Data trends at meteoLCD: 1998 to 2017 

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 2017.

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.


Most important conclusions from 1998 (2002, 2004) to 2017 linear trends:

1. Some minor solar dimming since 2004, sunshine duration decreases by 90 hours*decade-1
2. Local temperatures show slight warming of 0.04C/y since 2002
3. Diurnal temperature range (DTR) trend since 2004 is positive (= no anthropogenic warming fingerprint ).
4. The winter trend since 2002 shows a warming of +0.6 C*decade-1 ; the trend is practically equal to that of the winter NAO index
5. Since 2002 the ground O3 trend is marginal positive,
 the total thickness of the ozone layer slightly decreases by 7 DU*decade-1
6. Local CO2 mixing ratio increases by approx. +3.1 ppmV*year-1  from 2013 to 2016
7. The trend of the biologically effective yearly UVB dose is flat from 2002 to 2017
8. The UVA dose is slightly decreasing since 2004
9. Precipitation (rainfall) shows a sinusoidal pattern of 62 month period. From 2002 to 2017 the trend is flat
10. Energy content of moist air (enthalpy) shows a flat trend
NO/NOx measurements have 22% missing hours due to equipment failure; they are definitively stopped at the end of 2017.

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

Mean and stdev of the year 2016: 47.1 +/- 33.9   

Mean +/- stdev:
1998 to 2016: 39.9 +/- 6.7 ug/m3
2002 to 2016: 39.0 +/- 7.2

Trends 1998 - 2017:  practically flat
             2002 - 2017: +0.21 ug/m3 per year (nearly flat trend!)

Please note the succession of 2 different instruments; after the definitive breakdown of the Teledyne API400, the old O341M sensor from Environnement SA was used again.


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

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

Mean and stdev of the year 2017: 313.1 +/- 40.6
minimum : 224.5 (14 Jan)
maximum: 441.7 (06 Feb)

2017: Uccle (Brewer 178, DS only): 331.3 +/- 37.9

Trendlines (start year is x = 1):
1998 to 2017:                       
  (+7 DU/decade)
2002 to 2017:                          (- 7 DU/decade )

 gives a flat trend  for the 2002-2016 period) (see also [16])

Calibration multiplier to apply to the Diekirch DU data [55] and [56]
if Uccle Brewer 178 is the reference: 1.033 (R2 =0.76)

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 Teledine E600) has been replaced by a Vaisala GMP343 sensor the 27 Jun 2017. A new zero offset should be considered possible!

Mean and stdev of the year 2017: 416.3 +/- 31.6

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

Trendlines :
2002 to 2012:
400.3 + 1.35*x      (2002: x=1)
2013 to 2016: 398.5 + 3.11*x      (2013: x=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 5 years, and gives the readings of Diekirch (DIK), Mauna Loa (MLO) and Hohenpeissenberg (HPB, only from 2013 to 2016). 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 + 3.11  ppmV/year  
Hohenpeissenberg (HPG) + 2.00 ppmV/year [48]
Manua Loa (MLO) + 2.57 ppmV/year [34]




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 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: the summer low is quite proeminent, but the seasonal swing is much less sinusoidal.


See the end of addendum 3 for a picture of CO2 versus windspeed.



Air temperature [C]

Mean and stdev of the year 2017 (from monthly averages):
Diekirch: 11.60 +/- 6.67 
Findel:     10.23 +/- 6.89   

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

1998 to 2016 :  10.43 +/- 0.55 C                
2002 to 2016 :  10.50 +/- 0.58  C
2008 to 2016 :  10.52 +/- 0.71 C (last decade)

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 2017 (2016 was a very strong El Nino year,
so linear trends are almost meaningless!):
meteoLCD:   +0.041 C/year
Findel:           +0.037 C/year           

Latest Global temperature anomaly trends for same period 2002-2017:
UAH (satellite)   : + 0.010C/year   [45]
RSS (satellite)   : + 0.014C/year
CRU (Hadcrut4): + 0.017C/year

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 2017 (from monthly averages):
Diekirch: 8.87 +/- 2.89
Findel:     7.83 +/- 2.48

Mean DTR at Diekirch:
1998 to 2017:
2002 to 2016:  8.65
2007 to 2016:  8.61  (last decade)

For 2002 to 2017: all trends are nearly flat (DTR would diminish by 0.8C/century, which is insignificant)

A fingerprint of climate warming is that daily minima increase more rapidly than daily maxima so that the DTR trend should become negative. If we start our analysis after the exceptional heat-wave year 2003, DTR trends at  Diekirch and at the Findel airport are positive for the 14 year long period 2004-2017, as shown by the second figure! So this fingerprint does not exist here.

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! (graph here).

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


Winter temperatures [C]

Values of DJF temperature of the year 2017:
Diekirch: 2.91
Findel:     3.10
NOA:        0.65
 NAO normalized index [47]

The trends show warming winters since 2002 to 2017, with the warming probably caused by the NAO. The trend for Diekirch is practically equal to that of NAO index.

Trends  from 2002 to 2017 (2016 was a very strong El Nino year!):
Diekirch:   +0.060 C/year since 2002
Findel:       +0.089
Germany:  +0.072   [46]
NAO:         +0. 059

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

The North Atlantic Oscillation clearly influences our winters (but note the exception for the strong 2016 El Nino year! [51]); the correlations between the Diekirch DJF temperature series and the NAO_DJF normalized index is statistically significant (=0.56) at the 5% level.

Enthalpy of moist air in kJ/kg

Mean moist enthalpy of 2017: 31.09 +/- 13.62 kJ/kg

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 2017:  28.17 +/- 1.39 kJ/kg

Trend is marginally positive from 2002 to 2017; the last 2 years show the influence of the 2016 "monster" El Nino, which extended into 2017 .


Total Yearly Rainfall [mm]

Values of rainfall (precipitation) of the year 2017:
Diekirch: 779.2 mm
Findel:     722.5 mm

1998 to 2017 mean +/- stdev: 695.5 +/- 136.5 mm 
2002 to 2017 mean +/-stdev:  651.6 +/_105.8 mm

The negative trend from 1998 to 2017 seems spectacular: -91 mm/decade, caused by the very high values of 2000 and 2001.
The 2002-2017 period has a practically flat trend.





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

 A good model for the Diekirch data is a sinus function: the calculation suggest a 5.87 years period (~70 months, R2 = 0.26; in the model x = 0  corresponds to 1998), with a mean value of 707 mm and an amplitude of 94 mm; the phase shift of -1.79 rad is close to 1/3 period.  
gives short term periods of 6 to 7 years for the Western European region.








The third plot shows the modeling result if we restrict the data to the 2002-2017 range: the sinus model is exceptionally good with an R2 of 0.60. Note that all numerical values (here amplitude and level are rounded) are the result of the estimation calculus (Levenberg-Marquart algorithm). The calculated period is 5.16 years (~62 months).

The rainfall pattern is a good example how foolish it is to apply linear regressions to periodic data, something the media and many politicians delight in..

Solar energy on a horizontal plane

Values of total solar energy of the year 2016:
Diekirch: 1127.7 KWh/m2
Uccle:      1111.7    [49]

1998 to 2016 mean +/- stdev: 1108.1 +/-  44.0 kWh/m2
2002 to 2016 mean +/- stdev: 1106.2 +/- 47.5 kWh/m2

Visible negative trend: 1998 to 2016: -24.6 kWhm-2/decade
(not surprising with exceptional 2003 peak!).
Starting at 2004 after the heat-wave year the trend for solar energy is still negative, albeit by a very small amount (0.5 kwh per year)

(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 2017:
Diekirch:     1662 hours (215m asl) (from pyranometer)
Findel:         1858 hours (365m asl, (from Campbell- Stokes)
Trier:            1705 hours (279m asl)
[40] [53]
Maastricht: 1688 hours (114m asl) [53]

Negative trends:
1998 to 2017:  - 40 hours/decade
2004 to 2017:  - 90 hours/decade



The decline from 2012 to 2013 is -10.8%, to be compared to the data from the Fraunhofer Institut which gives a decline of -10.6 % of the German PV "Volllasttunden" [37]. The decline is potentially bad news for the solar PV installations; see a graph on the evolution of the German PV capacity factors here.

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


This 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).

Biologically eff. UVB dose on a horizontal plane in kWh/m2

Erythemal UVB dose of the year 2017: 0.142 KWh/m2

mean +/- stdev:
1998 to 2017: 0.131 +/- 0.009   eff. kWh*m-2y-1
2002 to 2017: 0.132 +/- 0.007
2004 to 2017: 0.131 +/- 0.006

The trend over 2002 - 2017 is absolutely flat! The 2004 - 2017 trend-line (not on the graph) shows a small increase of 0.0006 kWh*m-2y-1 (contrary to the trend-lines of solar energy 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 

UVA dose of the year 2017: 56.0 KWh/m2
(some intermittent problems with internal temperature stabilization of the sensor)

mean +/- stdev:
1998 to 2017  54.1 +/- 4.5 kWh*m-2*y-1
2002 to 2017: 54.8 +/- 3.9
2004 to 2017: 54.3 +/- 3.8

Trends from 2002 - 2017 and 2004 - 2017 slightly negative.

The 2 independent measures of solar energy and UVA doses all point to a slight solar dimming since 2004.

NOx, NO and NO2 concentration in ug/m3

(End of measurements useable for trends in 2013. No trends will be calculated for this year!)

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):


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.


1 Europe's Environment 4th AR (2007) Fig. 2.2.3
2 EPA: Ozone trends.

Jonson et al: Can we explain the trends in European ozone levels? Atmos. Chem. Phys. Discuss., 5, 59575985, 2005.

4 Ozone trends at Uccle
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 (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: (temporarly unavailable)
11 EEA: Emission trends of NOx 1990 - 2005
12 L. Motl: . 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:
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
30 K. Makowski: The daily temperature amplitude and surface solar radiation. Dissertation ETH Zrich #18319, 2009
32 Tim Osborne: NAO data (
33 Helioclim satellite measurements of solar irradiation at
36 Fung: A Hyperventilationg Biosphere (Sep. 2013)

Fraunhofer Institut Stromproduktion :

38 EEA interacive map.
39 Portail de l'Evironnement:
40 Wetterkontor:
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 :
46 Kmpfe, Kowatsch: Winter 2014/15 in Deutschland: Erneut zu mikd - warum ?
47 NOAA Climate Prediction Center:
48 Hohenpeissenberg Klimagase:
49 solar irradiance Uccle:
50 Francis Massen: Ozone, change is the norm!
51 El Nino's and La Nina's years and intensities:
52 Solar cycle progression :
53 Meteo data from all DE and NL stations: (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)



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:
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)


Addendum 2
2014 update!
It makes for an interesting exercise to compare the influence of mean yearly solar forcing on moist enthalpy and air temperature for the decade 2005 to 2014.

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 0.11 and is not significant at the p = 0.05 level, whereas the correlation between mean solar irradiance and temperature is 0.69 and significant.

A change of 1 Wm-2 of mean solar irradiance would cause a (big!) average heating of 1.2 C per decade and a change of 0.3 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.


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.






file: meteolcd_trends.html


02 Jan 2018: Start of update to include 2016 data.
11 Jan 2018: Update to 2017 data finished. Uccle dataset at WOUDC for TOC still uncomplete.
02 Apr 2018: Update to include total ozone column intercomparison with Uccle.