainly is also partially caused by the use of the Campbell-Stokes instrument known to give too high readings.

All 4 stations give totals that practically always vary in the same manner (synchronous increase and decrease).

The trends of all 4 stations are positive since 1998 and  similar, except Diekirch being lower

All stations show distinct decline in sunshine hours in 2021, and a 2022 total close to that of 2003.

 

 

 

 

 

 

 

 

These positive trends probably suffice to explain the warming since 1998:

The correlations between mean temperature are yearly sunshine hours are positive for the 4 stations, and these correlations are statistically significant at the alpha = 0.95 level for all stations:

Diekirch: 0.45, Findel 0.60, Trier 0.65, Maastricht 0.48

See the last multi-graph figure for the temp-versus-sunshine relationship at the 4 stations meteoLCD, Findel airport, Trier-Petrisberg and Maastricht airport.

 

Biologically eff. UVB dose on a horizontal plane in eff.kWh/(m2*y)

Erythemal UVB dose of the year 2021: 0.131 eff. kWh/m2
(was 0.131 in 2021)


mean +/- stdev:
1998 to 2022: 0.134 +/- 0.010   eff. kWh*m-2y-1
2002 to 2022: 0.135 +/- 0.009
 

The trend over 2002 - 2022 is slightly positive, in concordance with solar irradiance and sunshine hours.
 

.

See [10] and [22] (poster finds slight positive trend in June (+2%) and negative trend in August (-1%), no trend for other months, for period 1991 to 2008.

 

UVA dose on a horizontal plane in kWh/(m2*y) 

UVA dose of the year 2022: 58.7 KWh/m2
(was 52.5 in 2021)
Some intermittent problems with internal temperature stabilization of the sensor;
the influence seems minimal, so all readings have been kept.

mean +/- stdev:
1998 to 2022: 55.00 +/- 4.63 kWh*m-2*y-1
2002 to 2022: 55.70 +/- 4.10



 

The 3 independent measurements of  solar energy, sunshine hours, eff.UVB and UVA doses
all point to an increase since 1998. All are of similar magnitude than those of the heatwave year 2003.

Fine particle measurements at meteoLCD

An Airvisual Pro sensor from iQAir has been operational for the full year since 2019. Besides temperature, humidity and CO2, this sensor also measures PM2.5 and PM10 concentrations in ug/m3. All data are stored in a cloud managed by iQAir (https://airvisual.com/luxembourg/diekirch/diekirch/meteolcd shows only the PM2.5 concentrations. The measuring principle for the PM is LLS (Laser Light Scattering). Ambiant air is sucked into the measuring chamber by a small fan running continuously; there is no air drying nor correction for pressure variations. It has been found that the most important correction for this type of sensor is the humidity correction, as above 70% RH condensing water on the aerosol particles inflates the count and the reported mass. At meteoLCD we divide the raw data by a growth factor GF =a+(b*RH**2)/(1-RH) with a=1 and b = 0.25 and RH = RH%/100.

Read the article "One year of fine particle measurements by Airvisual Pro at meteoLCD" on the blog meteolcd.wordpress.com [ref. 59]

The next plot shows the PM2.5 readings per day. The green line represents the raw readings, the red line the corrected readings (division by growth-factor).

Yearly average +/. stdev:
raw PM 2.5 readings: 10.3 +/-10.7 ug/m3
corrected readings:       6.7 +/-  7.2 ug/m3

Maximum of daily average:
raw PM 2.5 readings: 62.7  ug/m3 (19-Dec-2022)
corrected readings:    35.8  ug/m3

 

The correspondence of the PM 2.5 measurements is very satisfying, and the peak situations mostly coincide. It must be noted that Beidweiler (black plot) has missing data; these missing data have been filled-in by repeating the last readings preceding the gap. Comparing the raw (magenta) curve with Beidweiler (black plot), it is obvious that a correction for humidity is mandatory especially during the humid autumn, winter and spring days.

The PM10 readings of the Airvisual Pro are very close (too close!) to it's PM2.5, and as such considerably different if compared to the Beidweiler readings; this same behavior has been shown by the previous Airvisual instrument.They will not be retained in this analysis.

A general conclusion is that fine particles do not present any health risk at Diekirch, and are substantially lower than the annual  interim target1 in the last WMO guidelines [61]  which are 35 ug/m3 for PM2.5 (24h short term is 75) resp 70 and 45 short-term for PM10. 

An exceptional situation is shown by the peak in late December, when several houses in the close neighborhood were probably burning wood.

 

 

NOx, NO and NO2 concentration in ug/m3

(End of measurements useable for trends in 2013. Measurements stopped in 2017).

Attention: only 78% of possible measurements available due to sensor downtime!
Comparison between yearly averages at Diekirch, Luxembourg-Libert and Vianden (rural) [39]; Luxembourg and Vianden values derived by inspection from graphs):
 

ug/m3

NOx NO2 NO maximum of daily avg. NO2
Diekirch 28    23 5   62
Luxembourg-Libert   ~ 50   ~ 82
Vianden   ~ 10   ~40


The NOx/NO measurements by the AC31M instruments from Environnement SA have been stopped the 30 December 2017. The AC31M has reached its end of life.



 

see [11] which gives ~30% reduction from 1990 to 2005 for the EU-15 countries.

References:

1 Europe's Environment 4th AR (2007) Fig. 2.2.3 http://reports.eea.europa.eu/state_of_environment_report_2007_1/en/Belgrade_EN_all_chapters_incl_cover.pdf
2 EPA: Ozone trends. http://www.epa.gov/airtrends/ozone.html
3

Jonson et al: Can we explain the trends in European ozone levels? Atmos. Chem. Phys. Discuss., 5, 59575985, 2005. http://www.atmos-chem-phys-discuss.net/5/5957/2005/acpd-5-5957-2005.pdf

4 Ozone trends at Uccle  http://ozone.meteo.be/ozone/trends.php
5 Rebetez, Beniston: Analyses of the elevation dependency of correlations between sunshine duration and diurnal temperature range this century in the Swiss Alps. 1998.
6 R.G. Vines, CSIRO: European rainfall patterns. International Journal of Climatology, vol.5, issue 6, p. 607-616.
7 http://global-warming.accuweather.com (15 Jan 2009).
8 J.W. Krzyscin, J.L.Borkowski: Total ozone trend over Europe: 1950 - 2004. ACPD, 8, 47-69, 2008.
9 NASA: Solar Physics: The Sunspot Cycle.
10 de Backer et al: ftp://ftp.kmi.be/dist/meteo/hugo/posters/20080630tromso_hdb.pdf (temporarly unavailable)
11 EEA: Emission trends of NOx 1990 - 2005
12 L. Motl: http://motls.blogspot.com/2009/12/uah-msu-temperatures-for-2009-and.html . Dec.2009
13 K. Makovski: The daily temperature amplitude and surface solar radiation..Dissertation for the degree of doctor of sciences. ETHZ 2009.
14 A. Ohmura: Observed long-term variations of solar irradiance at the earth's surface. Space Science Reviews (2006) 125: 111-128
15 J. van Oldenvorgh: Western Europe is warming much faster than expected. Clim.Past. 16Jan.2009
16 Van Malderen, De Backer, Delcloo: Revision of 40 years of ozone measurements in Uccle, Belgium. Poster, EGU2009, Vienna.
17 EEA: Air pollution by ozone across Europe during summer 2009
18 Climate4 you: Global temperature trends
19 Lindzen & Choi: On the determination of climate feedback from ERBE data (GRL, 2009)
20 Scafetta, N.: Empirical analysis of the solar contribution to global mean air surface temperature change. Journal of Atmospheric and Solar-Terrestrial Physics, 2009 (doi:10.1016/j.jastp.2009.07.007)
21 Massen, F., Beck, E. :Accurate estimation of CO2 background level from near ground measurements at non-mixed environments
in: Leal, W., editor: The Economic, Social and Political Elements of Climate Change
Climate Change Management, 2011, Part 4, 509-522. Springer. DOI: 10.1007/978-3-642-14776-0_31
22 De Backer & Van Malderen: Time series of daily erythemal UVB doses at Uccle Belgium. Poster, July 2009.
23 Massen, F.: Sunshine duration from pyranometer readings, 2011
24 Massen F.,  Calculating moist enthalpy from usual meteorological measurements (July 2010) and Calculating moist enthalpy revisited (Sep. 2010)
25 CDIAC: Online Trends
26 UAH MSU data: http://vortex.nsstc.uah.edu/public/msu/
27 Vigouroux et al:  Evaluation of ozone trends from g-b FTIR observations. Atmos. Chem. Phys., 8, 68656886, 2008
28 UNEP, Scientific Assessment: Stratospheric Ozone and Surface Ultraviolet Radiation
http://ozone.unep.org/Assessment_Panels/SAP/Scientific_Assessment_2010/04-Chapter_2.pdf
29 http://berkeleyearth.lbl.gov/regions/luxembourg
30 K. Makowski: The daily temperature amplitude and surface solar radiation. Dissertation ETH Zrich #18319, 2009
31 http://de.wikipedia.org/wiki/Zeitreihe_der_Lufttemperatur_in_Deutschland#Winter
32 Tim Osborne: NAO data (http://www.cru.uea.ac.uk/~timo/datapages/naoi.htm)
33 Helioclim satellite measurements of solar irradiation at http://www.soda-is.com/eng/services/services_radiation_free_eng.php
34 http://www.esrl.noaa.gov/gmd/ccgg/trends/index.html
35 http://www.atmos.ucla.edu/~qli/publications/Jiang_2011_GBC.pdf
36 Fung: A Hyperventilationg Biosphere (Sep. 2013)
37

Fraunhofer Institut Stromproduktion : http://www.ise.fraunhofer.de/de/downloads/pdf-files/aktuelles/stromproduktion-aus-solar-und-windenergie-2013.pdf

38 EEA interacive map. http://www.eea.europa.eu/themes/air/interactive/no2
39 Portail de l'Evironnement: http://www.environnement.public.lu/air_bruit/dossiers/PA-reseaux_mesure_air/reseau_automatique/resultats_mesures_live/index.html
40 Wetterkontor: http://www.wetterkontor.de/de/monatswerte-station.asp
41 Amplitude of the atmosphere's seasonal CO2 cycle . CO2science
42 Fung et al. : The changing carbon cycle at Mauna Loa observatory
43 Zeng et al.: Agricultural green revolution as a driver of increasing atmospheric CO2 seasonal amplitude (2014)
44 Zeng: The changing CO2 seasonal cycle (presentation, 2014)
45 Sceptical Science Trend Calculator : https://www.skepticalscience.com/trend.php
46 Kmpfe, Kowatsch: Winter 2014/15 in Deutschland: Erneut zu mikd - warum ?
47 NOAA Climate Prediction Center:  http://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/nao.shtml
48 Hohenpeissenberg Klimagase: http://www.dwd.de/DE/forschung/atmosphaerenbeob/zusammensetzung_atmosphaere/spurengase/inh_nav/klimagase_node.html
49 meteo.be: solar irradiance Uccle: ( this link is defunct: http://www.meteo.be/meteo/view/fr/23023844-2015.html)
50 Francis Massen: Ozone, change is the norm! https://meteolcd.wordpress.com/2015/10/26/the-total-ozone-column-change-is-the-norm/
51 El Nino's and La Nina's years and intensities: http://ggweather.com/enso/oni.htm
52 Solar cycle progression : http://services.swpc.noaa.gov/images/solar-cycle-sunspot-number.gif
53 Meteo data from all DE and NL stations: http://wetterzentrale.de (click on Archiv)
54 Massen, F. : First Radiation Amplification factor for 2016
55 Massen.F, Zimmer M.: Comparing the year 2016 Total Ozone Column measurements at Uccle and Diekirch (pdf)
56 MASSEN F, ZIMMER M., THOLL R., HARPES N.: Comparing the year 2017 Total Ozone Column measurements at Uccle and Diekirch (pdf)
57 WDCGG (World Data Center for Greenhouse Gases): http://gaw.kischou.go.jp  (registration mandatory to access data)
58 https://www.energy-charts.de/energy_de.htm
59 Francis Massen: One year of fine particle measurements by Airvisual Pro at meteoLCD (at blog meteolcd.wordpress.com, published 23-Jan-2020)
60 Discomap data retrieval for EEA data: https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm
61 WMO 2021 global air quality guidelines
62 http://vortex.nsstc.uah.edu/data/msu/v6.0/tlt/uahncdc_lt_6.0.txt

          

 


Addendum 1
2014 update!
Lindzen & Choi [19] define the non-feedback climate sensitivity as ΔT0 = G0*ΔF, where G0 = 0.25 Wm-2 and ΔF is the change in radiative forcing. A change in solar irradiance of -0.82 kWh*m-2y-1  (decade 2005 to 2014) corresponds to ΔF = - 820/8760 = -0.09 Wm-2 and should yield a cooling of  ΔT0 = -0.25*0.09 = -0.02 K (or C).per year. The meteoLCD measurements give a cooling of 0.0057 Ky-1, about 3 times less.

Scafetta [20] defines a climate sensitivity in respect to changes in solar radiation by k1s = ΔT/ΔF and finds k1s = 0.053. Our data for the decade 2005 to 2014 give ΔT/ΔF= - 0.0057/(-0.09) = 0.06, a value close to that of Scafetta!.

Summary for the 2005 to 2014 decade:
  <\table>
meteoLCD (observations) ΔT/ΔF = 0.06 K/(Wm-2y-1) ΔT       = - 0.0057 Ky-1
ΔF       = - 0.09 Wm-2y-1
ΔT/ΔF =   0.06
Lindzen & Choi G0 = 0.25 ΔT = - 0.0200 Ky-1 (computed)
Scafetta k1s  = 0.053 ΔT = -0.0050 Ky-1 (computed)


 

<\table>
Addendum 2
2018 update!
 

 

It makes for an interesting exercise to compare the influence of mean yearly solar forcing on moist enthalpy and air temperature for the 17 years period 2002 to 2018.
 

Both air temperature and moist enthalpy are positively correlated to changes in solar forcing ( = mean solar irradiance). The Pearson correlation between mean solar irradiance and moist enthalpy is 0.73 and is significant at the p = 0.05 level, whereas the correlation between mean solar irradiance and temperature is 0.42 (not significant).

A change of 1 Wm-2 of mean solar irradiance would cause a (big!) average heating of 0.5 C per decade and a change of 0.9 kJ/kg of moist enthalpy per decade.

Possibly taking into account some lag (as for instance 4 months for temperature lagging solar forcing) would change these numbers.

Our temperature measurements give a heating of 0.7C/decade for the same period (Findel shows 0.5/decade), which is close to the correlation given if the solar irradiance was the unique warming influence!

 

 

<\table>
Addendum 3
 
A short analysis of the seasonal CO2 pattern in 2014.

The mean monthly CO2 data show an oscillatory pattern which can be modeled by a 6 month period sine wave. This is not consistent with the commonly admitted explication that the summer lows and winter highs are a fingerprint of changing photosynthesis, which should lead to a single annual sinus wave (as in 2013).

The 6 month period is essentially caused by the low Jan, Feb and Dec values, and is replaced by the usual 12 period if these months are omitted.

The right figure shows the monthly mean CO2 and monthly mean wind speeds. Clearly low wind goes with high CO2, independent of the seasons (significant correlation R = -0.86 !)

The next figure gives the CO2 mixing ratios versus the monthly mean wind speed; the usual exponential model beautifully describes this pattern. The horizontal asymptote of 395.5 ppmV should correspond to the background CO2 level, as shown in [21].

There is some debate about the (global) changes of the seasonal CO2 amplitude, which seems to increase due to global greening [41], agricultural green revolution [43], changing air trans-continental circulation [42] and possibly other unknown factors. Look also at the presentation [44].

Locally it seems that the effects of higher/lower wind speeds and photosynthesis are difficult to untangle. If we restrict our data to those days where the mean wind speed is less than 1, the correlation between CO2 and wind speed is lower (-0.76) but still significant.

Curiously all the papers studying this seasonal amplitude problem seem to ignore the influence of changing wind speeds.

 

 

The same analysis for 2015

Here again higher wind speeds usually go together with lower CO2 levels (notice the exception on March!), but the monthly mean values do not follow the usual model well.

If we take all 17520 individual measurements, the picture becomes clearer, and we find that our "bumerang" model follows reasonably well the overall pattern. The horizontal asymptote suggest a background CO2 level of about 389 ppmV, which seems a bit low.

  

 

The same analysis for 2016 (wind speed from cup anemometer)

The high wind speeds lower the January , February (and December) values which normally should be higher; so the "usual" sinus pattern with a trough during the summer months is mostly absent.

 

Using all CO2 measurements of the year, we find again our boomerang pattern; the usual model has a better R2 than in 2015, but the asymptotic value of 383 is definitively too low!

 

CO2 versus wind speed for 2017 (wind speed by cup anemometer):

The Mauna Loa average CO2 mixing ratio for 2017 is 406.6, which would suggest that our asymptotic value of 392.3 is too low. If we use only the measurements by the new Vaisala GMP343 sensor, the asymptotic value becomes 395.7.

 

CO2 versus wind speed for 2018 (wind speed by cup anemometer, 17520 data points):

The Mauna Loa average CO2 mixing ratio for 2018 is 408.5, so our asymptotic value of 406 is quite close. The R2 of the model (the goodness of the fit) is also quite acceptable: R2 = 0.50. All parameters are significant at the 5% level (alpha = 0.95).

 

CO2 versus wind speed for 2019 (wind speed by cup anemometer, 17520 data points):

The Mauna Loa average CO2 mixing ratio for 2019 is 411.44, so our asymptotic value of 411 is practically the same! The R2 of the model (the goodness of the fit) is also quite acceptable: R2 = 0.52. All parameters are significant at the 5% level (alpha = 0.95).

 

CO2 versus wind speed for 2020 (wind speed by cup anemometer, 17568 data points):

The Mauna Loa average CO2 mixing ratio for 2020 is 414, so our asymptotic value of 417 is very close, keeping in mind that CO2 levels increase slightly with latitude!
The R2 of the model (the goodness of the fit) is also quite acceptable: R2 = 0.56. All parameters are significant at the 5% level (alpha = 0.95).

CO2 versus wind speed for 2021 (wind speed by cup anemometer, 17568 data points):

The Mauna Loa average CO2 mixing ratio for 2021 is 416.5, so our asymptotic value of 422.6 is close, keeping in mind that CO2 levels increase slightly with latitude!
(possibly ~2 ppm should be added to the MLO readings for the difference in latitudes from 20 to 50N).

The December 2020 CO2 readings at the Hohenpeissenberg station near Munich were 420.9 ppmV
The R2 of the model (the goodness of the fit) is also quite acceptable: R2 = 0.49. All parameters are significant at the 5% level (alpha = 0.95).

 

CO2 versus wind speed for 2022 (wind speed by cup anemometer, 17560 data points):

The Mauna Loa average CO2 mixing ratio for 2022 is 418.6, so our asymptotic value of 427.3 is close, keeping in mind that CO2 levels increase slightly with latitude!
(possibly ~2 ppm should be added to the MLO readings for the difference in latitudes from 20 to 50N).

The R2 of the model (the goodness of the fit) is also quite acceptable: R2 = 0.50. All parameters are significant at the 5% level (alpha = 0.95).

 

 

 

 

 

 

 


file: meteolcd_trends.html

francis.massen@education.lu
 

History:

12 Jan 2023: Start update of 2021 trends page to 2022

29 Mar 2023: Trends analysis for 2022 finished