Data trends at meteoLCD: 1998 to 2019
Trends computed from yearly averages at meteoLCD,
Graphs may be freely copied and used, under the condition to cite:
MASSEN, Francis: Data trends at meteoLCD, 1998 to 2018. http://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.
An Addendum 4 has been added to report on the PM (fine particle) measurements (04-Apr-20)
Most important conclusions from 1998 (2002, 2004) to 2019 linear trends:
1. Some small decrease in sunshine duration since 2004 by 21
(but total irradiance increases!)
2. Local temperatures show warming of 0.08°C/y since 2002 (2018 was a strong El Nino year)
3. Diurnal temperature range (DTR) trend since 1998 is positive (= no anthropogenic warming fingerprint ).
4. The winter trend since 2002 shows a warming of +0.9 °C*decade-1 ; the trend is close to that of the winter NAO index (+0.6 °C*decade-1)
5. Since 1998 the ground O3 trend is positive, from 2002 to 2019 the total thickness of the ozone layer slightly decreases by 8 DU*decade-1
6. Local CO2 mixing ratio continue to increase, but the huge increase in 2018 could be due to a sensor bias
7. The trend of the biologically effective yearly UVB dose is slightly positive from 2002 to 2019
8. The UVA dose is slightly positive from 2002 to 2019 (as is the trend in total solar irradiance)
9. Precipitation (rainfall) shows a sinusoidal pattern of close to 5 years period.
10. Energy content of moist air (enthalpy) shows a moderate positive trend
NO/NOx measurements have been definitively stopped at the end of 2017.
+/- stdev (from yearly means)
Ozone Column [DU]
Mean and stdev of the year 2019: 313.0 +/- 39.0 DU
minimum : 222.3 (16 Fep)
maximum: 451.3 (11 Mar)
181 common day
direct sun readings for 2019 at Uccle and Diekirch:
Trendlines (start year is x =
multiplier to apply to the Diekirch DU data
mixing ratio in ppmV
Attention: The instrument for measuring CO2 (API Teledine E600) has been replaced by a Vaisala GMP343 sensor the 27 Jun 2017.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 2019: 434.4 +/- 31.7 ppmV
The 1998-2001 data are too unreliable to be retained
for the trend analysis.
The second picture zooms on the last 6 years, and
gives the readings of Diekirch (DIK), Mauna Loa (MLO) and Hohenpeissenberg (HPB)
from 2014 to 2019; 2019 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.
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 un-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 and 2015. 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 the yearly amplitude of the sinus fit is 8.48 ppmV (a total swing of ~17 ppmV, to be compared to about 15 ppmV at the HPB station ).
See the end of addendum 3 for a picture of CO2 versus windspeed.
Mean and stdev of the year 2019
(from monthly averages):
1998 to 2019 :
10.59 +/- 0.73 °C
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.
Range (DTR) [°C]
DTR = daily max - daily min temperature
Mean DTR at Diekirch:
For 1998 to 2018: all trends are positive, the 24hmin
trend is lower than the 24hmax trend.
Winter temperatures [°C]
Values of winter DJF temperature of the year 2019:
NOA: 0.65 NAO normalized index 
The trends show warming winters since 2002 to 2018, with the warming probably caused by the NAO.
Trends from 2002 to 2018:
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
Enthalpy of moist air in kJ/kg
Mean moist enthalpy of 2019: 31.98 +/- 15.34 kJ/kg
See  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
Yearly Rainfall [mm]
Values of rainfall (precipitation) of the year 2019:
Diekirch: 687.6 mm
Findel: 779.2 mm
1998 - 2019 mean +/- stdev: 693.9 +/- 129.9 mm
The negative trend from 1998 to 2018 seems spectacular:
-82 mm/decade, caused by the very high values of 2000 and 2001.
Clearly precipitation shows an oscillation pattern, so linear trends should be taken with precaution (or simply seen as non-sensical).
A good model for the Diekirch data is a
sinus function: the calculation
suggests for the interval 2002 - 2019 a 5.14 years period (~62 months, R2 =
in the model x = 0 corresponds to 2002), with a mean value of 649 mm and an
amplitude of 95 mm; the phase shift of 1.04 rad is close to 1/6 period. All
these values are close to those of the preceding year.
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.
energy on a horizontal plane
Values of total solar energy
of the year 2019:
1998 to 2019 mean +/- stdev: 1115.7 +/- 48.8 kWh/m2
(meteoLCD values derived from pyranometer data by Olivieri's method)
Values of sunshine hours
of the year 2019:
Very small negative trends:
 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 (in July and August the excess of Findel readings was highest).
All 4 stations give totals that practically always vary in the same manner (synchronous increase and decrease). But notice how the readings in 2018 between the 2 groups (Diekirch, Maastricht) and (Findel, Trier) are quite different (blue arrow)!
eff. UVB dose on a horizontal plane in kWh/m2
Erythemal UVB dose of the year 2019: 0.141 kWh/m2
mean +/- stdev:
1999 to 2019: 0.133 +/- 0.008 eff. kWh*m-2y-1
2002 to 2019: 0.134 +/- 0.007
2010 to 2019: 0.134 +/- 0.008 (last decade)
The trend over
2002 - 2019 is slightly positive, the trend line from 1998 to 2019 should be
taken with a grain of salt, as the 1998 readings seem abnormally low.
dose on a horizontal plane in kWh/m2
UVA dose of the year 2019: 60.2 KWh/m2
(some intermittent problems with internal temperature stabilization of the sensor; the influence seems minimal, so all readings have been kept)
mean +/- stdev:
The 2 independent measures of UVB and UVA doses all
point to a very slight increase since 2002, conforming to the increase of
total solar irradiance
NO and NO2
concentration in ug/m3
(End of measurements useable for trends in 2013. Measurements stopped in 2017).
78% of possible measurements available due to sensor downtime!
see  which gives ~30% reduction from 1990 to 2005 for the EU-15 countries.
|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|
Jonson et al: Can we explain the trends in European ozone levels? Atmos. Chem. Phys. Discuss., 5, 5957–5985, 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)|
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, 6865–6886, 2008|
UNEP, Scientific Assessment: Stratospheric Ozone and
Surface Ultraviolet Radiation
|30||K. Makowski: The daily temperature amplitude and surface solar radiation. Dissertation ETH Zürich #18319, 2009|
|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|
|36||Fung: A Hyperventilationg Biosphere (Sep. 2013)|
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|
|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||Kämpfe, 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)|
|59||Francis Massen: One year of fine particle measurements by Airvisual Pro at meteoLCD (at blog meteolcd.wordpress.com, published 23-Jan-2020)|
|Lindzen & Choi  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  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:
|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 0.7°C/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!
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 .
There is some debate about the (global) changes of the seasonal CO2 amplitude, which seems to increase due to global greening , agricultural green revolution , changing air trans-continental circulation  and possibly other unknown factors. Look also at the presentation .
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.
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.
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!
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.
versus wind speed for 2018 (wind speed by cup anemometer, 17520 data
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).
versus wind speed for 2019 (wind speed by cup anemometer, 17520 data
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).
Fine particle measurements at meteoLCD
An Airvisual Pro sensor from iQAir has been operational for the full year 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; a private dashboard holds all data). 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 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
Read the article "One year of fine particle measurements by Airvisual Pro at meteoLCD" on the blog meteolcd.wordpress.com [ref. 59]
The next two plot show the PM2.5 readings per day, together with the corresponding values of the official measuring station at Beidweiler, which uses a Horiba sensor.
The correspondence shown on the first plot is excellent; the second plot suggest to divide the Diekirch data by 0.9572, if Beidweiler is considered as the reference.
The PM10 readings of the Airvisual Pro are very close (too close!) to it's PM2.5, and as such considerably too low if compared to the Beidweiler readings; we have no explanation for this for the moment.
09 Mar 2020: Update to include 2019 data finished; not all addendum's updated to 2019
04 Apr 2020: Added Addendum 4 with the fine particle
measurements of 2019