Thursday, June 14, 2012

5129.txt

date: Mon Jun 13 13:16:25 2005
from: Phil Jones <p.jonesatXYZxyz.ac.uk>
subject: Re: global surface temperature time series
to: "Thomas C Peterson" <Thomas.C.PetersonatXYZxyza.gov>, Kevin Trenberth <trenbertatXYZxyz.ucar.edu>

Tom,
Ch 3 of the IPCC report will discuss developments since the TAR. So Ch 1 should
probably go up to the TAR, but it could stop earlier around 1990 (with the first IPCC
report).
A smooth transition will likely be up to the TAR.
The CCSP document probably needs to go into much more detail, so may not
be entirely relevant.
I would have thought that Ch 1 should place greater emphasis on work pre-IPCC.
Some of the references I gave you the other week would be best for this. There is a need
to get across the fact that IPCC didn't invent global temperatures. Groups were working
on this before 1990 and there were two major reviews pre-1990, namely the SCOPE
one in 1986 and the earlier DoE State of the Art report from 1982 (which Bill Clark
edited).
The other thing to get across is that no matter how the data are analysed the results
are pretty much the same - even back to Murray Mitchell.
Cheers
Phil
At 20:11 08/06/2005, Thomas C Peterson wrote:

Dear Kevin & Phil,
I'm currently writing a new section for our IPCC introductory chapter on the history of
global surface temperatures time series. My description will focus on early efforts and
with some general comments about moderately recent developments - i.e., up to the TAR.
As I recall, your chapter discusses developments since the TAR. Which reminded me that
I wanted to send you the revised version of the article describing the new NCDC/NOAA
global temperature time series (attached). Also I wanted to know if I end around the
time of the TAR and refer vaguely to three major groups producing surface temperature
time series - NOAA, NASA and the UK (as per the CCSP VTT document (I'll paste the May
5th version below for reference purposes only) will that be a smooth transition into
your chapter or would you like me to deal with the different global analyses in some
other manner?
Regards,
Tom Peterson
2. SURFACE TEMPERATURES

2.1 Land-based temperature data
Over land temperature data come from fixed weather observing stations with thermometers
housed in special instrument shelters. Records of temperature from many thousands of
such stations exist. Chapter 2 outlines the difficulties in developing reliable surface
temperature datasets. One concern is the variety of changes that may affect temperature
measurements at an individual station. For example, the thermometer or instrument
shelter might change, the time of day when the thermometers are read might change, or
the station might move. These problems are addressed through a variety of procedures
(see Peterson et al., 1998a for a review) that are generally quite successful at
removing the effects of such changes at individual stations (e.g., Vose et al., 2003)
whether the changes are documented in the metadata or detected via statistical analysis
using data from neighboring stations as well (Aguilar et al., 2003). Subtle or
widespread impacts that might be expected from urbanization or the growth of trees
around observing sites might still contaminate a data set. These problems are addressed
either actively in the data processing stage (e.g., Hansen et al., 2001) or through data
set evaluation to ensure as much as possible that the data are not biased (e.g., Jones
et al., 1990; Peterson, 2003; Parker, 2004; Peterson and Owen, 2005). Changes in
regional land use such as deforestation, aforestation, agricultural practices, and other
regional changes in land use are not addressed in the development of these data sets.
Modeling studies have suggested over decades to centuries these affects can be important
on regional space scales (Oleson et al., 2004).

2.2 Marine temperature data
Data over the ocean come from moored buoys, drifting buoys, and volunteer observing
ships. Historically, ships have provided most of the data but in recent years an
increasing number of buoys have been used, placed primarily in data-sparse areas away
from shipping lanes. In addition, satellite data are often used after 1981. Many of the
ships and buoys take both air temperature observations and sea surface temperature (SST)
observations. Night marine air temperature (NMAT) observations have been used to avoid
the problem that the Suns heating of the ships deck can make the thermometer reading
warmer than the actual air temperature. Where there are dense observations of NMAT and
SST, over the long term they track each other very well. However, since marine
observations in an area may only be taken a few times per month, SST has the advantage
over air temperature in that water temperature changes much more slowly than that of
air. Also, there are twice as many SST observations as NMAT from the same platforms as
SSTs are taken during both the day and night and SST data are supplemented in data
sparse areas by drifting buoys which do not take air temperature measurements.
Accordingly, only having a few SST observations in a grid box for a month can still
provide an accurate measure of the average temperature of the month.

2.3 Global surface temperature data
Creating global surface temperature analyses usually involves not only merging land and
ocean data but also considering how best to represent areas where there are few or no
observations. One approach is to only use those grid boxes with data. This conservative
approach avoids any error associated with interpolating data. Unfortunately, the areas
without data are not evenly or even randomly distributed around the world, leading to
considerable uncertainties in the analysis, though it is possible to make an estimate of
these uncertainties. Using the conservative approach, the tropical land surface areas
would be under represented, as would the southern ocean. Therefore, techniques have been
developed to interpolate data to some extent into surrounding data-void regions. A
single group may produce several different such data sets for different purposes. The
choice may depend on whether the interest is a particular local region, the entire
globe, or use of the data set with climate models (Chapter 5). Currently, there are
three main groups creating global analyses of surface temperature (see Table 3A).

2.3.1 NOAA
The National Oceanic and Atmospheric Administration (NOAA) National Climatic
Data Center (NCDC) integrated land and ocean data set (see Table 3A) is derived from in
situ data. The SSTs come from the International Comprehensive Ocean-Atmosphere Data Set
(ICOADS) SST observations release 2 (Slutz et al., 1985; Woodruff et al., 1998; Diaz et
al., 2002). Those that pass quality control tests are averaged into monthly 2^o grid
boxes (Smith and Reynolds, 2003). The land surface air temperature data come from the
Global Historical Climatology Network (GHCN) (Peterson and Vose, 1997) and are averaged
into 5^o grid boxes. A reconstruction approach is used to create complete global
coverage by combining together the faster and slower varying components of temperature
(van den Dool et al., 2000; Smith and Reynolds, 2005).

2.3.2 NASA
The NASA Goddard Institute for Space Studies (GISS) produces a global air temperature
analysis (see Table 3A) known as GISTEMP using land surface temperature data primarily
from GHCN and the U.S. Historical Climatology Network (USHCN; Easterling, et al., 1996).
The NASA team modifies the GHCN/USHCN data by combining at each location the time
records of the various sources and adjusting the non-rural stations in such a way that
their long-term trends are consistent with those from neighboring rural stations (Hansen
et al., 2001). These meteorological station measurements over land are combined with in
situ sea surface temperatures and Infrared Radiation (IR) satellite measurements for
1982 to the present (Reynolds and Smith, 1994; Smith et al., 1996) to produce a global
temperature index (Hansen et al., 1996).

2.3.3 UK
The global land and ocean data set from the UK (see Table 3A) is produced as a joint
effort by the Climatic Research Unit of the University of East Anglia and the Hadley
Centre of the UK Meteorological (Met) Office. The land surface air temperature data are
from Jones and Moberg (2003) of the Climatic Research Unit. The global SST fields are
produced by the Hadley Centre using a blend of COADS and Met Office data bank in situ
observations (Rayner, et al., 2003). The integrated data set is known as HadCRUT2v
(Jones and Moberg, 2003). The temperature anomalies were calculated on a 5^ox5^o grid
box basis. Within each grid box, the temporal variability of the observations has been
adjusted to account for the effect of changing the number of stations or SST
observations in individual grid-box temperature time series (Jones et al., 1997, 2001).
There is no reconstruction of data gaps because of the problems of introducing biased
interpolated values. The global temperature and hemispheric time series have been
created using a technique known as optimal averaging (Parker et al., 2004; Folland et
al., 2001a) which provides estimates of uncertainty in the time series, including the
effects of data gaps and uncertainties related to bias corrections or uncorrected
biases.

2.3.4 Synopsis of surface datasets
Since the three chosen datasets utilize many of the same raw observations, there is a
degree of interdependence. Nevertheless, there are some differences among them as to
which observing sites are utilized. There are three ways to assess how well the changing
network of surface observations monitor global or regional temperature (Jones, 1995).
The first is using frozen grids where analysis using only those grid boxes with data
present in the sparsest years are used to compare to the full data set results from
other years (e.g., Parker et al., 1994). The results generally indicate very small
errors on multi-annual timescales (Jones, 1995). The second technique is subsampling a
spatially complete field, such as model output, only where in situ observations are
available. Again the errors are small (e.g., the standard errors are less than 0.06�C
for the observing period 1880 to 1990; Peterson et al., 1998b). The third technique is
comparing optimum averaging, which fills in the spatial field using covariance matrices,
eigenfunctions or structure functions, with other analyses. Again, very small
differences are found (Smith et al., 2005).
The fidelity of the surface temperature record is further supported by work
such as Peterson et al. (1999) which found that a rural subset of global land stations
had almost the same global trend as the full network and Parker (2004) that found no
signs of urban warming over the period. An important advantage of surface data is the
fact that at any given time there are thousands of thermometers in use that contribute
to a global or other large-scale average. Besides the tendency to cancel random errors,
the large number of stations also greatly facilitates temporal homogenization since a
given station may have several near-neighbors for buddy-checks. While there are
fundamental differences in the global averaging procedures applied, the differing
techniques with the same data produce almost the same results (Wuertz et al., 2005).

2.4 Global surface temperature variations and differences between the data sets
Examination of the three global temperature anomaly time series (T[sfc]) from 1958 to
the present shown in Figure 3.2.4 reveals that the three time series have a very high
level of agreement. They all show some cooling from 1958 to around 1976, followed by
strong warming. That most of the temperature change occurs after the mid 1970s has been
previously documented (Karl et al., 2000; Folland et al., 2001b; Seidel and Lanzante,
2004). The variability of the time series is quite similar as are their trends. The
signature of the El Ni�o-Southern Oscillation (ENSO), whose origin is in the tropics, is
responsible for many of the prominent short-term (several year) up and down swings of
temperature as expected (Trenberth et al., 2002). The strong El Ni�o of 1997-98 stands
out as an especially large warm event within an overall upward trend.

--
Thomas C. Peterson, Ph.D.
Climate Analysis Branch
National Climatic Data Center
151 Patton Avenue
Asheville, NC 28801
Voice: +1-828-271-4287
Fax: +1-828-271-4328

Prof. Phil Jones
Climatic Research Unit Telephone +44 (0) 1603 592090
School of Environmental Sciences Fax +44 (0) 1603 507784
University of East Anglia
Norwich Email p.jonesatXYZxyz.ac.uk
NR4 7TJ
UK
----------------------------------------------------------------------------

No comments:

Post a Comment