Species decline or scaremongering?

tiger in the snow

A study from the University of Exeter on species decline declares “climate change warnings not exaggerated.”

However the press release leaves one singularly unimpressed with the raw activism of the lead researcher, who says: “It is time to stop using the uncertainties as an excuse for not acting. We need to act now to prevent threatened species from becoming extinct. This means cutting carbon emissions.”

The paper is in press, but it mentions “existing responses to climate change.”

Decreased ice cover in the Bering Sea reduced the abundance of bivalve molluscs from about 12 to three per square metre over a very short period of time (1999-2001). These shells are the main food source for species higher up the food chain, such as Spectacled Eider.

Arctic sea ice cover is mostly a function of the winds. It’s a shame the molluscs have declined, but it was not caused by global warming, anthropogenic or not.

Climatic warming and droughts are causing severe declines in once-common amphibian species native to Yellowstone National Park in the United States of America. Between 1992-1993 and 2006-2008, the number of blotched tiger salamander populations fell by nearly half, the number of spotted frog populations by 68 per cent, and the number of chorus frog populations by 75 per cent.

So the recent amphibian virus infections widespread through North America have nothing to do with these reductions?

In Antarctica, few animals exist on land, but one of the most abundant, a nematode worm living in the soil in dry, cold valleys experienced a 65 per cent decline between 1993 and 2005 as a result of climate change.

First, so what? Second, there’s no evidence of warming temperatures in Antarctica except Steig’s deprecated paper using the peninsula data “smeared” over the rest of the continent. Quite naughty. Third, there are few scientists even in the dry valleys; I demand a recount.

Next, we’re treated to examples of “predicted responses” to climate change. How insulting is this? “We found some guys who made these forecasts. Be afraid, they could happen.”

This is not science!

On Tenerife, an endemic plant, the Caňadas rockrose has a 74 to 83 per cent chance of going extinct in the next 100 years as a result of climate change related droughts. [Who “calculates” these odds?]

In Madagascar, climate warming is predicted to cause endemic reptiles and amphibians, often found in mountain ranges, to retreat towards the summit of the mounts. With a warming of just two degrees Celsius, well within current projections, three species are predicted to lose all of their habitat.

Birds living in northern Boreal Forests in Europe are expected to decline as a result of global warming. Species such as Dotterel are predicted to decline by 97 per cent by 2100 and species such as Two-barred Crossbill and Pine Grosbeak could lose their entire range within Fenno-Scandia.

Gosh.

124 Thoughts on “Species decline or scaremongering?

  1. Richard C (NZ) on August 15, 2011 at 11:49 pm said:

    Nick, I’ve checked the Climate4you spec. humidity plot and discovered that it is for 90N – 90S, not 20N – 20S (tropics as in the D&D Fig 3 plot) so I’m wrong to compare one to the other. However I still don’t know what radiosonde data D&D have plotted in Fig 3 now that I’ve checked the ESRL database here:-

    http://www.esrl.noaa.gov/psd/cgi-bin/data/timeseries/timeseries1.pl

    Here’s the raw data for these parameters:-

    300mb Pressure Level Specific Humidity (gr/kg)
    Latitude Range used: 20.0 to -20.0
    Longitude Range used: 180.0 to 180.0
    Weighted area grids = yes

    http://www.esrl.noaa.gov/psd/cgi-bin/data/timeseries/timeseries.pl?ntype=1&var=Specific+Humidity+%28up+to+300mb+only%29&level=300&lat1=20&lat2=-20&lon1=180&lon2=180&iseas=0&mon1=0&mon2=0&iarea=1&typeout=1&Submit=Create+Timeseries

    And the plot of that data:-

    http://www.esrl.noaa.gov/psd/cgi-bin/data/timeseries/timeseries.pl?ntype=1&var=Specific+Humidity+%28up+to+300mb+only%29&level=300&lat1=20&lat2=-20&lon1=180&lon2=180&iseas=0&mon1=0&mon2=0&iarea=1&typeout=2&Submit=Create+Timeseries

    This plot from 1979 – 2009 does not look like the D&D plot. If we take 1980 for example, the annual average of the 12 ESRL monthly datapoints is 0.6 g/kg.but the D&D plot shows 0.5. Similarly 1990 ESRL 0.62 D&D 0.51, 1995 ESRL 0.54 D&D 0.45, 1998 ESRL 0.488 D&D 0.45, 2000 ESRL 0.4 D&D 0.4, 2005 ESRL 0.48 D&D 0.4, 2009 ESRL 0.49 D&D 0.4.

    Except for 2000 where ESRL corresponds exactly to D&D, the other D&D values seem about 0.1 g/kg too low last century and 0.085 too low this century. Note that ESRL 1998 is the same as ESRL 2009 at 0.49 but D&D has 1998 0.45 and 2009 0.4.

    Using the above ESRL values for D&D’s linear trend analysis might work in favour of D&D’s case (except for this century) but the difference in data ESRL vs D&D calls into question what data D&D actually used. I’ve probably got my story checked and sorted enough to make a query to Garth Paltridge now.

    What you may not have picked up on is that the ERA-40 reanalysis (triangle symbol series) also uses radiosonde data and that ERA-40 is the new ECMWF 40-year reanalysis. Therefore ERA-40 supersedes the “interim” ECMWF series that D&D have plotted (dot symbol series). This leaves only MERRA and JRA in conflict with NCEP trend-wise (and JRA is essentially flat 1979 – 1998). See:-

    ERA-40 Project Report Series No. 2
    The long-term performance of the radiosonde observing system to be used in ERA-40
    August 2000
    Kazutoshi Onogi

    http://www.ecmwf.int/research/era/_docs/ERA40PRS_2.pdf

    Figure 1 page 25 shows radiosonde distribution by manufacturer January 1988

    I cant ascertain why there is such a difference between NCEP and ERA-40 absolute values i.e. where are the ERA-40 adjustments to NCEP documented? There’s a number of plots at the bottom of the report that give a clue but that’s as far as I’ve got.

    The following article describes radiosondes, their components and the operation of them:-

    RADIOSONDES — An Upper Air Probe

    http://www.aos.wisc.edu/~hopkins/wx-inst/wxi-raob.htm

    The following is the observing requirements for the GCOS Upper-Air Network (GUAN):-

    http://gosic.org/gcos/GUAN-spec.htm

    Note the Design Principles and Best Practices e.g.

    # Changes of bias caused by changes in instrumentation should be evaluated by a sufficient overlapping period of observation (perhaps, as much as a year) or by making use of the results of instrument intercomparisons made at designated test sites.

    I note that Paltridge, Arking and Pook says this about satellites:-

    “As with radiosonde measurements, satellite observations of upper-level humidity have their own problems and must be treated with caution. Most work to date concerns analysis of the channel 12 data of the High-Resolution Infrared Radiation Sounder (HIRS) of the NOAA polar orbiting satellites. Channel 12 is located near the center of the 6.7-μm rotational–vibrational emission band of water vapor and is sensitive both to the water vapor emission and to the temperature of a broad layer from about 200 to 500 hPa. Soden et al. (2005) find no trend in the global mean channel 12 radiance. Since model simulations with constant relative humidity also show no trend and simulations with constant specific humidity show a positive trend, they conclude that the HIRS observations are consistent with constant relative humidity. A potential practical issue concerns the fact that the positive trend for the constant q simulation appears only in the last 10 years of the 24-year simulation. Perhaps more to the present point, Bates and Jackson (2001) find wide variations in the HIRS-derived RH trend for different latitude zones. The trend is negative in the Southern Hemisphere between 10° and 60° S and negative in the Northern Hemisphere between 10° and 40° N. It is significantly positive only for the tropical zone from 10° N to 10° S.”

    And

    “There are still many problems associated with satellite retrieval of the humidity information pertaining to a particular level of the atmosphere— particularly in the upper troposphere. Basically, this is because an individual radiometric measurement is a complicated function not only of temperature and humidity (and perhaps of cloud cover because “cloud clearing”
    algorithms are not perfect), but is also a function of the vertical distribution of those variables over considerable depths of atmosphere. It is difficult to assign a trend in such measurements to an individual cause.”

    It’s a case of two evils but one has been around longer than the other.

  2. Richard C (NZ) on August 16, 2011 at 1:51 pm said:

    Turns out that the MERRA reanalysis (the main satellite input based plank of Dessler and Davis) has problems of its own. See:-

    A Comparison of MERRA and NARR Reanalyses with the DOE ARM SGP data

    Aaron D. Kennedy, Xiquan Dong, and Baike Xi

    Shaocheng Xie and Yunyan Zhang

    Junye Chen

    2011 (PRELIMINARY ACCEPTED VERSION)

    257 Near the level of non-divergence (~400-500hPa), all biases change in sign from negative to positive. The MERRA bias has a peak of 8% near 300 hPa and then decreases towards 0% at 100 hPa,

    6% is approximately 1 g kg-1 so MERRA is more than 1 g kg-1 too moist at 300hPa RH in the study location.

    262 The MERRA moist bias in the upper troposphere is also larger during the summer months and doubles during time periods of precipitation.

    264 To better understand these humidity biases, histograms were calculated at 925 hPa and 200 hPa (Fig. 2) which represent the boundary layer and near the tropopause, respectively. […] Fig. 2a clearly shows that MERRA is dry [below 300hPa] as its distribution is shifted approximately 5-10% to the left of the other datasets.

    306 MERRA captures the general shape of RH at the ARM SGP site (Fig. 4c), but with a ~5% negative bias throughout the year in the upper troposphere except during the late spring and early summer when convection is most common at the ARM SGP site. compared to ARM and NARR. Seasonal RMSE plots (not shown) demonstrate that the largest disagreement between MERRA and ARM continuous forcing for mixing ratio occur during the spring (MAM) and summer seasons (JJA) in the boundary layer and upper troposphere. The maximum RH for MERRA occurs during June when boundary layer humidity is highest. As will be shown later, cloud fraction in MERRA also peaks in June, suggesting that this may be a byproduct of the convective parameterization used in the AGCM. This is also supported by the fact that the RH bias in the upper troposphere doubles during periods of precipitation in the summer months. Like ARM and NARR, additional peaks occur during January and March. It is concluded that the seasonal cycle of RH from three different datasets generally agree during this 319 3-yr period except for the upper troposphere during the summer months. During this time period, MERRA has a considerable positive bias (~10-15%)

    This effects radiation.

    488 MERRA has larger biases than NARR for LW-down under both clear-sky and all-sky conditions (-20 and -19 w m-2). Compared to ARM and NARR, these negative biases are consistent with the drier conditions in MERRA as demonstrated in Figs. 1, 2, and 4 and the seasonal variations of precipitable water vapor (not shown). Atmospheric water vapor is extremely important for LW-down fluxes under both clear-sky and all-sky conditions (Dong et al. 2006) and is supported by the fact these biases are largest during the warm season.

    Figure 1. Biases of ARM continuous forcing (black), NARR (red), and MERRA (blue) relative to the ARM Cloud Modeling Best Estimate (CMBE) sounding profiles during the period 1999-2001 for (a) temperature, (b) zonal wind, (c) meridional wind, and (d) relative humidity. (e)-(h) are the same as (a)-(d) except for the RMSE.

    MERRA bias @ 300hPa in the study location

    Temperature: 0.25 K (positive and warm)

    Relative Humidity: 5% (positive and moist)

    Figure 8. Monthly total precipitation measured over the ARM SGP domain by ARM (black), NARR (red) and MERRA (blue) during the period 1999-2001.

    MERRA is the outlier, not enough precipitation in the study location.

  3. Richard C (NZ) on August 16, 2011 at 5:22 pm said:

    This “Rebuttal of Miskolczi’s alternative greenhouse theory”, an article by Rob van Dorland and Piers M. Forster cites “A more robust analysis of water vapour changes by Mears et al. (2010) shows that total column water vapour is increasing over the oceans in the period 1988-2009 at a rate of 0.27 +/- 0.08 mm/decade”

    So what? That’s just TCWV over the ocean. The “more robust analysis” (Mears et al (2010) turns out to be merely a chapter (page 29) in the report “State of the Climate in 2009” headed

    c. Hydrological cycle
    1) Total column water vapor—C. Mears, J.
    Wang, S. Ho, L. Zhang, and X. Zhou

    http://www.indiaenvironmentportal.org.in/files/climate-assessment-2009-lo-rez.pdf

    Figure 2.11 shows the over-the-ocean-only 0.27 +/- 0.08 mm/decade TCWV trend (NOTE THAT THE TREND IS A FRACTION OF A MILLIMETRE PER DECADE) that van Dorland and Forster describe as “more robust”. But when we look at GLOBAL TCWV it’s a different story entirely. See the following plot:-

    Total Column Water Vapor (cm):
    21-Year Deviations and Anomalies of Region Monthly Mean From Total Period Mean Over Global

    Go to the bottom windows of this page:-

    http://isccp.giss.nasa.gov/products/browseatmos.html

    Select a variable:[Total Column Water Vapour]

    Select a geographic region: [Global]

    View Plot

    First, the anomaly baseline is 2.41 Centimetres and corresponds with the Climate4you TCWV plot here:-

    http://climate4you.com/ (click “Greenhouse Gasses”)

    Second, the data is from the file B128B129glbp.dat but I can’t open it from the ftp site and plot it to obtain a trend but eyeballing the plot reveals that over 2 1/3 decades the procession is:-

    1984 24.5 mm

    1988 25.1 mm

    1992 24.1 mm

    1996 25.0 mm
    1998 26.0 mm
    2000 23.0 mm

    2004 23.1 mm

    2008 22.1 mm

    The trend from the 4 yr values is -0.1098x or -1.098 mm/decade (GET THAT? DECREASING ONE WHOLE MILLIMETRE PER DECADE AND THAT INCLUDES OVER-OCEAN DATA).

    Note that this is SATELLITE data, see:-

    ISCCP OVERVIEW

    http://isccp.giss.nasa.gov/overview.html

    TCWV is significantly influenced by evapotranspiration from Northern Hemisphere land (land area greater in the NH (70%) than in the SH (30$)). By looking at over-ocean-only TCWV van Dorland and Forster neglect this most important item.

  4. Richard C (NZ) on August 16, 2011 at 5:32 pm said:

    Miklos Zagoni rebuts van Dorland and Forster here|-

    http://miskolczi.webs.com/MiklosZagoni_ReplyToRob.pdf

    Dear Dr. Dorland, Dear Professor Forster,

    Gentlemen
    With full respect, I must say that your attempt to understand Miskolczi’s results correctly was only a partial success.

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