Data trends at meteoLCD: 1998 to 2016 (update finished)

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 2016.

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 2016 linear trends:

1. Noticeable solar dimming since 2004, sunshine duration decreases by -126 hours*decade-1
2. Local temperatures show slight warming of 0.017C/y since 2002
3. DTR trends since 2002 and for last decade remain flat
4. The winter trend since 2002 shows a warming of +0.7 C*decade-1
5. Since 2002 ground O3 trend is flat,
 thickness of ozone layer slightly decreases by 5 DU*decade-1
6. Local CO2 mixing ratio increases by approx. +2 ppmV*year-1  from 2013 to 2016
7. The biologically effective UVB dose is very slightly increasing since 2004 (and since 1998)
8. The UVA dose is slightly decreasing since 2004
9. Precipitation (rainfall) shows a sinusoidal pattern; over the last 2 full periods 2005-2016 the trend is +5.2 mm/y
10. Energy content of moist air (enthalpy) declines by -0.7 kJ*kg-1*decade-1
NO/NOx measurements are discontinued in 2014 and 2015; reintroduced in 2016 (30% missing hours).

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

Mean and stdev of the year 2016: 39.9 +/- 31.2   

Mean +/- stdev:
1998 to 2016: 39.5 +/- 6.6 ug/m3
2002 to 2016: 38.5 +/- 7.2

Trends 1998 - 2015:  -0.24 per year
             2002 - 2015: +0.03 per year (flat trend!)

Watch the left scale to note that trends are very small!


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

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

Mean and stdev of the year 2016: 317.9 +/- 45.4
minimum : 219.0 (12 Nov)
maximum: 422.0 (24 Apr)

Uccle (Brewer 16&178, DS only): 331.1 +/- 41.4

Trendlines (start year is x = 1):
1998 to 2016:
314.0 + 0.1*x  (+1 DU/decade)
2002 to 2016: 332.2 - 0.5*x   (- 5 DU/decade )

 gives +0.95DU*y-1  for the 1998-2010 period) (see also [16])

Calibration multiplier to apply to the Diekirch DU data [55]:
if Uccle Brewer 16 is the reference: 0.96-1 = 1.04 (R2 =0.802)
if Uccle Brewers 16&178
are the reference: 0.97-1
= 1.03 (R2=0.795)

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 

Mean and stdev of the year 2015: 407.6 +/- 4.9

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

Trendlines :
2002 to 2012:
400.31 + 1.36*x    (2002: x=1)
2013 to 2016: 400.75 + 2.00*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 4 years, and gives the readings of Diekirch, Mauna Loa (MLO) and Hohenpeissenberg (HPB, only from 2013 to 2015, 2016 data not yet available). Note the very different elevations! Mauna Loa has no vegetation at all, Diekirch and HPB similar grass and forests.

The yearly trends are:

Diekirch + 2.00  ppmV/year  
Hohenpeissenberg + 1.48 ppmV/year [48]
Manua Loa + 2.53 ppmV/year [34]





The CO2 data (monthly averages) often show the summer-time lows and winter highs, 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, as the seasonal swing is much less sinusoidal, and only a sharp minimum in August can be seen.








Air temperature [C]

Mean and stdev of the year 2016 (from monthly averages):
Diekirch: 11.15 +/- 6.81 
(11.16 from all half-hour readings)
Findel:     10.83 +/- 6.90   

Mean temperatures (+/- stdev):

1998 to 2016 :  10.37 +/- 0.48 C                
2002 to 2016 :  10.43 +/- 0.51  C
2007 to 2016 :  10.44 +/- 0.66 C (last decade)

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

Trends  from 2002 to 2016 (2016 was a very strong El Nino year!):
meteoLCD:   +0.017 C/year
Findel:           +0.032 C/year           

Latest Global temperature anomaly trends for same period:
UAH (satellite)   : + 0.009C/year   [45]
RSS (satellite)   : + 0.005C/year
CRU (Hadcrut4): + 0.009C/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 2016 (from monthly averages):
Diekirch: 8.31 +/- 2.55 
(8.79 from all half-hour readings)
Findel:     7.58 +/- 2.22

Mean DTR ( +/- stdev):
1998 to 2016:
 8.52 +/- 0.54
2002 to 2016:  8.63 +/- 0.55
2007 to 2016:  8.58 +/- 0.47 (last decade; trend is slightly positive: +0.003 C/y)

For 2002 to 2016: all trends are practically flat (DTR would diminish by 0.17C/century, which is insignificant)
Findel DTR trend is  flat from 2004 to 2015 (+0.001 C/year)

A fingerprint of climate warming is that daily minima increase more rapidly than daily maxima so that the DTR trend should become negative. Clearly the data for the last decade do not show this neither at Diekirch nor at the Findel.

The BEST data set for Luxembourg [29] stops at 2013.

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

Winter temperatures [C]

Values of DJF temperature of the year 2016:
Diekirch: 3.66
Findel:     4.13

Contrary to what is often suggested in the media, winters were cooling since 2002 to 2013. The cooling trend (about -0.5 C/decade) has now reversed into a slight warming.

Trends  from 2002 to 2016 (2016 was a very strong El Nino year!):
Diekirch:   +0.068 C/year since 2002
Findel:       +0.074
Germany:  +0.093   [46]
NAO:         +0.028  NAO normalized index/year

The plot shows the mean temperatures of December (from previous year), January and February. It also shows in brown the NAO index for the months Dec to Feb [47]

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 Findel, DE and NAO normalized index are 0.94, 0.93 and 0.50, all except the last (NAO) significant at the 5% level. The NAO index
for 2016 does not include the December data, so the 0.5 correlation  values might slightly change.

Enthalpy of moist air in kJ/kg

Mean moist enthalpy of 2016: 30.72 +/- 14.41 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 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:
2002 to 2016  27.98 +/- 1.20 kJ/kg

Trend is slightly negative from 2002 to 2016 (which would point to an ongoing cooling trend), but note the spectacular rise for the strong El Nino year 2016.


Total Yearly Rainfall [mm]

Values of rainfall (precipitation) of the year 2016:
Diekirch: 602.4
Findel:     864.6

1998 to 2016 mean +/- stdev: 695.5 +/- 136.5 mm 
2002 to 2016 mean +/-stdev:  651.6 +/_105.8 mm

The negative trend from 1998 to 2015 seems spectacular: -120 mm/decade, which comes from the very high values of 2000 and 2001.
The 2002-2016 period has a much lower trend of -36.4 mm/decade





Clearly precipitation shows an oscillation pattern, so linear trends should be taken with precaution. A good model for the Diekirch data is a sinus function of 5.35 years period (~64 months, R2 = 0.62; see lower graph). If we take the period 2005 - 2016 corresponding to 2 full periods, the linear trend becomes positive with +5.2 mm/y !

The same model  suggests a similar period of 5.73 years for the Findel precipitation pattern (R2 = 0.52)

gives short term periods of 6 to 7 years for the Western European region.












The third plot shows both the Findel and Diekirch annual precipitation, with the averages as dotted lines. Clearly the Findel readings are always higher, which seems normal considering the exposed situation at Findel airport versus Diekirch situated at the bottom of a valley (the elevation  at Findel is also  higher by 156 m ).

Solar energy on a horizontal plane

Values of total solar energy of the year 2016:
Diekirch: 1052.1 KWh/m2
Uccle:      1111.7    [49]

1998 to 2016 mean +/- stdev: 1107.1 +/-  44.9 kWh/m2
2002 to 2016 mean +/- stdev: 1104.8 +/- 48.8 kWh/m2

Visible negative trend: 1998 to 2016: -24.6 kWhm-2/decade
(not surprising with exceptional 2003 peak!)
Large drop in 2016 causes clear negative trend of -18 kWh/m-2 per decade for the period 2004 to 2016 (the very weak actual solar cycle #24 started in Jan. 2009, reached its last peak at the start of 2014 and was close to the minimum at the end of 2016). [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 2016:
Diekirch:     1524 hours (215m asl)
Findel:         1657 hours (365m asl, Campbell- Stokes)
Trier:            1448 hours (279m asl)
[40] [53]
Maastricht: 1715 hours (114m asl) [53]

Negative trends:
1998 to 2016: -   50 hours/decade
2004 to 2016: - 126 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]. For a graph on the evolution of the German PV capacity factors click 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 2016: 0.132 KWh/m2

mean +/- stdev:
1998 to 2016: 0.130 +/- 0.008 eff. kWh*m-2y-1
2004 to 2016: 0.130 +/- 0.005

Trends over 1998-2016 and 2004-2016 are the same, are practically flat!

From 2002 to 2016, the trend is slightly negative (-0.0002 eff. kWh*m-2y-1); this is compatible with the small decrease in TOC (total ozone column). Several comments [54] have shown that the RAF (radiation amplification factor) at Diekirch is about 1.0. If we apply the RAF formula to the start and end-points of the 2002-2016 trendlines of UVBdose and TOC we get 1.006 = ~1

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 2016: 52.7 KWh/m2
(some problems with internal temperature stabilization of the sensor)

mean +/- stdev:
1998 to 2016  54.0 +/- 4.6 kWh*m-2*y-1
2004 to 2016: 54.2 +/- 3.9

Overall trend is nearly flat, from 2004 to 2016 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 70% of possible measurements available due to sensor downtime!
Comparison between yearly averages at Diekirch, Luxembourg-Bonnevoie and Vianden (rural) [39]; Luxembourg and Vianden values derived by inspection from graphs):


NOx NO2 NO maximum of daily avg. NO2
Diekirch 33    25 8   98
Luxembourg   ~ 35   ~ 65
Vianden   ~ 10   ~45


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)



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!














file: meteolcd_trends.html


02 Jan 2017: Start of update to include 2016 data.