Transcript The Surface Temperature Record

A secret history of the observed
surface temperature record
Phil Jones CRU, UEA
• Developing the Global Temperature Record
• Terrestrial and Marine components
• Biases
Boulder, June 2009
Earliest work – late 1970s and early 1980s
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Interpolation used inverse-distance weighted best fit planes with a
base period of 1946-60
The base period (i.e. using anomalies) is crucial in all the studies. It
overcomes most elevation issues and some urbanization issues (later)
Grid points at 10° longitude by 5° latitude intersections
Station temperature data came from an NCAR DS, supplied on a
6250bpi tape
Assessed data for outliers and removed those beyond reasonable
bounds
Programs ran on an IBM computer at Cambridge, for which cards had to
be submitted
Assessment of the effects of sparser coverage in early decades
Compared results with all earlier studies
Jones, P.D., Wigley, T.M.L. and Kelly, P.M., 1982: Variations in surface air temperatures, Part 1: Northern Hemisphere, 18811980. Monthly Weather Review 110, 59-70
Kelly, P.M., Jones, P.D., Sear, C.B., Cherry, B.S.G. and Tavakol, R.K., 1982: Variations in surface air temperatures, Part 2: Arctic
regions, 1881-1980. Monthly Weather Review 110, 71-83
Raper, S.C.B., Wigley, T.M.L., Mayes, P.R., Jones, P.D. and Salinger, M.J., 1984. Variations in surface air temperature. Part 3: The
Antarctic, 1957-82. Monthly Weather Review 112, 1341-1353
Pre-CRU land temperature series, each adjusted to have
Brohan et al average (HadCRUT3) over their last 30 years
of overlap (from Ch1 of AR4: zero line is 1961-90)
AR4 shouldn’t have compared land only series with HadCRUT3!
Mid -1980s
• Extended the spatial coverage of the station record, by
digitizing records held in Met Office and other archives
• Assessed homogeneity of the records (subjectively)
• Base period of 1951-70
• Each station associated with its nearest grid point (as in 1982)
and weights were inverse distance
• More extended analysis of the effects of fewer stations in the
early decades (using frozen grids)
• Record extended back to 1851 (from 1881 earlier)
• First combined land and marine temperature record in 1986
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Jones, P.D., Raper, S.C.B., Bradley, R.S., Diaz, H.F., Kelly, P.M. and Wigley, T.M.L., 1986: Northern
Hemisphere surface air temperature variations: 1851-1984. Journal of Climate and Applied Meteorology
25, 161-179
Jones, P.D., Raper, S.C.B. and Wigley, T.M.L., 1986: Southern Hemisphere surface air temperature
variations: 1851-1984. Journal of Climate and Applied Meteorology 25, 1213-1230
Jones, P.D., Wigley, T.M.L. and Wright, P.B., 1986: Global temperature variations, 1861-1984. Nature 322,
430-434
Comparison of the versions across the years
(1982, 1986, 1994, 2003 and 2006)
NH – Top
SH – Bottom
Left – as was
Right – 1951-80
Q – was all the
effort worth
it? It shows
how robust the
series is!
Questions that began to be asked (1)
(some despite being discussed the papers)
• Effect of sparser coverage – don’t you need more stations?
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The frozen grid analyses had effectively answered this, but many only became
convinced once the uncertainties were displayed with error ranges
These also led to the understanding of the number of spatial degrees of
freedom (Neff)
Looking at numerous seasonal and annual temperature anomaly maps indicated
that Neff must be much smaller than the number of observing sites, and that
the number varies depending on the season and on the timescale
The paper that derived the necessary formulae (Jones et al., 1997) was based
on the Wigley et al. (1984) r-bar paper
The fact that Neff was about 100 led to the cottage industry of reconstruction
the past temperatures from natural and documentary proxies
The fact that r-bar is higher in winter implies that these would be much
better, if we had more winter-responding proxies
Wigley, T.M.L., Briffa, K.R. and Jones, P.D., 1984: On the average value of correlated time series with applications in
dendroclimatology and hydrometeorology. Journal of Climate and Applied Meteorology 23, 201-213
Jones, P.D., Osborn, T.J. and Briffa, K.R., 1997: Estimating sampling errors in large-scale temperature averages. J.
Climate 10, 2548-2568
Questions that began to be asked (2)
• Isn’t the fact that most sites have
moved more than once in their history
important?
• We’d addressed the long-term homogeneity
of the station series in the mid-1980s
• The effort was large in person years, but it’s
affect was barely noticeable (the comparison
of the 1982 and 1986 papers showed this)
• Clearly important for individual series, but
not that vital as the space scale increases
Homogenisation adjustment uncertainty
Black line is based on 763 sets of
adjustments
Red – hypothesised distribution
Blue – the difference, so that used
for stations where adjustments have
not been made
Similar bimodal distribution in a
recent paper on the USHCN
Menne et al
(2009) in BAMS
USHCN – all and 70 best (latter partly determined by surface stations.org)
The 70 obviously omit large parts of the contiguous US
Questions that began to be asked (3)
• Isn’t the world warming because of many stations
being located in cities?
• The urbanization issue! People always remember the greatest
UHI that someone has shown on a particular day!
• They forget the similar warming between the land and the ocean
components
• Comparisons of rural-only station datasets
• One new example here (London)
• Recognition that UHIs exist, but what matters is urban-related
warming trends not the UHI size
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Jones, P.D., Groisman, P.Ya., Coughlan, M., Plummer, N., Wang, W-C. and Karl, T.R., 1990: Assessment of
urbanization effects in time series of surface air temperature over land. Nature 347, 169-172
Jones, P.D., Lister, D.H. and Li, Q., 2008: Urbanization effects in large-scale temperature records, with an
emphasis on China. J. Geophys. Res. 113, D16122, doi:10.1029/2008/JD009916.
Urbanization Influence
• Homogeneity testing may not remove all urban
affected sites if all sites are similarly
affected by urban growth
• Approach to assess residual effect is to
develop a dataset of rural-only stations.
• Grid the rural-only stations and then compare
with the grid with all the stations
• The issue here is assessment of the effect on
monthly and annual average temperatures –
not the effect on a single day
Urbanization Examples
• Few studies have looked at urbanization
studies across large areas of the world
• Major studies are Jones et al. (1990), Parker
(2004, 2006) and Peterson and Owen (2005) –
references in Chapter 3 of AR4
• Basic conclusion is that any residual warming
is an order of magnitude less than the
warming that has occurred over the last 100
years
• Effect can be large in rapidly developing
areas like China – but even here the largescale warming is 1.6 times greater (Jones et
al. 2008).
London
UHI greater
for Tn than Tx.
Central London
sites always
warmest at
night, but
warmer during
day west of
London
London has an Urban Heat Island (UHI), but no urban-related warming
since at least 1900. In other words, the centre got warmer earlier.
Questions that began to be asked (4)
• What causes the temperatures to change so
much from year to year?
• El Niño and La Niña (ENSO) explain some of the highfrequency variability
• Volcanoes cause cooling
• Circulation change can cause warming and cooling (e.g.
COWL patterns)
• Possible to factor out these influences
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Jones, P.D., 1989: The influence of ENSO on global temperatures. Climate
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Thompson. D.W.J., Wallace, J.M., Jones, P.D. and Kennedy, J.J., 2009: Removing
the signatures of known climate variability from global-mean surface
temperature: Methodology and Insights. J. Climate (tentatively accepted).
Monitor 17, 80-89
Questions that began to be asked (5)
• There are sites moves and/or urbanization influences
at the nearby site (to where this talk is being given)
• You’ve very few sites in Africa and South America
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Few seem to follow the logic of the Neff argument and think that one
site can influence the global average
Seem to accept the limited number when it comes to proxy data, but
not when it is instrumental data !
Developing these series (or regional ones) brings so many insights, which
are clearly apparent in examples
Different observation times (and methods of calculating mean T) in
different countries don’t matter, as long as they don’t get changed
A few outliers don’t matter. I once had some 100°C anomalies for a few
boxes in Siberia, but the effect on NH averages was a few hundredths
Combining the wrong month’s station data with a different month’s
absolute temperatures produces far greater differences (as GISS
realised last year!)
Issues Today
• SST measurement
• Exposure of thermometers in Europe in
summer in the mid-19th century
• Homogeneity of daily temperature
series – easiest to use the monthly
adjusted series, and change the daily
accordingly (not addressed here)
What does the corrected
series look like?
Globalaverage SST
anomaly (°C)
wrt 19611990
HadSST2
HadSST2(no
(nocorrections
correctionsafter
after1941)
1941)
Uncorrected data
Uncorrected data
Other Marine (SST) problems
• The various measuring platforms (ships, buoys,
satellites etc) have slightly different biases
• SSTs in the 1940s (Thompson et al., 2008)
• Recent SSTs (buoys slightly cooler than ships)
• Neither of these two adjusted for yet
• Issue is the same in all cases – instruments improved
(response time, better sampling etc.) but no overlap
measurements made beforehand. The bias gets
sorted out in retrospect, when there is enough data
available to clearly show the problem
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Thompson, D.W.J., Kennedy, J.J., Wallace, J.M. and Jones, P.D., 2008: A large
discontinuity in the mid-twentieth century in observed global-mean surface
temperature. Nature 453, 646-649.
Huge change in marine observing
network in the past 25 years
Percentage of
observations
coming from
DRIFTERS
and
SHIPS
What will the corrected
series look like?
Globalaverage SST
anomaly (°C)
wrt 19611990
New Corrections
HadSST2 (no corrections after 1941)
Uncorrected data
Early exposure issues
• Europe affected, before the development of
Stevenson screens
• Solution has come about from modern parallel
measurements (in Austria and Spain, with the
old screens)
• Effect is annually ~0.4°C, with most series
too warm by up to 0.7°C in June
• Surprisingly (for Austria), the effect is much
smaller using the (Tx+Tn)/2 method of
calculating averages than using fixed hours
• Issue important as it is the summers that
calibrate the natural and documentary proxies
Kremsmünster - Austria
Kremsmünster - Austria
When built in the 1770s, this monastery was the tallest in Europe for the time
Kremsmünster – historic minus
modern diurnal cycles
NNE exposure
JAN
APR
°C
FEB
MAY
MAR
JUN
°C
2.5
2.5
2.0
2.0
1.5
1.5
1.0
1.0
0.5
0.5
0.0
0.0
-0.5
-0.5
-1.0
-1.0
1
3
5
7
9 11 13 15 17 19 21 23
time (MLT)
JUL
OCT
1
3
5
7
AUG
NOV
9
SEP
DEC
11 13 15 17 19 21 23
time (MLT)
Work undertaken by Reinhard Böhm et al (ZAMG, accepted Climatic Change)
8 complete years of overlap between the ‘window’ and the modern Austrian observing site
Effect through different formulae
used to calculate monthly mean T
true mean (n=24)
(t6+t13+t20)/3
°C
(t7+t14+2*t21)/4
(tx+tn)/2
1.2
1.0
NNE-facing wall
(measured)
0.8
0.6
0.4
0.2
0.0
-0.2
DEC
NOV
OCT
SEP
AUG
JUL
JUN
MAY
APR
MAR
FEB
JAN
-0.4
Different exposures – NNW
(left) and N (right)
JAN
APR
°C
FEB
MAY
MAR
JUN
°C
2.5
2.5
2.0
2.0
1.5
1.5
1.0
1.0
0.5
0.5
0.0
0.0
-0.5
-0.5
-1.0
-1.0
1
3
5
7
9
11 13 15 17 19 21 23
time (MLT)
JAN
APR
1
3
5
7
FEB
MAY
9
MAR
JUN
11 13 15 17 19 21 23
time (MLT)
Adjustment across all 32 sites in
the Greater Alpine Region (GAR)
EIP minus non EIP (K)
0.5
0.0
-0.5
DEC
NOV
OCT
SEP
AUG
JUL
JUN
MAY
APR
MAR
FEB
JAN
-1.0
GAR is 4-19°E by 43-49°N
To apply adjustments need to know the NW-to-NE direction each site faced
Effect of changes across the GAR
Thin is raw data. Grey is after adjustment for site moves and observation
time changes. Thick shows the effect of exposure issues (thick black
overlies grey since about 1860).
0.5
1.5
WINTER HALF YEAR (ONDJFM)
anomalies to 1978-2007 (K)
anomalies to 1978-2007 (K)
SUMMER HALF YEAR
1.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0
1750
1800
1850
1900
1950
2000
-1.0
-1.5
-2.0
-2.5
-3.0
1750
1800
1850
1900
1950
2000
YEAR (Jan-Dec)
anomalies to 1978-2007 (K)
SUMMER- minus WINTER-HALfYEARS
anomalies to 1978-2007 (K)
-0.5
1.5
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
1750
0.0
1800
1850
1900
1950
2000
1.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0
1750
1800
1850
1900
1950
2000
Conclusions
• Individual station homogeneity assessment has
little effect on global averages, but is vital for
local- and regional-scale homogeneity
• Biases (buckets and some urbanization) are what is
important for homogeneity (particularly the way
SSTs have been measured)
• Problems in the 1945-55 will be adjusted for as
well as issues of the differences between ships
and drifters today (Effect will to delay the postWW2 cooling to the mid-1950s and will slightly
raise temperatures over the last 10 years)
• Exposure issues with pre-screen measurements
addressed in Europe (Effect is to cool summer
temperatures by about 0.5°C before about 1860)
• 2001-2008 0.18°C warmer than 1991-2000, which
was 0.14°C warmer than 1981-90