August 2016 Global Surface (Land+Ocean) and Lower Troposphere Temperature Anomaly Update

This post provides updates of the values for the three primary suppliers of global land+ocean surface temperature reconstructions—GISS through August 2016 and HADCRUT4 and NCEI (formerly NCDC) through July 2016—and of the two suppliers of satellite-based lower troposphere temperature composites (RSS and UAH) through August 2016.  It also includes a few model-data comparisons.

This is simply an update, but it includes a good amount of background information for those new to the datasets. Because it is an update, there is no overview or summary for this post.  There are, however, summaries for the individual updates. So for those familiar with the datasets, simply fast-forward to the graphs and read the summaries under the heading of “Update”.  

(I’m still on holiday, so I may not get a chance to respond to comments.)

INITIAL NOTES:

We discussed and illustrated the impacts of the adjustments to surface temperature data in the posts:

The NOAA NCEI product is the new global land+ocean surface reconstruction with the manufactured warming presented in Karl et al. (2015).  For summaries of the oddities found in the new NOAA ERSST.v4 “pause-buster” sea surface temperature data see the posts:

Even though the changes to the ERSST reconstruction since 1998 cannot be justified by the night marine air temperature product that was used as a reference for bias adjustments (See comparison graph here), and even though NOAA appears to have manipulated the parameters (tuning knobs) in their sea surface temperature model to produce high warming rates (See the post here), GISS also switched to the new “pause-buster” NCEI ERSST.v4 sea surface temperature reconstruction with their July 2015 update.

The UKMO also recently made adjustments to their HadCRUT4 product, but they are minor compared to the GISS and NCEI adjustments.

We’re using the UAH lower troposphere temperature anomalies Release 6.5 for this post even though it’s in beta form.  And for those who wish to whine about my portrayals of the changes to the UAH and to the GISS and NCEI products, see the post here.

The GISS LOTI surface temperature reconstruction and the two lower troposphere temperature composites are for the most recent month.  The HADCRUT4 and NCEI products lag one month.

Much of the following text is boilerplate that has been updated for all products. The boilerplate is intended for those new to the presentation of global surface temperature anomalies.

Most of the graphs in the update start in 1979.  That’s a commonly used start year for global temperature products because many of the satellite-based temperature composites start then.

We discussed why the three suppliers of surface temperature products use different base years for anomalies in chapter 1.25 – Many, But Not All, Climate Metrics Are Presented in Anomaly and in Absolute Forms of my free ebook On Global Warming and the Illusion of Control – Part 1 (25MB).

Since the July 2015 update, we’re using the UKMO’s HadCRUT4 reconstruction for the model-data comparisons using 61-month filters.

And I’ve resurrected the model-data 30-year trend comparison using the GISS Land-Ocean Temperature Index (LOTI) data.

For a continued change of pace, let’s start with the lower troposphere temperature data.  I’ve left the illustration numbering as it was in the past when we began with the surface-based data.

UAH LOWER TROPOSPHERE TEMPERATURE ANOMALY COMPOSITE (UAH TLT)

Special sensors (microwave sounding units) aboard satellites have orbited the Earth since the late 1970s, allowing scientists to calculate the temperatures of the atmosphere at various heights above sea level (lower troposphere, mid troposphere, tropopause and lower stratosphere). The atmospheric temperature values are calculated from a series of satellites with overlapping operation periods, not from a single satellite. Because the atmospheric temperature products rely on numerous satellites, they are known as composites. The level nearest to the surface of the Earth is the lower troposphere. The lower troposphere temperature composite include the altitudes of zero to about 12,500 meters, but are most heavily weighted to the altitudes of less than 3000 meters.  See the left-hand cell of the illustration here.

The monthly UAH lower troposphere temperature composite is the product of the Earth System Science Center of the University of Alabama in Huntsville (UAH). UAH provides the lower troposphere temperature anomalies broken down into numerous subsets.  See the webpage here.  The UAH lower troposphere temperature composite are supported by Christy et al. (2000) MSU Tropospheric Temperatures: Dataset Construction and Radiosonde Comparisons.  Additionally, Dr. Roy Spencer of UAH presents at his blog the monthly UAH TLT anomaly updates a few days before the release at the UAH website.  Those posts are also regularly cross posted at WattsUpWithThat.  UAH uses the base years of 1981-2010 for anomalies. The UAH lower troposphere temperature product is for the latitudes of 85S to 85N, which represent more than 99% of the surface of the globe.

UAH recently released a beta version of Release 6.0 of their atmospheric temperature product. Those enhancements lowered the warming rates of their lower troposphere temperature anomalies.  See Dr. Roy Spencer’s blog post Version 6.0 of the UAH Temperature Dataset Released: New LT Trend = +0.11 C/decade and my blog post New UAH Lower Troposphere Temperature Data Show No Global Warming for More Than 18 Years. The UAH lower troposphere anomaly data, Release 6.5 beta, through August 2016 are here.

Update:  The August 2016 UAH (Release 6.5 beta) lower troposphere temperature anomaly is +0.44 deg C.  It made a uptick in August (an increase of about +0.05 deg C).

04-uah-tlt

Figure 4 – UAH Lower Troposphere Temperature (TLT) Anomaly Composite – Release 6.5 Beta

RSS LOWER TROPOSPHERE TEMPERATURE ANOMALY COMPOSITE (RSS TLT)

Like the UAH lower troposphere temperature product, Remote Sensing Systems (RSS) calculates lower troposphere temperature anomalies from microwave sounding units aboard a series of NOAA satellites. RSS describes their product at the Upper Air Temperature webpage.   The RSS product is supported by Mears and Wentz (2009) Construction of the Remote Sensing Systems V3.2 Atmospheric Temperature Records from the MSU and AMSU Microwave Sounders. RSS also presents their lower troposphere temperature composite in various subsets. The land+ocean TLT values are here.  Curiously, on that webpage, RSS lists the composite as extending from 82.5S to 82.5N, while on their Upper Air Temperature webpage linked above, they state:

We do not provide monthly means poleward of 82.5 degrees (or south of 70S for TLT) due to difficulties in merging measurements in these regions.

Also see the RSS MSU & AMSU Time Series Trend Browse Tool. RSS uses the base years of 1979 to 1998 for anomalies.

Note:  RSS recently release new versions of the mid-troposphere temperature and lower stratosphere temperature (TLS) products.  So far, their lower troposphere temperature product has not been updated to this new version.

Update:  The August 2016 RSS lower troposphere temperature anomaly is +0.46 deg C.  It dropped very slightly (it’s basically unchanged with a decline of only -0.01 deg C) since July 2016.

05-rss-tlt

Figure 5 – RSS Lower Troposphere Temperature (TLT) Anomalies

GISS LAND OCEAN TEMPERATURE INDEX (LOTI)

Introduction: The GISS Land Ocean Temperature Index (LOTI) reconstruction is a product of the Goddard Institute for Space Studies.  Starting with the June 2015 update, GISS LOTI uses the new NOAA Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4), the pause-buster reconstruction, which also infills grids without temperature samples.  For land surfaces, GISS adjusts GHCN and other land surface temperature products via a number of methods and infills areas without temperature samples using 1200km smoothing. Refer to the GISS description here.   Unlike the UK Met Office and NCEI products, GISS masks sea surface temperature data at the poles, anywhere seasonal sea ice has existed, and they extend land surface temperature data out over the oceans in those locations, regardless of whether or not sea surface temperature observations for the polar oceans are available that month.  Refer to the discussions here and here. GISS uses the base years of 1951-1980 as the reference period for anomalies.  The values for the GISS product are found here. (I archived the former version here at the WaybackMachine.)

Update:  The August 2016 GISS global temperature anomaly is +0.98 deg C. According to the GISS LOTI data, global surface temperature anomalies made a noticeable uptick in August, a +0.13 deg C increase.

01-giss-loti

Figure 1 – GISS Land-Ocean Temperature Index

NCEI GLOBAL SURFACE TEMPERATURE ANOMALIES

NOTE:  The NCEI only produces the product with the manufactured-warming adjustments presented in the paper Karl et al. (2015). As far as I know, the former version of the reconstruction is no longer available online. For more information on those curious NOAA adjustments, see the posts:

And recently:

Introduction: The NOAA Global (Land and Ocean) Surface Temperature Anomaly reconstruction is the product of the National Centers for Environmental Information (NCEI), which was formerly known as the National Climatic Data Center (NCDC).  NCEI merges their new “pause buster” Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4) with the new Global Historical Climatology Network-Monthly (GHCN-M) version 3.3.0 for land surface air temperatures. The ERSST.v4 sea surface temperature reconstruction infills grids without temperature samples in a given month.  NCEI also infills land surface grids using statistical methods, but they do not infill over the polar oceans when sea ice exists.  When sea ice exists, NCEI leave a polar ocean grid blank.

The source of the NCEI values is through their Global Surface Temperature Anomalies webpage.  Click on the link to Anomalies and Index Data.)

Update: The July 2016 NCEI global land plus sea surface temperature anomaly was +0.87 deg C.  See Figure 2. It decreased slightly (a drop of about -0.03 deg C) since June 2016.

02-ncei

Figure 2 – NCEI Global (Land and Ocean) Surface Temperature Anomalies

UK MET OFFICE HADCRUT4 (LAGS ONE MONTH)

Introduction: The UK Met Office HADCRUT4 reconstruction merges CRUTEM4 land-surface air temperature product and the HadSST3 sea-surface temperature (SST) reconstruction.  CRUTEM4 is the product of the combined efforts of the Met Office Hadley Centre and the Climatic Research Unit at the University of East Anglia. And HadSST3 is a product of the Hadley Centre.  Unlike the GISS and NCEI reconstructions, grids without temperature samples for a given month are not infilled in the HADCRUT4 product.  That is, if a 5-deg latitude by 5-deg longitude grid does not have a temperature anomaly value in a given month, it is left blank. Blank grids are indirectly assigned the average values for their respective hemispheres before the hemispheric values are merged.  The HADCRUT4 reconstruction is described in the Morice et al (2012) paper here.  The CRUTEM4 product is described in Jones et al (2012) here. And the HadSST3 reconstruction is presented in the 2-part Kennedy et al (2012) paper here and here.  The UKMO uses the base years of 1961-1990 for anomalies.  The monthly values of the HADCRUT4 product can be found here.

Update (Lags One Month):  The July 2016 HADCRUT4 global temperature anomaly is +0.74 deg C. See Figure 3.  It is unchanged from June to July 2016.

03-hadcrut4Figure 3 – HADCRUT4

COMPARISONS

The GISS, HADCRUT4 and NCEI global surface temperature anomalies and the RSS and UAH lower troposphere temperature anomalies are compared in the next three time-series graphs. Figure 6 compares the five global temperature anomaly products starting in 1979.  Again, due to the timing of this post, the HADCRUT4 and NCEI updates lag the UAH, RSS, and GISS products by a month. For those wanting a closer look at the more recent wiggles and trends, Figure 7 starts in 1998, which was the start year used by von Storch et al (2013) Can climate models explain the recent stagnation in global warming?  They, of course, found that the CMIP3 (IPCC AR4) and CMIP5 (IPCC AR5) models could NOT explain the recent slowdown in warming, but that was before NOAA manufactured warming with their new ERSST.v4 reconstruction…and before the strong El Niño of 2015/16.

Figure 8 starts in 2001, which was the year Kevin Trenberth chose for the start of the warming slowdown in his RMS article Has Global Warming Stalled?

Because the suppliers all use different base years for calculating anomalies, I’ve referenced them to a common 30-year period: 1981 to 2010.  Referring to their discussion under FAQ 9 here, according to NOAA:

This period is used in order to comply with a recommended World Meteorological Organization (WMO) Policy, which suggests using the latest decade for the 30-year average.

The impacts of the unjustifiable adjustments to the ERSST.v4 reconstruction are visible in the two shorter-term comparisons, Figures 7 and 8.  That is, the short-term warming rates of the new NCEI and GISS reconstructions are noticeably higher than the HADCRUT4 data.  See the June 2015 update for the trends before the adjustments.

06-comparison-1979-start

Figure 6 – Comparison Starting in 1979

#####

07-comparison-1998-start

Figure 7 – Comparison Starting in 1998

#####

08-comparison-2001-start

Figure 8 – Comparison Starting in 2001

Note also that the graphs list the trends of the CMIP5 multi-model mean (historic through 2005 and RCP8.5 forcings afterwards), which are the climate models used by the IPCC for their 5th Assessment Report.  The metric presented for the models is surface temperature, not lower troposphere.

AVERAGE

Figure 9 presents the average of the GISS, HADCRUT and NCEI land plus sea surface temperature anomaly reconstructions and the average of the RSS and UAH lower troposphere temperature composites.  Again because the HADCRUT4 and NCEI products lag one month in this update, the most current monthly average only includes the GISS product.

09-surface-and-tlt-averages

Figure 9 – Average of Global Land+Sea Surface Temperature Anomaly Products

MODEL-DATA COMPARISON & DIFFERENCE

As noted above, the models in this post are represented by the CMIP5 multi-model mean (historic through 2005 and RCP8.5 forcings afterwards), which are the climate models used by the IPCC for their 5th Assessment Report.

Considering the uptick in surface temperatures in 2014, 2015 and now 2016 (see the posts here and here), government agencies that supply global surface temperature products have been touting “record high” combined global land and ocean surface temperatures. Alarmists happily ignore the fact that it is easy to have record high global temperatures in the midst of a hiatus or slowdown in global warming, and they have been using the recent record highs to draw attention away from the difference between observed global surface temperatures and the IPCC climate model-based projections of them.

There are a number of ways to present how poorly climate models simulate global surface temperatures.  Normally they are compared in a time-series graph.  See the example in Figure 10. In that example, the UKMO HadCRUT4 land+ocean surface temperature reconstruction is compared to the multi-model mean of the climate models stored in the CMIP5 archive, which was used by the IPCC for their 5th Assessment Report. The reconstruction and model outputs have been smoothed with 61-month running-mean filters to reduce the monthly variations.  The climate science community commonly uses a 5-year running-mean filter (basically the same as a 61-month filter) to minimize the impacts of El Niño and La Niña events, as shown on the GISS webpage here. Using a 5-year running mean filter has been commonplace in global temperature-related studies for decades. (See Figure 13 here from Hansen and Lebedeff 1987 Global Trends of Measured Surface Air Temperature.)  Also, the anomalies for the reconstruction and model outputs have been referenced to the period of 1880 to 2013 so not to bias the results.  That is, by using the almost the full term of the data, no one with the slightest bit of common sense can claim I’ve cherry picked the base years for anomalies with this comparison.

10-model-data-time-series

Figure 10

It’s very hard to overlook the fact that, over the past decade, climate models are simulating way too much warming…even with the small recent El Niño-related uptick in the data.

Another way to show how poorly climate models perform is to subtract the observations-based reconstruction from the average of the model outputs (model mean). We first presented and discussed this method using global surface temperatures in absolute form. (See the post On the Elusive Absolute Global Mean Surface Temperature – A Model-Data Comparison.)  The graph below shows a model-data difference using anomalies, where the data are represented by the UKMO HadCRUT4 land+ocean surface temperature product and the model simulations of global surface temperature are represented by the multi-model mean of the models stored in the CMIP5 archive. Like Figure 10, to assure that the base years used for anomalies did not bias the graph, the full term of the graph (1880 to 2013) was used as the reference period.

In this example, we’re illustrating the model-data differences smoothed with a 61-month running mean filter. (You’ll notice I’ve eliminated the monthly data from Figure 11. Example here.  Alarmists can’t seem to grasp the purpose of the widely used 5-year (61-month) filtering, which as noted above is to minimize the variations due to El Niño and La Niña events and those associated with catastrophic volcanic eruptions.)

11-model-data-difference

Figure 11

The difference now between models and data is almost worst-case, comparable to the difference at about 1910. 

There was also a major difference, but of the opposite sign, in the late 1880s. That difference decreases drastically from the 1880s and switches signs by the 1910s.  The reason:  the models do not properly simulate the observed cooling that takes place at that time.  Because the models failed to properly simulate the cooling from the 1880s to the 1910s, they also failed to properly simulate the warming that took place from the 1910s until the 1940s. (See Figure 12 for confirmation.) That explains the long-term decrease in the difference during that period and the switching of signs in the difference once again.  The difference cycles back and forth, nearing a zero difference in the 1980s and 90s, indicating the models are tracking observations better (relatively) during that period. And from the 1990s to present, because of the slowdown in warming, the difference has increased to greatest value ever…where the difference indicates the models are showing too much warming.

It’s very easy to see the recent record-high global surface temperatures have had a tiny impact on the difference between models and observations.

See the post On the Use of the Multi-Model Mean for a discussion of its use in model-data comparisons.

MODEL-DATA COMPARISON – 30-YEAR RUNNING TRENDS

Yet another way to show how poorly climate models simulate surface temperatures is to compare 30-year running trends of global surface temperature data and the model-mean of the climate model simulations of it. See Figure 12. In this case, we’re using the global GISS Land-Ocean Temperature Index for the data.  For the models, once again we’re using the model-mean of the climate models stored in the CMIP5 archive with historic forcings to 2005 and worst case RCP8.5 forcings since then.

12-model-data-30-year-trends

Figure 12

There are numerous things to note in the trend comparison. First, there is a growing divergence between models and data starting in the early 2000s. The continued rise in the model trends indicates global surface warming is supposed to be accelerating, but the data indicate little to no acceleration since then. Second, the plateau in the data warming rates begins in the early 1990s, indicating that there has been very little acceleration of global warming for more than 2 decades.  This suggests that there MAY BE a maximum rate at which surface temperatures can warm. Third, note that the observed 30-year trend ending in the mid-1940s is comparable to the recent 30-year trends. (That, of course, is a function of the new NOAA ERSST.v4 data used by GISS.)  Fourth, yet that high 30-year warming ending about 1945 occurred without being caused by the forcings that drive the climate models.  That is, the climate models indicate that global surface temperatures should have warmed at about a third that fast if global surface temperatures were dictated by the forcings used to drive the models. In other words, if the models can’t explain the observed 30-year warming ending around 1945, then the warming must have occurred naturally. And that, in turns, generates the question: how much of the current warming occurred naturally? Fifth, the agreement between model and data trends for the 30-year periods ending in the 1960s to about 2000 suggests the models were tuned to that period or at least part of it. Sixth, going back further in time, the models can’t explain the cooling seen during the 30-year periods before the 1920s, which is why they fail to properly simulate the warming in the early 20th Century.

One last note, the monumental difference in modeled and observed warming rates at about 1945 confirms my earlier statement that the models can’t simulate the warming that occurred during the early warming period of the 20th Century.

MONTHLY SEA SURFACE TEMPERATURE UPDATE

The most recent sea surface temperature update can be found here.  The satellite-enhanced sea surface temperature composite (Reynolds OI.2) are presented in global, hemispheric and ocean-basin bases.

RECENT RECORD HIGHS

We discussed the recent record-high global sea surface temperatures for 2014 and 2015 and the reasons for them in General Discussions 2 and 3 of my recent free ebook On Global Warming and the Illusion of Control (25MB).   The book was introduced in the post here (cross post at WattsUpWithThat is here).

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About Bob Tisdale

Research interest: the long-term aftereffects of El Niño and La Nina events on global sea surface temperature and ocean heat content. Author of the ebook Who Turned on the Heat? and regular contributor at WattsUpWithThat.
This entry was posted in TLT and LOST Updates. Bookmark the permalink.

29 Responses to August 2016 Global Surface (Land+Ocean) and Lower Troposphere Temperature Anomaly Update

  1. Pingback: Das Jahr 2016 ist nun kälter als 1998: „Global Warming“ Reality Check August 2016 – wobleibtdieglobaleerwaermung

  2. ren says:

    Decrease anomalies of sea surface temperature.

  3. Bob Tisdale says:

    ren, how do you equate earthquake reports to a volcano?

  4. ren says:

    Axial Seamount
    Axial Seamount is an active submarine volcano on the Juan de Fuca Ridge in the NE Pacific. It rises to a depth of 1400 m below sea level and is located approximately 300 miles off the coast of Oregon.
    http://www.pmel.noaa.gov/eoi/axial_site.html
    http://novae.ocean.washington.edu/files/chadwick_novae_2015_slides-20150421121549.pdf

  5. ren says:

    Axial Seamount is an underwater mountain that juts up 3,000 feet (900 meters) from the ocean floor, and is part of a string of volcanoes that straddle the Juan de Fuca Ridge, a tectonic-plate boundary where the seafloor is spreading apart.
    http://www.csmonitor.com/Science/2015/0503/Underwater-volcano-spewing-lava-off-Oregon-coast-video

  6. Bob Tisdale says:

    ren, you haven’t answered my question.

  7. ren says:

    As the plates spread apart in the Pacific where we have an outflow of lava. I wrote only that volcanoes in this area are active. Of course, to be expected earthquakes in the region, as well as the warmer water.

  8. ren says:

    Of course, you consider it unfounded.

  9. Bob Tisdale says:

    ren says: “Of course, you consider it unfounded.”

    Of course. Earthquakes and volcanic eruptions are not necessarily associated with one another.

  10. ren says:

    Do not forget that Axial erupted in May 2015.

  11. Bob Tisdale says:

    ren, The Blob was making its presence known in January 2014, a year and a half before May 2015. My first post on it:
    https://bobtisdale.wordpress.com/2014/02/05/the-hotspot-in-the-north-pacific/

  12. ren says:

    “A hydrothermal vent is a fissure in a planet’s surface from which geothermally heated water issues. Hydrothermal vents are commonly found near volcanically active places, areas where tectonic plates are moving apart, ocean basins, and hotspots.”

  13. Bob Tisdale says:

    ren, show me the data for the hydrothermal vent and how it relates to the recent earthquake with which you started.

  14. ren says:

    The Juan de Fuca Ridge is a tectonic spreading center located off the coasts of the state of Washington in the United States and the province of British Columbia in Canada. It runs northward from a transform boundary, the Blanco Fracture Zone, to a triple junction with the Nootka Fault and the Sovanco Fracture Zone. To its east is the Juan de Fuca Plate, which together with the Gorda Plate to its south and the Explorer Plate to its north, is what remains of the once-vast Farallon Plate which has been largely subducted under the North American Plate. To its west is the Pacific Plate. The Juan de Fuca Ridge is a remnant of the former Pacific–Farallon Ridge.

    The first cataclysmic hydrothermal vent or megaplume was found on the ridge in 1986 near 44°49′N 130°14′W / 44.817°N 130.233°W.[1] [2]
    http://broom02.revolvy.com/main/index.php?s=Juan%20de%20Fuca%20Ridge

  15. Bob Tisdale says:

    ren, thanks for continuing this line of discussion and not letting my skepticism stop you.

    Curiously, an earlier Blob appeared in August 1986, the month of the megaplume from the Juan de Fuca Ridge.
    http://www.pmel.noaa.gov/pubs/outstand/bake1050/bake1050.shtml

    http://climexp.knmi.nl/ps2pdf.cgi?file=data/g20160927_0045_25009_1.eps.gz

    This will be worth a post…I’ll give you credit. I still have some difficulties, like the more recent Blob forming west of there and migrating eastward.

  16. ren says:

    Bob currently operate on the Sun big coronal holes, which cause magnetic storms. It has been observed many times during these storms increase in the level of lava, eg. in a volcano at Kiluea and an increase in seismic activity.
      This old and very dense tectonic plate quite quickly collapses under Oregon.

  17. ren says:

    Wind may move the warm water on the surface to the east.

  18. ren says:

    Sorry: volcano Kilauea.

  19. ren says:

    HAWAIIAN VOLCANO OBSERVATORY DAILY UPDATE
    U.S. Geological Survey
    Monday, September 26, 2016, 7:02 AM HST (Monday, September 26, 2016, 17:02 UTC)

    KILAUEA VOLCANO (VNUM #332010)
    19°25’16” N 155°17’13” W, Summit Elevation 4091 ft (1247 m)
    Current Volcano Alert Level: WATCH
    Current Aviation Color Code: ORANGE

    Activity Summary: Kīlauea Volcano continues to erupt at its summit and at the Puʻu ʻŌʻō vent on its East Rift Zone. Summit tiltmeters recorded an inflationary tilt over the past day, and the lava lake level rose an estimated 16 m (52 ft) since yesterday morning. The 61G lava flow continues to flow into the sea at Kamokuna. For the past week, breakouts have occurred about 2 km (1.2 mi) inland from the coast. The flow poses no threat to nearby communities.

  20. ren says:

    BNPD in Indonesia reported yesterday that an eruption occurred at Mount Barujari (child of Rinjani) at 14:45 local time. An ash plume rose up to 2,000 m and the PVMBG raised the alert level from Normal (Level I) to Alert (Level II) at 15:00. Based on information of the Mount Rinjani National Park (from 25-27 September) there were 289 tourists (333 foreign tourists, 56 local) in the area. As of the last report agencies and volunteers are coordinating to determine the location and condition of tourists.
    The current recommendation for the volcano is a 3 km radius evacuation, with 1,113 people evacuating as of yesterday.
    http://earthquake-report.com/2015/04/02/volcano-news-by-volcanologist-janine-krippner/

  21. frankclimate says:

    o/t, anyway interesting IMO: http://www.earth-syst-dynam-discuss.net/esd-2016-35/esd-2016-35.pdf about stepchanges in the climate system. You are mentioned (p. 26)… Have fun!

  22. Bob Tisdale says:

    Thanks for the heads-up, frankclimate.

    Cheers.

  23. ren says:

    The energy of magnetic oscillations and the level of lava in the volcano Kilauea.

  24. Pingback: Das Jahr 2016 bleibt kälter als 1998: „Global Warming“ Reality Check September 2016 – wobleibtdieglobaleerwaermung

  25. Pingback: The Divergence between Surface and Lower Troposphere Global Temperature Datasets and its Implications | Bob Tisdale – Climate Observations

  26. Pingback: The Divergence between Surface and Lower Troposphere Global Temperature Datasets and its Implications | Watts Up With That?

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