Data trends at meteoLCD: 1998 to 2017 (update finished)
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 2017. 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.
Most important conclusions from 1998 (2002, 2004) to 2017 linear trends: (grey = 2016 situation)
1. Some minor solar dimming since 2004, sunshine
duration decreases by 90
2. Local temperatures show slight warming of 0.04°C/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, thickness of ozone layer slightly decreases by 5 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.
Mean and stdev of the year 2016: 47.1 +/- 33.9
Ozone Column [DU]
Mean and stdev of the year 2017: 313.1 +/- 40.6
minimum : 224.5 (14 Jan)
maximum: 441.7 (06 Feb)
Uccle (Brewer 16&178, DS only): 331.1 +/- 41.4
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Attention: The instrument for measuring CO2 (API Teledine E600) has been replaced by a Vaisala GMP343 sensor the 27 Jun 2017. 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.
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 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.
Mean and stdev of the year 2017
(from monthly averages):
1998 to 2016 :
10.43 +/- 0.55 °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 2002 to 2017: all trends are nearly flat (DTR
would diminish by 0.8°C/century, which is insignificant)
Values of DJF temperature of the year 2017:
NOA: 0.65 NAO normalized index 
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!):
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 2017: 31.09 +/- 13.62 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 2017:
Diekirch: 779.2 mm
Findel: 722.5 mm
1998 to 2017 mean +/- stdev: 695.5 +/- 136.5 mm
The negative trend from 1998 to 2017 seems spectacular:
-91 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-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.
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..
energy on a horizontal plane
Values of total solar energy
of the year 2016:
1998 to 2016 mean +/- stdev: 1108.1 +/- 44.0 kWh/m2
(meteoLCD values derived from pyranometer data by Olivieri's method)
Values of sunshine hours
of the year 2017:
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" . The decline is potentially bad news for the solar PV installations; see a graph on the evolution of the German PV capacity factors here.
 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).
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).
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:
The 2 independent measures of solar energy and UVA doses all point to a slight solar dimming since 2004.
NO and NO2
concentration in ug/m3
(End of measurements useable for trends in 2013. No trends will be calculated for this year!)
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|
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|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: 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|
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|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 decade 2005 to 2014.
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.
02 Jan 2017: Start of update to include 2016 data.
11 Jan 2017: Update to 2017 data finished. Uccle dataset at WOUDC for TOC still uncomplete.