ArticlePDF Available

Abstract and Figures

The International Surface Pressure Databank (ISPD) is the world's largest collection of global surface and sea-level pressure observations. It was developed by extracting observations from established international archives, through international cooperation with data recovery facilitated by the Atmospheric Circulation Reconstructions over the Earth (ACRE) initiative, and directly by contributing universities, organizations, and countries. The dataset period is currently 1768-2012 and consists of three data components: observations from land stations, marine observing systems, and tropical cyclone best track pressure reports. Version 2 of the ISPD (ISPDv2) was created to be observational input for the Twentieth Century Reanalysis Project (20CR) and contains the quality control and assimilation feedback metadata from the 20CR. Since then, it has been used for various general climate and weather studies, and an updated version 3 (ISPDv3) has been used in the ERA-20C reanalysis in connection with the European Reanalysis of Global Climate Observations project (ERA-CLIM). The focus of this paper is on the ISPDv2 and the inclusion of the 20CR feedback metadata. The Research Data Archive at the National Center for Atmospheric Research provides data collection and access for the ISPDv2, and will provide access to future versions.
Content may be subject to copyright.
The International Surface Pressure
Databank version 2
Thomas A. Cram
1
*, Gilbert P. Compo
2,3
, Xungang Yin
4
, Robert J. Allan
5,6
, Chesley McColl
2,3
, Russell
S. Vose
7
, Jeffrey S. Whitaker
3
, Nobuki Matsui
2,3
, Linden Ashcroft
8,
, Renate Auchmann
9
, Pierre
Bessemoulin
10
, Theo Brandsma
11
, Philip Brohan
6
, Manola Brunet
12,17
, Joseph Comeaux
1
, Richard
Crouthamel
13
, Byron E. Gleason Jr
7
, Pavel Y. Groisman
7,14,15
, Hans Hersbach
16
, Philip D. Jones
17,18
,
Trausti J
onsson
19
, Sylvie Jourdain
20
, Gail Kelly
5
, Kenneth R. Knapp
7
, Andries Kruger
21
, Hisayuki
Kubota
22
, Gianluca Lentini
23,
, Andrew Lorrey
24
, Neal Lott
7
, Sandra J. Lubker
3
,J
urg Luterbacher
25
,
Gareth J. Marshall
26
, Maurizio Maugeri
23
, Cary J. Mock
27
, Hing Y. Mok
28
, Øyvind Nordli
29
, Mark J.
Rodwell
6,§
, Thomas F. Ross
7
, Douglas Schuster
1
, Lidija Srnec
30
, Maria Ant
onia Valente
31
, Zsuzsanna
Vizi
32,
, Xiaolan L. Wang
33
, Nancy Westcott
34
, John S. Woollen
35,36
and Steven J. Worley
1
1
National Center for Atmospheric Research, Boulder, CO, USA
2
University of Colorado, CIRES, Boulder, CO, USA
3
NOAA Earth System Research Laboratory, Physical Sciences Division, Boulder, CO, USA
4
ERT, Inc., Asheville, NC, USA
5
Atmospheric Circulation Reconstructions over the Earth Initiative, Met Ofce Hadley Centre, Exeter, UK
6
Met Ofce Hadley Centre, Exeter, UK
7
NOAA National Centers for Environmental Information, Asheville, NC, USA
8
School of Earth Sciences, University of Melbourne, Melbourne, Vic., Australia
9
Institute of Geography and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
10
M
et
eo-France, Toulouse, France
11
Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands
12
Centre for Climate Change, Universitat Rovira i Virgili, Tarragona, Spain
13
International Environmental Data Rescue Organization (IEDRO), Deale, MD, USA
14
University Corporation for Atmospheric Research, Boulder, CO, USA
15
P. P. Shirshov Institute of Oceanology, Russian Academy of Sciences, Moscow, Russia
16
European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
17
Climatic Research Unit, University of East Anglia, Norwich, UK
18
Center of Excellence for Climate Change Research/Department of Meteorology, King Abdulaziz University, Jeddah,
Saudi Arabia
19
Icelandic Met Ofce, Reykjavik, Iceland
20
M
et
eo-France, Direction de la Climatologie, Toulouse, France
21
South African Weather Service, Pretoria, South Africa
22
Department of Coupled OceanAtmosphereLand Processes Research, Japan Agency for MarineEarth Science and
Technology, Yokosuka, Japan
23
Dipartimento di Fisica, Universit
a degli Studi di Milano, Milan, Italy
24
National Institute of Water and Atmospheric Research (NIWA), Auckland, New Zealand
25
Department of Geography, Climatology, Climate Dynamics and Climate Change, Justus Liebig University Giessen, Giessen,
Germany
26
British Antarctic Survey, Natural Environment Research Council, Cambridge, UK
27
Department of Geography, University of South Carolina, Columbia, SC, USA
28
Hong Kong Observatory, Hong Kong, China
29
Norwegian Meteorological Institute, Oslo, Norway
30
Meteorological and Hydrological Service, Zagreb, Croatia
31
Instituto Dom Luiz, Laborat
orio Associado, Universidade de Lisboa, Lisbon, Portugal,
32
Nicolaus Copernicus University, Toru
n, Poland
33
Climate Research Division, Science and Technology Branch, Environment Canada, Toronto, ON, Canada
34
Midwestern Regional Climate Center, Illinois State Water Survey, University of Illinois at UrbanaChampaign, Urbana, IL, USA,
35
I.M. Systems Group, Inc., Rockville, MD, USA
36
NOAA National Centers for Environmental Prediction, Environmental Modeling Center, Camp Springs, MD, USA
*Correspondence: Thomas Cram, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000, USA,
E-mail: tcram@ucar.edu
ª2015 The Authors. Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and
reproduction in any medium, provided the original work is properly cited.
Current address: Centre for Climate Change, Universitat Rovira i Virgili, Tarragona, Spain
Current address: Ente Regionale per i Servizi allAgricoltura e alle Foreste (ERSAF), Milan, Italy
§Current address: European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
Current address: Department of Space and Climate Physics, University College London, London, UK
The International Surface Pressure Databank (ISPD) is the worlds largest collection of global surface and sea-level pressure
observations. It was developed by extracting observations from established international archives, through international coopera-
tion with data recovery facilitated by the Atmospheric Circulation Reconstructions over the Earth (ACRE) initiative, and directly by
contributing universities, organizations, and countries. The dataset period is currently 17682012 and consists of three data com-
ponents: observations from land stations, marine observing systems, and tropical cyclone best track pressure reports. Version 2
of the ISPD (ISPDv2) was created to be observational input for the Twentieth Century Reanalysis Project (20CR) and contains the
quality control and assimilation feedback metadata from the 20CR. Since then, it has been used for various general climate and
weather studies, and an updated version 3 (ISPDv3) has been used in the ERA-20C reanalysis in connection with the European
Reanalysis of Global Climate Observations project (ERA-CLIM). The focus of this paper is on the ISPDv2 and the inclusion of the
20CR feedback metadata. The Research Data Archive at the National Center for Atmospheric Research provides data collection
and access for the ISPDv2, and will provide access to future versions.
Geosci. Data J. 2:3146 (2015), doi: 10.1002/gdj3.25
Received: 15 May 2014, revised: 3 June 2015, accepted: 8 June 2015
Key words: surface pressure, sea level pressure, observations, stations, ships
Dataset
Identier: doi:http://dx.doi.org/10.5065/D6SQ8XDW
Creators: Compo, G. P., J. S. Whitaker, P. D. Sardeshmukh, N. Matsui, R. J. Allan, X. Yin, B. E. Gleason, R. S. Vose, G. Rutledge, P.
Bessemoulin, S. Br
onnimann, M. Brunet, R. I. Crouthamel, A. N. Grant, P. Y. Groisman, P. D. Jones, M. C. Kruk, A. C. Kruger, G. J.
Marshall, M. Maugeri, H. Y. Mok, O. Nordli, T. F. Ross, R. M. Trigo, X. L. Wang, S. D. Woodruff and S. J. Worley
Title:International Surface Pressure Databank (ISPDv2)
Publisher: Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Labo-
ratory, Boulder, Colorado, USA
Publication year: 2010
Resource type: Dataset
Version: 2.0
Introduction
The International Surface Pressure Databank (ISPD) is
the worlds largest collection of global surface and
sea-level pressure observations. Since its inception in
2002, its development has been facilitated by coopera-
tive efforts between the Global Climate Observing Sys-
tem (GCOS)/World Climate Research Program (WCRP)
Working Group on Observational Data Sets for Reanal-
ysis [2007 to 2011], and the ongoing efforts of the
GCOS Working Group on Surface Pressure and the
international Atmospheric Circulation Reconstructions
over the Earth initiative (ACRE, Allan et al., 2011).
The full observational record is extracted and assem-
bled from established international archives and newly
available collections from more than 60 different con-
tributing organizations, shown in Tables 1 and 2.
The ISPD version 2 (ISPDv2) covers the period
17682012. It merges data from three input compo-
nents: observations from land stations, marine observ-
ing systems, and tropical cyclone best track pressure
reports. The land station component was extracted
from many national and international collections of
sea-level pressure and surface pressure. The largest
contributor to this component was the Integrated Sur-
face Database (Smith et al., 2011), which consists of
global hourly and synoptic surface observations col-
lected from many sources. The land stations were
merged following a two-step algorithm to rst remove
duplicates within a collection and then remove dupli-
cates between collections (Yin et al., 2008).
The marine component consists of sea-level pressure
observations extracted from the International Compre-
hensive OceanAtmosphere Data Set (ICOADS, Woo-
druff et al., 1998; Parker et al., 2004; Woodruff et al.,
2005; Worley et al., 2005; Woodruff et al., 2011), a
global ocean marine meteorological and surface ocean
dataset that is considered the most complete of its kind.
It is comprised of measurements and observations
extracted from many international data sources, includ-
ing ship reports, moored and drifting buoys, coastal sta-
tions, and other marine platforms. ICOADS release 2.4
was used in the ISPDv2 for the period 19522008 and
ICOADS release 2.5 (Woodruff et al., 2011) was used
for the period 17851951 and 20092012.
The tropical cyclone component was taken from the
International Best Track Archive for Climate Steward-
ship (IBTrACS; Knapp et al., 2010). The IBTrACS data-
set consists of global tropical cyclone best track
position and intensity observations, and reports col-
lected from each of the World Meteorological Organi-
zation (WMO) Regional Specialized Meteorological
32 T. A. Cram et al.
ª2015 The Authors.
Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd.Geoscience Data Journal 2:3146 (2015)
Centers, Tropical Cyclone Warning Centers, and other
national agencies. The IBTrACS Beta version was used
for the years 19522006, version v01r01 for 1871
1883 and 18861951, version v02r01 for 18841885
and 20072008, version v03r02 for 20092010, and
version v03r05 for 20112012.
The inclusion of various versions of both ICOADS
and IBTrACS in ISPDv2 was the result of using the
most up-to-date data that were available during the
assimilation into the Twentieth Century Reanalysis
(20CR; Compo et al., 2011). This rationale is elabo-
rated upon in Section 2.1.
Table 1. Contributing organizations to the International
Surface Pressure Databank.
1. All-Russian Research Institute of Hydrometeorological
Information World Data Center
a
2. Atmospheric Circulation Reconstructions over the
Earth (ACRE)
a
3. Australian Bureau of Meteorology
a
4. Australian Meteorological Association, Todd Project
team
5. British Antarctic Survey
a
6. Cook Islands Meteorological Service
7. Danish Meteorological Institute
a
8. Deutscher Wetterdienst (DWD)
a
9. Environment Canada, Climate Research Division
a
10. ETH Zurich, Switzerland
a
11. European and North Atlantic Daily to Multidecadal
Climate Variability (EMULATE)
a
12. EUropean Reanalysis and Observations for Monitoring
(EURO4M)/The WMO MEditerranean DAta REscue
Initiative (MEDARE)
13. European Reanalysis of Global Climate Observations
(ERA-CLIM)
14. GCOS Atmospheric Observation and Ocean
Observation Panels for Climate Working Group on
Surface Pressure
a
15. GCOS/WCRP Working Group on Observational Data
Sets for Reanalysis
a
16. Hong Kong Observatory
a
17. Icelandic Meteorological Ofce (IMO)
18. Instituto Geosico da Universidade do Porto,
Portugal
a
19. International Best Track Archive for Climate
Stewardship (IBTrACS)
a
20. International Comprehensive OceanAtmosphere
Data Set (ICOADS)
a
21. International Environmental Data Rescue
Organization (IEDRO)
a
22. Japan Agency for Marine-Earth Science and
Technology (JAMSTEC)
23. Japan Meteorological Agency
a
24. Jersey Met Department
a
25. Koninklijk Nederlands Meteorologisch Instituut (KNMI;
Royal Netherlands Meteorological Institute)
a
26. Lamont-Doherty Earth Observatory of Columbia
University
27. McGill University, Canada
28. Met Ofce Hadley Centre, UK
a
29. M
et
eo-France
a
30. M
et
eo-France Division of Climate
31. Meteorological and Hydrological Service, Croatia
a
32. National Center for Atmospheric Research (NCAR),
USA
a
33. National Institute for Water and Atmospheric
Research (NIWA), New Zealand
34. Nicolaus Copernicus University Department of
Meteorology and Climatology, Poland
35. Niue Meteorological Service
36. NOAA Climate Database Modernization Program
(CDMP), USA
a
(continued)
37. NOAA Earth System Research Laboratory (ESRL),
Physical Sciences Division, USA
a
38. NOAA Midwest Regional Climate Center at UIUC,
USA
a
39. NOAA National Centers for Environmental Prediction
(NCEP), USA
a
40. NOAA National Climatic Data Center (NCDC), USA
a
41. NOAA Northeast Regional Climate Center at Cornell
University, USA
a
42. NOAA Pacic Marine Environmental Laboratory, USA
43. Norwegian Meteorological Institute
a
44. Ohio State University Byrd Polar Research Center,
USA
a
45. Oldweather.org
46. Portuguese Institute of Sea and Atmosphere (IPMA),
Portugal
47. Proudman Oceanographic Laboratory, UK
a
48. Signatures of environmental change in the
observations of the Geophysical Institutes (SIGN)
a
49. South African Weather Service
a
50. South Eastern Australian Recent Climate History
(SEARCH) project, The University of Melbourne
51. Tanzania Meteorological Agency
52. University of Abderdeen, Scotland, UK
53. University of Bern, Switzerland
54. University of Colorado Climate Diagnostics Center
(CDC) of the Cooperative Institute for Research in
Environmental Sciences (CIRES)
a
55. University of East Anglia Climatic Research Unit,
UK
a
56. University of Giessen Department of Geography,
Germany
57. University of Lisbon Instituto Dom Luiz, Portugal
a
58. University of Milan Department of Physics, Italy
a
59. University of Porto-Instituto Geosico, Portugal
a
60. University Rovira i Virgili Centre for Climate Change
(C3), Spain
a
61. University of South Carolina, USA
62. University of Toronto Department of Physics,
Canada
63. University of Washington, USA
64. World Meteorological Organization MEditerranean
climate DAta REscue (MEDARE)
65. Zentralanstalt f
ur Meteorologie und Geodynamik
(ZAMG; Austrian Weather Service)
a
a
Contributors to version 2 (cf. Compo et al., 2011, Table II).
Table 1. (continued)
The international surface pressure databank 33
ª2015 The Authors.
Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd. Geoscience Data Journal 2:3146 (2015)
Table 2. ISPDv2 dataset collection identiers and names.
ISPD ID Name Description Period No. stations/records
NCDC
reference NCAR reference Reference publication
0100 ICOADS Release 2.1 Global Marine Surface Observations 17842005 /~185 million DSI-1170 ds540.0, ds540.1 Parker et al. (2004),
Woodruff et al. (1998, 2005),
Worley et al. (2005)
0104 ICOADS Release 2.4 Global Marine Surface Observations 17842007 /~238 million DSI-1170 ds540.0, ds540.1 Parker et al. (2004),
Woodruff et al. (1998, 2005),
Worley et al. (2005)
0105 ICOADS Release 2.5 Global Marine Surface Observations 17842007 /~261 million DSI-1173 ds540.0, ds540.1 Woodruff et al. (2011)
0200 ICOADS Auxiliary Kobe Global Marine Surface Observations 18891943 /3135 ds530.0
0300 ICOADS Auxiliary Whaling Global Marine Surface Observations 19501984 /20 ds530.0
0400 ICOADS Auxiliary Russian Global Marine Surface Observations 19502000 /1789 ds530.0
0500 ICOADS Auxiliary Russian Global Marine Surface Observations 18892000 /7873 ds530.0
0700 ICOADS Auxiliary Challenger Global Marine Surface Observations 18721876 (n/a) ds530.0
1000 Federal Climate Complex
Integrated Surface Database
Global Land Surface Observations 19012008 23 363/~1.2 billion DSI-3505 ds463.3 Smith et al. (2011)
1002 CDMP SAO/1001 Forms US Land Surface Observations 19281948 27/236 886 DSI-3851 Dupigny-Giroux et al. (2007)
1003 Russian Empire Stations Russian Land Surface Observations 18492000 1860/87 795 180 DSI-9290c
1004 Air Weather Service TD13 Global Land Surface Observations 19011973 32/55 748 ds467.0
1005 Hadley Centre Individual Stations from
Hadley Centre
1833present 11/520 594 Allan et al. (2011)
1006 CDMP-International Chile, Mexico, Uruguay 1800s1980 14/326 175 Dupigny-Giroux et al. (2007)
1007 READER Antarctic & Southern
Hemisphere
20 Stations via British
Antarctic Survey
19472007 18/562 784
1010 U.S. Air Force DATSAV US Air Force Compilation 19761980 DSI-9685 ds463.0
1011 Royal Netherlands Meteorological
Institute (KNMI)
KNMI stations 19112006 3/1 045 250
1012 CDMP Forts US Army Signal Service and
other 19
th
Century
Voluntary Observations
18411893 49/629 491 DSI-3297 Dupigny-Giroux et al. (2007)
2000 NCEP-NCAR BUFR Archive Global Observations 19482003 6625/26 225 199 ds090.0 Kalnay et al. (1996),
Kistler et al. (2001)
2001 NCEP Operational BUFR Archive Global Observations 19281948,
20032005
1053/2 940 516 ds090.0
3002 WASA Stations Observations
Sea Level Pressure
Northern Europe, Greenland 18711996 20/2 181 628 Schmith et al. (1997, 1998)
3004 Environmental Canada
Pressure Observations
Canadian Stations 18422004 746/25 277 856
3005 West African Synoptic observations West African Land Surface 18501980 123/3 022 702
3006 The Australian Bureau of
Meteorology Station
Pressure Dataset
Australian Land stations 19001956 63/1 418 093
(continued)
34 T. A. Cram et al.
ª2015 The Authors.
Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd.Geoscience Data Journal 2:3146 (2015)
Table 2. (continued)
ISPD ID Name Description Period No. stations/records
NCDC
reference NCAR reference Reference publication
3007 Northern Italian Pressure
Observations
18781940 1/62 253 Maugeri et al. (2004, 2008)
3008 Brazil Surface Observations 19511980 8/78 ds486.0
3009 Spanish Hourly Pressure
Observations from EMULATE
Hourly Spanish Land Stations 18502003 5/190 899 Ansell et al. (2006)
3010 Germany Deutscher Wetterdienst (DWD)
Web Archive
18762000 9/405 167
3011 Austria Emulate Stations 18722002 9/467 124 Ansell et al. (2006)
3012 Switzerland Emulate Stations 19001973 8/815 363 Ansell et al. (2006)
3013 South Africa South African Weather
Service Stations
18502003 187/2 636 479
3014 Norway 18632007 16/225 687
3015 Croatia Meteorological
and Hydrological
Service Land Stations
Land Stations 18582005 4/444 398
3016 Portugal Portuguese SIGN stations 18602006 2/151 989 Valente et al. (2008)
4000 Hong Kong Hourly
Pressure Observations
Hong Kong Observatory 18851939 1/430 920
4001 Jakarta/Batavia
Pressure Observations
Dutch Royal Observatory 18661944 1/604 518 ds490.0
4002 Liverpool Proudman Ocean. Lab stations 17681793 1/9094
4003 Jersey Channel Island Pressure
Observations
Channel Island Stations 18641913 7/159 781
4004 CMDP-USNO US Naval Observatory
at Washington
18411913 1/83 261 Dupigny-Giroux et al. (2007)
5000 Antarctic Expedition keyed
by Hadley Centre
18991941 11/119 991 Allan et al. (2011)
5002 Byrd Antarctic Expeditions
Observations
Monthly Weather Review
Supplemental No. 41
19291930
8000 Atlantic/North Eastern Pacic
Hurricane Reanalysis
US Hurricane ReAnalysis Dataset 1848present
8001 International Best Track Archive for
Climate Stewardship (IBTrACS)
National Climatic Data Center 1848present DSI-9637 Knapp et al. (2010)
010000019999 NCAR Upper Air Stations National Center for
Atmospheric Research
19431998 1081/1 275 272 ds370.1
The columns denote, respectively, the ISPDv2 collection ID, collection name, description, yearly period of record, total number of stations and observational records in the collection, reference
number for the corresponding National Climatic Data Center (NCDC) dataset, reference number for the corresponding dataset archived in the Research Data Archive (RDA) at the National Center
for Atmospheric Research (NCAR), and reference publication.
*The Air Weather Service TD13 observational records originating from collection ID 1004 are incorrectly labeled with collection ID 2001 in the native HDF5 tables in both ISPDv2 and ISPD ver-
sion 3.
The international surface pressure databank 35
ª2015 The Authors.
Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd. Geoscience Data Journal 2:3146 (2015)
1. Dataset content and coverage
The ISPDv2 dataset is stored in the Hierarchical Data
Format version 5 (HDF5: http://www.hdfgroup.org),
which allows for efcient archiving of large, complex
data structures and a diverse set of metadata. Each
HDF5 le contains 13 tables and 15 directory subgroups
that dene how to decode the individual records, and
each observation record includes metadata dening the
data collection source and observation type (e.g. sta-
tion observation, marine observation, surface pressure
observation from a radiosonde sounding).
Because the ISPDv2 was assimilated as observa-
tional input into the 20CR, each record also includes
the 20CR quality control and assimilation feedback
metadata. These quality control and feedback meta-
data include the results of the ve-step quality control
procedure described in Appendix B of Compo et al.
(2011) and all the relevant statistical quantities
returned by the 20CR ensemble data assimilation sys-
tem. The format of the HDF5 metadata, therefore, is
designed to allow traceability of observations from
their original source to the ISPDv2, and to permit
direct feedback from the 20CR data assimilation back
to the original source archives.
Each observation at any single observation time is
assigned a unique number that when combined with
the time stamp (year, month, date, hour, and minute)
forms a unique identier within the complete dataset.
Thus, every observation has an unambiguous refer-
ence within the full ISPDv2 dataset, which allows for
precise identication and traceability. This data man-
agement feature is critical for accurately adding addi-
tional metadata from other future reanalyses, and
evolving the ISPDv2 to new versions while maintaining
the usage provenance of each record.
The 20CR required hourly les from the ISPDv2 data
for the data assimilation system, thus each data le in
the ISPDv2 collection contains one hour of global obser-
vations. Based on this le organization and data cover-
age, a maximum of 24 data les per day and 8760 data
les per (non-leap) year exist in the ISPDv2 collection.
The total dataset volume is 491.32 GB. The HDF5 data
are available for download as either individual hourly
data les, or as yearly tar les, which are tarballs of one
entire year of HDF5 data les.
The ISPDv2 is also available in NetCDF and ASCII col-
umn data formats, both of which are derived from the
native HDF5 format. The data content within these two
formats is not a complete reproduction of the tabular
table content and metadata that are contained within
the HDF5 data. It does contain, however, essential
information, including the 20CR quality control feed-
back information, for most users to carry out their
research projects. The ASCII data are available as
monthly tar les for direct download from the National
Center for Atmospheric Research (NCAR) Research
Data Archive (RDA) website (http://dx.doi.org/
10.5065/D6SQ8XDW), while the NetCDF data are pro-
duced on demand through the data subsetting service
described in Section 3.1.
The data availability varies considerably over the
period of record. Maps of annual land station distribu-
tion (Figure 1) highlight the steady growth in the
number of reporting stations, and hence data cover-
age, since the year 1850 (station coverage maps for
every year are available at http://www.esrl.noaa.gov/
psd/data/ISPD/v2.0/). The sources of land station
Figure 1. Annual distribution of ISPDv2 land station locations for the years 1850, 1900, 1950, and 2000. Station coverage maps
for every year in the ISPDv2 are available at http://www.esrl.noaa.gov/psd/data/ISPD/v2.0/.
36 T. A. Cram et al.
ª2015 The Authors.
Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd.Geoscience Data Journal 2:3146 (2015)
observations in the 19th and early 20th centuries are
located primarily in Europe and North America. The
data coverage increases dramatically across most of
the rest of the globe from 1950 to the present.
Figure 2 shows time series of the total number of
land observing stations in each indicated continental
region by year from 1850 to 2010. The notable decline
in stations during the mid-1960s primarily in the
Asia/Eastern Europe time series is attributed to the
reduction in the Integrated Surface Database records
during this period, and is explained in Smith et al.
(2011) as the result of the transition from keying of
observational data to digital transmission and receipt
of data.
The Asia time series also exhibits a considerable
decline in station coverage starting in the mid-1980s.
A listing of the number of stations originating from the
Integrated Surface Database for the years 19631964
and selected years between 1980 and 2001 is shown
in Table 3, which reveals these reductions are primar-
ily associated with the observations originating from
the Russian Federation and other former Soviet Union
countries. These reductions can be explained by the
deterioration of funding of the meteorological observa-
tion network in the nal years of the Soviet Union
existence and thereafter in the 1990s and 2000s. The
observation network health during this post-Soviet
period was a function of the economic situation in
each of the 15 newly independent nations (and, to
some extent, a function of foreign sponsorship). The
China, North and Central America, and South-West
Pacic regions also exhibit notable decreases between
1963 and 1964.
The average number of pressure observations per
day contained in the 20CR feedback records from all
ISPDv2 components (station, marine, and tropical
cyclone) is illustrated by maps for the years 1900,
1950, and 2000 in Figure 3. The preponderance of
observations in the Northern Hemisphere in earlier
years is evident, as is the growth of marine observa-
tions over time (see Woodruff et al., 2011 for a com-
plete description).
The total number of observations per year in the
ISPDv2 increases steadily from approximately 100 000
records in 1870 to 53 million in 2010 (Figure 4). A
local increase and subsequent decrease in this trend
during the 18781894 period is attributed to the addi-
tion of 1.8 million records from the US Marine Meteo-
rological Journals and digitized for ICOADS Release
2.5 (Woodruff et al., 2011). A decrease during the
World War I years is evident, while an increase during
the World War II years is attributed to the inclusion of
1.6 million ICOADS records digitized from the UK
Royal Navy Shipslogbooks (Brohan et al., 2009;
Woodruff et al., 2011). A decrease during the mid
1960s is associated with the aforementioned decline in
station records in the Integrated Surface Database
(Smith et al., 2011). A decrease in 2007 is most likely
due to a delay in processing, and therefore incomplete
records, in the Integrated Surface Database. Other
small-scale variations in the curve likely can be corre-
lated with increased scientic interest (local peaks)
and research budget reductions (local troughs; Flem-
ing, 2000).
The dataset contains reports from more than
10 000 land stations, which, in addition to the marine
observing systems reports, comprise over 1.5 billion
observations in total. Between 1860 and 1917, the
total number of marine and surface station observa-
tions is comparable, but in later years the total count
Figure 2. Time series of the total number of ISPDv2 land observing stations in each indicated continental region by year from
1850 to 2010. The inset panel indicates the boundaries used for the continental regions.
The international surface pressure databank 37
ª2015 The Authors.
Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd. Geoscience Data Journal 2:3146 (2015)
is dominated by land surface station reports (not
shown). This prevalence of land station observations is
primarily due to the input data compiled for the
National Centers for Environmental Prediction (NCEP)/
NCAR Reanalysis project (19482003; Kalnay et al.,
1996; Kistler et al., 2001).
2. Data usage and application
2.1. Reanalysis
The primary motivation for the creation and develop-
ment of the ISPD was to facilitate progress on research
and understanding of long-term trends and variations
in global surface pressure. In particular, a long-term
historical dataset, such as the ISPD, becomes essential
to providing an observational underpinning to retro-
spective climate analysis datasets, commonly known as
reanalyses. Reanalysis products are used extensively in
climate research, applications, and services, including
for monitoring and comparing current climate condi-
Table 3. Total number of stations originating from the Integrated Surface Database and included in ISPDv2 for selected years
between 1963 and 2001.
WMO region 1963 1964 1980 1990 1993 1995 1998 2001
0 47 28 877 1007 925 927 869 1058
1 184 166 717 813 740 748 777 935
2 914 223 800 695 683 577 410 482
3 795 124 1031 878 835 751 607 584
4 466 353 916 932 864 876 867 1049
5 518 347 727 598 592 587 558 638
6 211 222 689 768 758 776 825 887
7 484 280 1524 1594 1671 1697 1560 1770
8 132 132 550 695 661 684 630 671
9 689 626 578 865 874 898 860 841
The columns represent the station count for the years listed and organized by WMO regional block number (0 and 1 =Europe; 2 and
3=Russian Federation and other former Soviet Union countries; 4 =Asia; 5 =China; 6 =Africa; 7 =North America, Central America, and
the Caribbean; 8 =South America and Antarctica; and 9 =South-West Pacic).
Figure 3. Maps showing the average number of pressure
observations per day contained in the 20CRv2 feedback
records from all ISPDv2 components: station, marine, and
tropical cyclone for the years 1900 (top), 1950 (middle), and
2000 (bottom). Counts of observations are made in ve-de-
gree grid boxes.
Figure 4. Time series of the number of pressure observa-
tions per year in version 2 of the International Surface Pres-
sure Databank (ISPDv2) from 1870 to 2010. Note the
logarithmic scale along the y-axis. Inset panel: time series
during the same period showing the number of observations
in the Northern Hemisphere (blue curve) and Southern
Hemisphere (red curve).
38 T. A. Cram et al.
ª2015 The Authors.
Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd.Geoscience Data Journal 2:3146 (2015)
tions with those of the past, identifying the causes of
climate variations and change, and preparing climate
predictions. Information derived from reanalyses is also
being used increasingly in commercial and business
applications in sectors such as energy, agriculture,
water resources, and insurance.
Prior to completion of the ISPD, most reanalysis
products (including those from the NCEP/NCAR Reanal-
ysis Project (Kalnay et al., 1996; Kistler et al., 2001)
and the European Centre for Medium-Range Weather
Forecasts [ECMWF; Uppala et al., 2005]) could only
extend back to about 1950 since they relied on assimi-
lating a set of observations that included upper-level
atmospheric information. The primary input data source
for the NCEP/NCAR Reanalysis, for example, are the
global rawinsonde observations, which contain a period
of record substantial enough to produce that reanalysis
back to 1948. The limited time range of these reanaly-
ses restricts their usefulness for many climate research
applications. The ISPD adds value by providing a much
longer period of record and thus enables the develop-
ment of reanalyses with longer time spans.
The rst reanalysis to make use of the ISPD is the
20CR Project (Compo et al., 2011), which assimilates
only the ISPDv2 surface and sea-level pressure
observations and prescribes observed monthly sea
surface temperature and seaice distributions from
HadISST1.1 (Rayner et al., 2003) as boundary condi-
tions. This global reanalysis dataset spans the late
19th century, the entire 20th century, and the early
21st century (18712012). It estimates the state of
the atmosphere the analysisby combining the
hourly and synoptic ISPDv2 observations in a 6-h time
window with a dynamically generated 9-h rst guess
forecast initialized from the previous analysis. Cycling
this procedure with overlapping 6-h analyses and 9-h
forecasts has the effect of spreading the observational
information from the ISPD three-dimensionally in
space and in time.
As the 20CR was produced, the early period ISPDv2
betaversion was revised to include newly digitized
additions and improve the overall coverage of stations.
As ICOADS Release 2.5 was then available, it was also
included to increase the number of marine observa-
tions in the early period. Similarly for IBTrACS, newly
digitized records from the most recent versions of
IBTrACS were added to ISPDv2 as it evolved and
improved over time. The nal ISPDv2, therefore, con-
tains multiple source versions of ICOADS and IBTrACS.
The newer versions of ICOADS and IBTrACS did not
change appreciably except to expand their data inputs
and coverage, therefore we do not anticipate that the
use of multiple versions introduces inconsistencies in
the pressure dataset.
2.2. Twentieth century reanalysis quality
control and data assimilation feedback
During the data assimilation processing of the 20CR,
the ISPDv2 input data were interrogated by a ve-step
quality control procedure that included, among other
steps, checking the observations for meteorological
plausibility, comparing them with neighbouring data
values, and performing a bias correction on the land
component (see Compo et al., 2011 for more informa-
tion on this procedure). The 20CR then applied an
Ensemble Kalman Filter data assimilation method
which used background rst-guess elds supplied by
an ensemble of 56 forecasts from an experimental
version of the NCEP Global Forecast System (GFS)
numerical weather prediction model (Kanamitsu et al.,
1991; Moorthi et al., 2001; Saha et al., 2006). These
quality control and data assimilation results, which
include the rst-guess elds, analysis departures, bias
estimates, and observation errors, were then written
back into the ISPDv2 so that each observational
record contains this feedback information. Future
users of the ISPDv2, and those who contributed the
original observational data, therefore can utilize this
information to make an informed decision on the qual-
ity and usefulness of each observational record during
the 20CR time period.
One project that utilized the 20CR feedback meta-
data is the ERA-20C reanalysis (Poli et al., 2013),
which is the rst reanalysis produced under the Euro-
pean Reanalysis of Global Climate Observations data-
base project (ERA-CLIM: www.era-clim.eu). The ERA-
20C is a global reanalysis that spans the 20th cen-
tury (19002010) assimilating only surface and sea-
level pressure observations from ISPD and ICOADS
and marine surface winds from ICOADS. Feedback
information from the 20CR was used in the bias cor-
rection scheme for ISPD data that were assimilated
in the ERA-20C reanalysis; this was based on a
break-point analysis using the 20CR rst-guess depar-
tures (Hersbach et al., 2015). In locations where
breakpoints were suspected (e.g. instances of instru-
ment errors or a change in station location), the
ERA-20C bias correction scheme assigned less con-
dence to the 20CR rst-guess value, and thus was
allowed to be more adaptive compared to cases
where no irregularities in long-term departures were
identied.
In another study, Wang et al. (2014) used the
20CR feedback metadata to show that the strong
extratropical cyclone events in the 20CR agree well
with the geostrophic wind extremes derived from
in situ pressure observations. This study illustrates
how the 20CR quality control procedure performed
well in identifying errors in the observational record.
For example, the 20CR quality control system identi-
ed and rejected 143 of 146 erroneous observation
values from the Aberdeen, Scotland records for the
period 18711921. This quality control feedback infor-
mation, therefore, is valuable in identifying observa-
tion errors which might otherwise be overlooked and,
coupled with continued data rescue efforts such as
those facilitated by ACRE (Allan et al., 2011), serves
to strengthen and reduce uncertainty in the observa-
tional record.
The international surface pressure databank 39
ª2015 The Authors.
Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd. Geoscience Data Journal 2:3146 (2015)
3. Data access and user services
3.1. Research data archive data location
and accessibility
The primary repository for ISPDv2 is in the RDA
(http://rda.ucar.edu) at NCAR in Boulder, Colorado.
The RDA is a free and open data collection where data
discovery can be achieved through faceted searches
based on Global Change Master Directory (GCMD:
http://gcmd.nasa.gov) metadata keywords, free text
queries, and lists highlighting the most used datasets.
Data access is free, but requires each user to register
through a simple online process that validates the sub-
mitted email address.
The ISPDv2 data access is organized by year and
month on the RDA website (http://dx.doi.org/
10.5065/D6SQ8XDW), and users may browse through
the pages to locate specic data of interest. Data les
are organized in default groups by observation year
and month. They can be downloaded directly from the
RDA web interface using server-supplied wgetscripts.
Users may select a collection of les to download from
default lists by using the Web File Listingoption
under the Data Accesstab.
A more rened option is provided through the data
subset request form, which may be accessed via the
Get a subsetlink under the Data Accesstab. This
produces a customized data subset of the HDF5 data
based on user-provided constraints. Users can specify
the temporal limits, spatial domain, observation type
(s), and ASCII or NetCDF data output formats through
the data subset request form (Figure 5). The output
le compression can also be requested on this form
(not shown). Observations from individual observing
stations may also be requested, which is a useful fea-
ture for users who wish to procure time series for
specic locations or regions. Once submitted, the data
subset requests are produced by a delayed mode data
processing procedure. Users are notied and directed
to a web download location when their data request
output les are accessible.
3.2. Dataset citation
The RDA also provides a data citation service. The
dataset homepage provides citation syntax in the stan-
dard forms recommended by the Federation of Earth
System Information Partners (ESIP), American Meteo-
rological Society (AMS), American Geophysical Union
(AGU), DataCite, and the Geoscience Data Journal.
This dataset citation is also available in Research
Information System (RIS) format so that users may
import the citation for this dataset directly into their
citation reference management software (e.g. End-
Note, Zotero, etc.). One key element in the dataset
citation is the data access date (Accessed dd mmm
yyyy). Leveraging the fact that RDA data users are
registered, a customized data citation can be prepared
for the user by using the Get a customized data cita-
tionlink on the dataset home page. The dates that
users access the data are recorded and can be
retrieved on demand at later times.
3.3. File content metadata
File content metadata are collected as part of the data
processing and preparation of the ISPDv2 for the
RDA. The metadata supports additional information
services for interested users. The overall ISPDv2 meta-
data summary is provided from the dataset home
page under the More Detailslink. Here, tabulations of
the observing station location, observation type, plat-
form identication, and maps of the global distribution
for the full ISPDv2 can be viewed (Figure 6).
In addition, users may view the content metadata
for the yearly tar le archives by clicking on a looking
glass icon adjacent to the tar le listing, accessible
under the Data Accesstab. This service enables users
to query the ISPDv2 via an interactive map of land
station locations, and more efciently determine the
observational data available at any particular time and
region of interest (Figure 7).
3.4. Supporting software and documentation
Source code developed at the University of Colorado
CIRES and NOAA Earth System Research Laboratory
and written in C language to read and decode the
HDF5 data into ASCII column output is provided on the
RDA website under the Softwaretab on the dataset
home page. Documentation describing the HDF5 tables
and ASCII output is also available for download under
the Documentationtab on the dataset home page.
3.5. ECMWF data location and accessibility
A subset of the ISPDv2 is also archived at ECMWF
(http://apps.ecmwf.int/datasets). Here, users may
extract temporal subsets from the ISPDv2 archive
stored at ECMWF and rene their selection by obser-
vation platform. The output from these requests is
written in ASCII column format and contains the date,
location, observed value, report type, unique identier
which allows traceability to other ISPD versions, and
several feedback parameters from the 20CR, including
the 20CR bias estimate, the 20CR ensemble mean rst
guess pressure minus the modiedobserved pres-
sure, and the ensemble mean analysis pressure minus
the modiedobserved pressure. The modiedpres-
sure is the pressure value after the 20CR system
adjusted it to be consistent with the orography in the
assimilating model. This is similar to a reduction to sea
level but instead is an adjustment to the 20CR orogra-
phy, which could be higher or lower than the station
elevation. In addition to the ASCII output, users may
produce observation count maps for their data selec-
tion on the ECMWF web interface.
40 T. A. Cram et al.
ª2015 The Authors.
Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd.Geoscience Data Journal 2:3146 (2015)
4. Future plans
Since the release of ISPDv2, over 22 additional organi-
zations have contributed to the station and marine
components of the ISPD (Table 1) and the period of
record now extends back to 1755. These new contri-
butions have been incorporated into a newer version 3
of the ISPD (ISPDv3), which will be made available in
the near future through the NCAR RDA. The observa-
tion feedback archive (OFA) version of this dataset as
used in the ERA-20C reanalysis is available from
ECMWF (Hersbach et al., 2015).
The gain in observations for ISPDv3 is illustrated by
the time series shown in Figure 8. Most of the
increase occurs in the Northern Hemisphere, and there
are visible increases in the late 19th century and dur-
ing the World War I years. The 2007 decrease in
merged records from the Integrated Surface Database
into ISPDv2 (cf. Figure 4) is recovered in ISPDv3.
These and increases in other years come from diverse
station and marine collections contributed to the ISPD
under the auspices of GCOS, WCRP, and the ACRE ini-
tiative. This includes the initial efforts of the Old-
Weather.org citizen science project that focused on UK
Figure 5. The data subsetting interface found on the ISPDv2 Research Data Archive webpage after user authentication. Users
choose temporal range, spatial selection (choices are interactive map functions, manual latitude/longitude entry, or station identi-
er entry), and also can select one or many observation types, and ASCII or NetCDF output data formats.
The international surface pressure databank 41
ª2015 The Authors.
Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd. Geoscience Data Journal 2:3146 (2015)
Royal Navy observations during 19141923, the citi-
zen-led Todd Project team (http://www.charlestodd.-
net) that focused on recovering observations
assembled by Sir Charles Todd, Meteorologist of the
Colony of South Australia during 18791909, and the
efforts of many other university, national, and interna-
tional organizations (Table 1). Another notable contri-
bution are the North African locations, which arose
from the joint effort between the European Union-
funded EUropean Reanalysis and Observations for
Monitoring (EURO4M: http://www.euro4m.eu) project
in connection with the WMO/MEditerranean climate
DAta REscue (MEDARE) initiative (http://www.omm.
urv.cat/MEDARE; Brunet et al., 2014). The merging of
these contributions results in increases that are partic-
ularly dramatic when viewed as a map, as shown for
the example year of 1918 (Figure 9). The increases
over eastern South America, eastern Africa, China,
New Zealand, and selected central Pacic islands are
especially evident.
ISPDv3 (specically, version 3.2.6) was imple-
mented into the European Reanalysis of Global Climate
Observations database (ERA-CLIM: www.era-clim.eu),
with a goal of using these and additional observations
Figure 6. Example of the content metadata interface found on the ISPDv2 Research Data Archive webpage. Here, users can
view and investigate tabulations of the location, observation type, platform identication, and maps of the global distribution of
observations for the full dataset.
42 T. A. Cram et al.
ª2015 The Authors.
Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd.Geoscience Data Journal 2:3146 (2015)
in an ECMWF data assimilation system to generate
new global climate reanalyses of the 20th century.
The ERA-20C global reanalysis, the rst reanalysis
under this project, spans the 20th century (1900
2010) assimilating only surface and sea-level pressure
observations from ISPDv3 and marine wind observa-
tions from ICOADS 2.5 using a 4D-Var assimilation
system (Poli et al., 2013).
In addition, ISPD version 3.2.9 is being assimilated
into the next generation of the 20CR (version 2c).
20CRv2c uses the same NCEP atmosphere/land cou-
pled model at the same spectral resolution of total
wavenumber 62 (about 200 km by 200 km) as
20CRv2 but has different prescribed boundary condi-
tions. Daily sea surface temperatures come from a
new Simple Ocean Data Assimilation with sparse input
version 2 (SODAsi.2, B.S. Giese, H.F. Seidel, G.P.
Compo and P.D. Sardeshmukh, in review). Prescribed
monthly averaged sea ice concentrations from the
new COBE-SST2 (Hirahara et al., 2014) correct the
known misspecication in 20CRv2 (Compo et al.,
2011; Br
onnimann et al., 2012). This activity is cur-
rently underway and will be reported on in due
course.
The ISPD also will be used in the planned centennial
coupled reanalysis being developed under the Japa-
Figure 7. Example of the le content metadata interface for the yearly tar les found on the ISPDv2 Research Data Archive
webpage. This service enables users to probe the ISPDv2 via an interactive map of land station locations, and more efciently
search for the observational data at any particular time and region of interest.
The international surface pressure databank 43
ª2015 The Authors.
Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd. Geoscience Data Journal 2:3146 (2015)
nese research project Program for Risk Information on
Climate Change (SOUSEI: http://www.jamstec.go.jp/
sousei/eng/research/theme_a.html), by the Meteoro-
logical Research Institute of the Japan Meteorological
Agency and collaborators (M. Ishii, 2014, pers. comm.).
Comments and questions on the ISPD can be direc-
ted to the RDA data specialist or left at the reanaly-
ses.org webpage for this dataset (http://reanalyses.org/
observations/international-surface-pressure-databank).
Acknowledgements
Construction of the ISPD is supported by the U.S.
Department of Energy Ofce of Science (Biological
and Environmental Research program) and the NOAA
Climate Program Ofce. The Twentieth Century
Reanalysis Project and the International Surface Pres-
sure Databank used resources of the National Energy
Research Scientic Computing Center and of the Oak
Ridge Leadership Computing Facility at Oak Ridge
National Laboratory, which are supported by the
Ofce of Science of the U.S. Department of Energy
under Contract No. DE-AC02-05CH11231 and Con-
tract No. DE-AC05-00OR22725, respectively. Manola
Brunet and Philip D. Jones are grateful to the Euro-
pean Union-funded projects EUropean Reanalysis and
Observations for Monitoring (EURO4M, FP7-SPACE-
2009-1 Project No. 242093) and Uncertainties in
Ensembles of Regional Reanalyses (UERRA, FP7-
SPACE-2013-1 Project No. 607193). Pavel Groisman
was partially supported by Grant 14.B25.31.0026 of
the Russian Ministry of Education and Science.
Renate Auchmann was supported by the Swiss
National Science Foundation (SNF; Project TWIST,
200021_146599/1).
The authors thank the following individuals and
organizations for their contributions: (1) Rajmund
Przybylak of the Nicolaus Copernicus University,
Department of Meteorology and Climatology, Poland.
(2) Frank Le Blancq and many others, Jersey Met Ser-
vice, for Channel Island data. (3) The IT group at
CIRES and NOAA/ESRL Physical Sciences Division. (4)
Prashant Sardeshmukh of University of Colorado/
CIRES for valuable guidance and input. (5) Paul Della-
Marta and MeteoSwiss colleagues who originally pro-
vided data to EMULATE. (6) Ingeborg Auer of Zen-
tralanstalt f
ur Meteorologie und Geodynamik (ZAMG)
who originally provided Austrian data to EMULATE. (7)
Clive Wilkinson of NCDC and Climatic Research Unit,
University of East Anglia, Norwich, UK, and Eric Free-
man of NCDC for World War II UK Royal Navy Data.
(8) David Tse of Hong Kong Observatory for Hong
Kong data. (9) Volker Wagner for a subset of the
Deutscher Wetterdienst HISTOR marine data. (10)
Societ
a Meteorologica Italiana (SMI) for the Torino
record.
(11) Mac Benoy and the Australian Meteorological
Association, Todd Project team. (12) Dick Dee,
ECMWF, and the ERA-CLIM project. (13) Manuel Bar-
ros, Instituto Geof
ısico da Universidade do Porto,
University of Porto, Vila Nova de Gaia, Portugal. (14)
Jonathan Burroughs, Joseph Elms (retired) and Tho-
mas C. Peterson, NOAA National Climatic Data Center,
Asheville, NC, USA. (15) Karen Andsager (retired), for-
merly of Midwestern Regional Climate Center, Cham-
Figure 8. Time series of the number of pressure observa-
tions per year in version 2 (thin blue curve) and version
3.2.8 (red curve) of the International Surface Pressure Data-
bank (ISPD) from 1800 to 2011. Note the logarithmic scale
along the y-axis. Inset panel: Time series (shown on a linear
scale) during the period 1820 to 1920 showing the number
of observations in the Northern Hemisphere (blue curves)
and Southern Hemisphere (red curves). The shading high-
lights the gain in observations between the two versions.
Figure 9. Upper panel: Map showing the daily average
number of pressure observations in the new ISPDv3.2.8 for
the year 1918. Lower panel: Difference in the daily average
number of pressure observations between ISPDv3.2.8 and
the ISPDv2 (cf. Figure 3). Counts of observations are made
in ve-degree grid boxes.
44 T. A. Cram et al.
ª2015 The Authors.
Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd.Geoscience Data Journal 2:3146 (2015)
paign, IL USA. (16) Joelle Gergis, University of Mel-
bourne, Australia. (17) Jennifer Hardwick, Met Ofce
Hadley Centre, Exeter, UK. (18) David Jones and Blair
Trewin, Bureau of Meteorology, Melbourne, Victoria,
Australia. (19) Philip L. Woodworth, Proudman
Oceanographic Laboratory, Liverpool, UK. (20) Rein-
hard Zoellner, Deutscher Wetterdienst, Hamburg, Ger-
many. (21) Dr Birger Tinz, Division Maritime
Climatological Monitoring Centre, Deutscher Wetterdi-
enst, Hamburg, Germany. (22) Ricardo M. Trigo, Insti-
tuto Dom Luiz, University of Lisbon, Lisbon, Portugal.
(23) Lu
ıs Filipe Nunes and Manuel Mendes, Instituto
Portugu^
es do Mar e da Atmosfera, Lisbon, Portugal.
(24) Scott D. Woodruff, NOAA Earth System Research
Laboratory, Physical Sciences Division, Boulder, CO,
USA. (25) Michael Kruk, ERT Inc., Asheville, NC, USA.
(26) Leslie Stoecker, Midwestern Regional Climate Cen-
ter, Champaign, IL, USA. (27) Hui Wan and Val Swail,
Environment Canada, Toronto, Canada. (28) Fatou
Sima, Division of Meteorology, Department of Water
Resources, Banjul, The Gambia. (29) Andrea Lock-
wood and Philip Mooney, Hollings Scholars, NOAA
Earth System Research Laboratory, Boulder, CO, USA.
(30) Sam Dean of National Institute of Water and
Atmospheric Research (NIWA), Auckland, New Zealand
for productive conversations.
The following persons afliated with the University
of Giessen, Germany, digitized subdaily pressure data
originating from Europe, Asia, Africa, northern South
America, and the Caribbean: Christine Kolbe, Lisa Flen-
der, Marina Ostheimer, Johanna Englhardt, Athanasios
Tsikerdekis, Lamprini Dergianli, Despina Xoplaki, Chris-
tos Samaras, Eleni Kaimasidou, Nancy Gouta, Elisabet
Tsalkitzidou, Mary Athanasiou, Christakis Athana-
siou, Panagiota Katsaouni, Alexander Theocharis,
Stella Dafka, Kevin Pometti, Gail Kelly, Elda Fleitmann,
Lisa Theile, Petra Strehlau, Ines Stange, and Jonas
Viezens.
References
Allan R, Brohan P, Compo GP, Stone R, Luterbacher J,
Br
onnimann S. 2011. The International Atmospheric Cir-
culation Reconstructions over the Earth (ACRE) initia-
tive. Bulletin of the American Meteorological
Society 92: 14211425, doi:10.1175/2011BAMS3218.1.
Ansell TJ, Jones PD, Allan RJ, Lister D, Parker DE, Brunet
M, Moberg A, Jacobeit J, Brohan P, Rayner NA, Aguilar
E, Alexandersson H, Barriendos M, Brandsma T, Cox NJ,
Della-Marta PM, Drebs A, Founda D, Gerstengarbe F,
Hickey K, Jónsson T, Luterbacher J, Nordli Ø, Oesterle
H, Petrakis M, Philipp A, Rodwell MJ, Saladie O, Sigro J,
Slonosky V, Srnec L, Swail V, García-Suárez AM, Tuo-
menvirta H, Wang X, Wanner H, Werner P, Wheeler D,
Xoplaki E. 2006. Daily mean sea level pressure recon-
structions for the EuropeanNorth Atlantic region for
the period 18502003. Journal of Climate 19: 2717
2742, doi:10.1175/JCLI3775.1.
Brohan P, Allan R, Freeman JE, Waple AM, Wheeler D,
Wilkinson C, Woodruff S. 2009. Marine observations of
old weather. Bulletin of the American Meteorological
Society 90: 219230, doi:10.1175/2008BAMS2522.1.
Br
onnimann S, Grant AN, Compo GP, Ewen T, Griesser T,
Fischer AM, Schraner M, Stickler A. 2012. A multi-data
set comparison of the vertical structure of temperature
variability and change over the Arctic during the past
100 years. Climate Dynamics 39: 15771598,
doi:10.1007/s00382-012-1291-6.
Brunet M, Gilabert A, Jones P, Efthymiadis D. 2014. A his-
torical surface climate dataset from station observations
in Mediterranean North Africa and Middle East areas.
Geoscience Data Journal 1: 121128, doi:10.1002/
gdj3.12.
Compo GP, Whitaker JS, Sardeshmukh PD, Matsui N, Allan
RJ, Yin X, Gleason BE, Vose RS, Rutledge G, Bessemoulin
P, B r
onnimann S, Brunet M, Crouthamel RI, Grant AN,
Groisman PY, Jones PD, Kruk MC, Kruger AC, Marshall GJ,
Maugeri M, Mok HY, Nordli O, Ross TF, Trigo RM, Wang
XL, Woodruff SD, Worley SJ. 2011. The Twentieth Cen-
tury Reanalysis Project. Quarterly Journal Royal Mete-
orological Society 137:128, doi:10.1002/qj.776.
Compo GP, Whitaker JS, Sardeshmukh PD, Matsui N,
Allan RJ, Yin X, Gleason BE, Vose RS, Rutledge G, Bes-
semoulin P, Bronnimann S, Brunet M, Crouthamel RI,
Grant AN, Groisman PY, Jones PD, Kruk MC, Kruger AC,
Marshall GJ, Maugeri M, Mok HY, Nordli O, Ross TF,
Trigo RM, Wang XL, Woodruff SD, Worley SJ. . 2010.
International Surface Pressure Databank (ISPDv2).
Research Data Archive at the National Center for
Atmospheric Research, Computational and Informa-
tion Systems Laboratory, Boulder, Colorado, USA.
doi:10.5065/D6SQ8XDW.
Dupigny-Giroux L-A, Ross TF, Elms JD, Truesdell R, Doty
SR. 2007. RESOURCES - NOAAs Climate Database Mod-
ernization Program: rescuing, archiving, and digitizing
history. Bulletin of the American Meteorological
Society 88: 10151017, doi:10.1175/BAMS-88-7-1015.
Fleming JR. 2000. Meteorology in America, 1800
1870.Johns Hopkins University Press: Baltimore, MD;
292.
Hersbach H, Poli P, Dee D. 2015. The observation feed-
back archive for the ICOADS and ISPD data sets. ERA
Report Series, 18, ECMWF, UK.
Hirahara S, Masayoshi I, Fukuda Y. 2014. Centennial-scale
sea surface temperature analysis and its uncertainty.
Journal of Climate 27:5775, doi:10.1175/JCLI-D-12-
00837.1.
Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D,
Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y,
Leetmaa A, Reynolds R, Chelliah M, Ebisuzaki W, Higgins
W, Janowiak J, Mo KC, Ropelewski C, Wang J, Jenne R,
Joseph D. 1996. The NCEP/NCAR 40-year reanalysis
project. Bulletin of the American Meteorological
Society 77: 437471, doi:10.1175/1520-0477(1996)
077<0437:TNYRP>2.0.CO:2.
Kanamitsu M, Alpert JC, Campana KA, Caplan PM, Deaven
DG, Iredell M, Katz B, Pan H-L, Sela J, White GH. 1991.
Recent changes implemented into the Global Forecast
System at NMC. Weather Forecasting 6: 425435,
doi:10.1175/1520-0434(1991)006%3C0425:RCIITG%
3E2.0.CO;2.
The international surface pressure databank 45
ª2015 The Authors.
Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd. Geoscience Data Journal 2:3146 (2015)
Kistler R, Collins W, Saha S, White G, Woollen J, Kalnay
E, Chelliah M, Ebisuzaki W, Kanamitsu M, Kousky V, van
den Dool H, Jenne R, Fiorino M. 2001. The NCEP-NCAR
50-year reanalysis: monthly means CD-ROM and docu-
mentation. Bulletin of the American Meteorological
Society 82: 247267, doi:10.1175/1520-0477(2001)
082<0247:TNNYRM>2.3.CO:2.
Knapp KR, Kruk MC, Levinson DH, Diamond HJ, Neumann
CJ. 2010. The International Best Track Archive for Cli-
mate Stewardship (IBTrACS): unifying tropical cyclone
best track data. Bulletin of the American Meteorologi-
cal Society 91: 363376, doi:10.1175/2009BAMS2755.1.
Maugeri M, Brunetti M, Monti F, Nanni T. 2004. Sea-level
pressure variability in the Po plain (17652000) from
homogenized daily secular records. International Jour-
nal of Climatology 24: 437455, doi:10.1002/joc.991.
Maugeri M, Lentini G, Brunetti M, Nanni T. 2008. Availabil-
ity and quality of Italian secular meteorological records
and consistency of still unexploited early data. In
MEDAREProceedings of the International Work-
shop on Rescue and Digitization of Climate Records
in the Mediterranean Basin, Brunet M, Kuglitsch FG
(eds). World Meteorological Organization, WCDMP-67,
WMO/TD No. 1432, June 2008; 6170.
Moorthi S, Pan H-L, Caplan P. 2001. Changes to the 2001
NCEP operational MRF/AVN global analysis/forecast sys-
tem. Technical Procedures Bulletin 484, NOAA, NWS:
Silver Spring, MD. http://www.nws.noaa.gov/om/tpb/
484.htm (accessed 3 June 2010).
Parker D, Kent E, Woodruff S, Dehenauw D, Harrison DE,
Manabe T, Mietus M, Swail V, Worley S. 2004. The Sec-
ond JCOMM Workshop on Advances in Marine Climatol-
ogy (CLIMAR-II). WMO Bulletin 53: 157159.
Poli P, Hersbach H, Tan D, Dee D, Th
epaut J-N, Simmons
A, Peubey C, Laloyaux P, Komori T, Berrisford P, Dragani
R, Tr
emolet Y, H
olm E, Bonavita M, Isaksen L, Fisher M.
2013. The data assimilation system and initial perfor-
mance evaluation of the ECMWF pilot reanalysis of the
20th-Century assimilating surface observations only
(ERA-20C). ERA Report Series, 14, ECMWF, UK.
Rayner A, Parker DN, Horton EE, Folland CB, Alexander
LK, Rowell DV, Kent EP, Kaplan CA. 2003. Global analy-
ses of sea surface temperature, sea ice, night marine
air temperature since the late nineteenth century. Jour-
nal of Geophysical Research 108 (D14): 4407,
doi:10.1029/2002JD002670.
Saha S, Nadiga S, Thiaw C, Wang J, Wang W, Zhang Q,
van den Dool HMPan H-L, Moorthi S, Behringer D, Stokes
D, Pe~
na M, Lord S, White G, Ebisuzaki W, Peng P, Xie P.
2006. The NCEP climate forecast system. Journal of Cli-
mate 19: 34833517, doi:10.1175/JCLI3812.1.
Schmith T, Alexandersson H, Iden K, Tuomenvirta H. 1997.
North Atlantic-European pressure observations 1868-
1995 (WASA dataset version 1.0). Technical Report 97-3,
Danish Meteorological Institute, Copenhagen, Denmark,
13. http://www.dmi.dk/leadmin/user_upload/Rapporter
/TR/1997/tr97-3.pdf (accessed 16 February 2001).
Schmith T, Kaas E, Li T-S. 1998. Northeast Atlantic winter
storminess 18751995 re-analysed. Climate Dynamics
14: 529536, doi:10.1007/s003820050239.
Smith A, Lott N, Vose RS. 2011. The Integrated Surface
Database: recent developments and partnerships. Bul-
letin of the American Meteorological Society 92:
704708, doi:10.1175/2011BAMS3015.1.
Uppala SM, K
Allberg PW, Simmons AJ, Andrae U, Bech-
told VDC, Fiorino M, Gibson JK, Haseler J, Hernandez A,
Kelly GA, Li X, Onogi K, Saarinen S, Sokka N, Allan RP,
Andersson E, Arpe K, Balmaseda MA, Beljaars ACM,
Berg LVD, Bidlot J, Bormann N, Caires S, Chevallier F,
Dethof A, Dragosavac M, Fisher M, Fuentes M, Hage-
mann S, H
olm E, Hoskins BJ, Isaksen L, Janssen PAEM,
Jenne R, Mcnally AP, Mahfouf J-F, Morcrette J-J, Rayner
NA, Saunders RW, Simon P, Sterl A, Trenberth KE, Untch
A, Vasiljevic D, Viterbo P, Woollen J. 2005. The ERA-40
re-analysis. Quarterly Journal of the Royal Meteoro-
logical Society 131: 29613012, doi:10.1256/
qj.04.176.
Valente MA, Trigo R, Barros M, Nunes LF, Alves EI, Pinhal E,
Coelho FES, Mendes M, Miranda JM. 2008. Early stages of
the recovery of Portuguese historical meteorological data.
In MEDAREProceedings of the International Work-
shop on Rescue and Digitization of Climate Records
in the Mediterranean Basin, Brunet M, Kuglitsch FG
(eds). World Meteorological Organization, WCDMP-67,
WMO/TD No. 1432, June 2008; 95102.
Wang XL, Feng Y, Compo GP, Zwiers FW, Allan RJ, Swail
VR, Sardeshmukh PD. 2014. Is the storminess in the
Twentieth Century Reanalysis really inconsistent with
observations? A reply to the comment by Krueger et al.
(2013b). Climate Dynamics 42: 11131125,
doi:10.1007/s00382-013-1828-3.
Woodruff SD, Diaz HF, Elms JD, Worley JS. 1998. COADS
Release 2 data and metadata enhancements for
improvements of marine surface ux elds. Physics and
Chemistry of the Earth 23: 517526.
Woodruff SD, Diaz HF, Worley SJ, Reynolds RW, Lubker
SJ. 2005. Early ship observational data and ICOADS.
Climatic Change 73: 169194, doi:10.1007/s10584-
005-3456-3.
Woodruff SD, Worley SJ, Lubker SJ, Ji Z, Eric Freeman J,
Berry DI, Brohan P, Kent EC, Reynolds RW, Smith SR,
Wilkinson C. 2011. ICOADS Release 2.5: extensions and
enhancements to the surface marine meteorological
archive. International Journal of Climatology 31:
951967, doi:10.1002/joc.2103.
Worley SJ, Woodruff SD, Reynolds RW, Lubker SJ, Lott N.
2005. ICOADS release 2.1 data and products. Interna-
tional Journal of Climatology 25: 823842,
doi:10.1002/joc.1166.
Yin X, Gleason BE, Compo GP, Matsui N, Vose RS. 2008.
The International Surface Pressure Databank (ISPD)
land component version 2.2. National Climatic Data Cen-
ter, Asheville, NC, 112. ftp://ftp.ncdc.noaa.gov/pub/-
data/ispd/doc/ISPD2_2.pdf (accessed 19 August 2010).
46 T. A. Cram et al.
ª2015 The Authors.
Geoscience Data Journal published by Royal Meteorological Society and John Wiley & Sons Ltd.Geoscience Data Journal 2:3146 (2015)
... An initiative funded by the Leverhulme Trust saw many of the manuscript instrumental records from 1850 to 1997 digitized (Kenworthy et al., 1997), although until recently knowledge of this dataset remained almost entirely limited to Durham University. The barometric pressure observations (which terminated after December 1960) were included in this project-fortunately, for the Durham pressure record now represents by far the longest digital barometric pressure series in northern England, filling a very large gap in the International Surface Pressure Database (ISPD: Cram et al., 2015). The ISPD forms the majority of the input to the NOAA-CIRES-DOE Twentieth Century Reanalysis version 3 (hereafter 20CRv3; Slivinski et al, 2019, 2021), currently extended back to 1806. ...
... Slivinski et al. (2019) provide an in-depth description of the system that generated the 20CRv3 reanalysis product, which consists of a numerical weather prediction model, an observational dataset and an assimilation method. Using an 80-member ensemble Kalman filter, the 20CRv3 system assimilates only surface pressure observations (a so-called 'sparse' reanalysis) from the open, unrestricted and publicly available International Surface Pressure Databank (ISPD) version 4.7 (Cram et al., 2015;Compo et al., 2019), into the U.S. National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model, with a horizontal resolution of about 60 km at the equator and a vertical atmospheric resolution of 64 levels. Sea surface temperature and sea ice, solar radiation and time-varying atmospheric constituents of volcanic aerosols, stratospheric ozone and atmospheric carbon dioxide (CO 2 ) levels are also specified. ...
Article
Full-text available
A twice‐daily record of barometric pressure exists for Durham Observatory (54.768 °N, 1.584 °W, barometer cistern 107.3 m above mean sea level, MSL) from 23 July 1843 to 31 December 1960 and is published here for the first time. The Durham record, which is 98.7% complete, is by far the longest digital barometric pressure series in northern England and fills a very large temporal and spatial gap in the International Surface Pressure Database (ISPD: Cram et al, [2015] Geoscience Data Journal, 2, 31–46). In what is believed to be the first study of its kind, the record has been independently quality‐controlled against the NOAA–CIRES–DOE Twentieth Century Reanalysis version 3 (20CRv3; Slivinski et al., [2019] Quarterly Journal of the Royal Meteorological Society, 145, 2876; Slivinski et al., [2021] Journal of Climate, 34, 1417–1438), which did not include the Durham records in its assimilation set. This paper describes the instruments used and their exposure, the sources of the record, digitization work undertaken to generate the digital time series (including quality control assessments using 20CRv3) and reduction to mean sea level pressure from station level observations, and examines consistency over the period of record against 20CRv3, concluding with a summary of monthly and annual means and extremes over the 117 year series and the details of the new dataset. A twice‐daily record of barometric pressure exists for Durham Observatory (54.768 °N, 1.584 °W, barometer cistern 107.3 m above mean sea level, MSL) from 23 July 1843 to 31 December 1960, and is published here for the first time. The Durham record, which is 98.7% complete, is by far the longest digital barometric pressure series in northern England, and fills a very large temporal and spatial gap in the International Surface Pressure Database (ISPD). In what is believed to be the first study of its kind, the record has been independently quality‐controlled against the NOAA–CIRES–DOE Twentieth Century Reanalysis version 3 (20CRv3), which did not include the Durham records in its assimilation set.
... The data of the NOAA-CIRES-DOE 20th Century Reanalysis version 3 (20CRv3) on a 1° × 1° grid for the period 1900-2015 were used [56,57]. It assimilated observational data from the International Surface Pressure Databank (ISPD version 4.7), which is the world's largest collection of atmospheric pressure observational data [58]. These surface atmospheric pressure observations are made available through international collaboration with the support of the Atmospheric Circulation Reconstructions over the Earth (ACRE) initiative and the Global Climate Observing System and World Climate Research Programme. ...
Article
Full-text available
The interannual variability of the global mean monthly anomalies of near-surface air temperature, sea-level pressure, wind speed near the surface, amount of precipitation and total cloudiness was investigated. The amplitudes of the anomalies of these hydrometeorological characteristics between opposite phases of the Global Atmospheric Oscillation (GAO) were calculated. The regional element of the GAO in the tropics of the Indian and Pacific Oceans is the Southern Oscillation. The results show that the oscillations of these characteristics are associated with the GAO not only in the tropical belt of the Earth but also in the middle and high latitudes, especially in the Arctic and northern Eurasia. The physical mechanism by which the transition of the GAO from the negative to the positive phase influences the weakening of the Pacific trade winds, and, as a consequence, the onset of El Niño is described.
... These improvements have reduced the sea-level pressure bias and helped to constrain error estimates. However, our checking of International Surface Pressure Databank (ISPD, [55]) archives does not show the presence of Icelandic surface pressure data until November 1845, in both available and assimilated data ( Figure S1). This is somewhat surprising given the known presence of daily pressure observations from Southwest Iceland (Reykjavík and Stykkishólmur) back to 1820 [5,56]. ...
Article
Full-text available
Based on newly-available meteorological reanalysis, we compile and present extended seasonal series of the North Atlantic Oscillation (NAO) and Greenland Blocking indices spanning 1800–2020, which we analyse for evidence of significant trends. This represents a major backward extension of the previously available instrumental-/reanalysis-based Azores–Iceland and principal component-based NAO indices, and allows us to evaluate the potential effect of natural climate perturbations, especially the 1809 and 1815 major volcanic eruptions and ~1790s–1830 Dalton solar minimum, on North Atlantic atmospheric circulation. We find that winters 1809/10 and 1816/17 mark positive NAO peaks, relative to several years before and afterwards, which is in accordance with the theory of volcanic forcing of climate. However, there is little evidence of a summer NAO volcanic signature. Overall, based on the significantly longer new reanalysis time series, the new series presented here corroborate and extend our previous results of: (1) a significantly more variable year-to-year NAO with a recent exceptional clustering of extreme events since 2000 for winter; (2) a significant increasing trend in blocking over Greenland in summer. These trends have major repercussions for the probability of the occurrence of extreme weather events over northwest Europe and for the sensitivity and response of the Greenland Ice Sheet to global warming, especially if they continue as an integral part of anthropogenic climate change.
... After the surface pressure adjustment step, we also apply similar quality control steps as in 20CRv3 considering the plausible range of observations and the distance between first guess and observations. We use the latest version of the surface pressure observational data set, the International Surface Pressure Databank (ISPD) version 4.7 Cram et al., 2015). The surface pressure data from MERRA2 (Gelaro et al., 2017) is used during 2017-2019, since the ISPD version 4.7 data is available up to 2016. ...
Article
Full-text available
Using GFDL's new coupled model SPEAR, we have developed a decadal coupled reanalysis/initialization system (DCIS) that does not use subsurface ocean observations. In DCIS, the winds and temperature in the atmosphere, along with sea surface temperature (SST), are restored to observations. Under this approach the ocean component of the coupled model experiences a sequence of surface heat and momentum fluxes that are similar to observations. DCIS offers two initialization approaches, called A1 and A2, which differ only in the atmospheric forcing from observations. In A1, the atmospheric winds/temperature are restored toward the JRA reanalysis; in A2, surface pressure observations are assimilated in the model. Two sets of coupled reanalyses have been completed during 1961–2019 using A1 and A2, and they show very similar multi‐decadal variations of the Atlantic Meridional Overturning Circulation (AMOC). Two sets of retrospective decadal forecasts were then conducted using initial conditions from the A1 and A2 reanalyses. In comparison with previous prediction system CM2.1, SPEAR‐A1/A2 shows comparable skill of predicting the North Atlantic subpolar gyre SST, which is highly correlated with initial values of AMOC at all lead years. SPEAR‐A1 significantly outperforms CM2.1 in predicting multi‐decadal SST trends in the Southern Ocean (SO). Both A1 and A2 have skillful prediction of Sahel precipitation and the associated ITCZ shift. The prediction skill of SST is generally lower in A2 than A1 especially over SO presumably due to the sparse surface pressure observations.
Article
Daily weather reconstructions (called “reanalyses”) can help improve our understanding of meteorology and long-term climate changes. Adding undigitized historical weather observations to the datasets that underpin reanalyses is desirable; however, time requirements to capture those data from a range of archives is usually limited. Southern Weather Discovery is a citizen science data rescue project that recovered tabulated handwritten meteorological observations from ship log books and land-based stations spanning New Zealand, the Southern Ocean, and Antarctica. We describe the Zooniverse-hosted Southern Weather Discovery campaign, highlight promotion tactics, and replicate keying levels needed to obtain 100% complete transcribed datasets with minimal type 1 and type 2 transcription errors. Rescued weather observations can augment optical character recognition (OCR) text recognition libraries. Closer links between citizen science data rescue and OCR-based scientific data capture will accelerate weather reconstruction improvements, which can be harnessed to mitigate impacts on communities and infrastructure from weather extremes.
Preprint
The winter 1788/9 was one of the coldest winters Europe had witnessed in the past 300 years. Fortunately for historical climatologists, this extreme event occurred at a time when many stations across Europe, both private and as part of coordinated networks, were making quantitative observations of the weather. This means that several dozens of early instrumental series are available to carry out an in-depth study of this severe cold spell. While there have been attempts to present daily spatial information for this winter, there is more to be done to understand the weather variability and day-to-day processes that characterised this weather extreme. In this study, we seek to reconstruct daily spatial high-resolution temperature and sea level pressure fields of the winter 1788/9 in Europe, from November through February. The reconstruction is performed with an analogue resampling method (ARM) that uses both historical instrumental data and a weather type classification. Analogue reconstructions are then post-processed through an ensemble Kalman fitting (EnKF) technique. Validation experiments show a good skill for both reconstructed variables, which manage to capture the dynamics of the extreme in relation to the large-scale circulation. These results are promising for more such studies to be undertaken, focusing on different extreme events and other regions in Europe and perhaps even further back in time. The dataset presented in this study may be of sufficient quality to allow historians to better assess the environmental and social impacts of the harsh weather.
Article
Full-text available
We describe a new El Nino Southern Oscillation (ENSO) index that we call the Ensemble Oceanic NINO Index (Ensemble ONI). Ensemble ONI uses monthly sea surface temperature (SST) anomalies in the NINO3.4 region from 32 input SST datasets, which form the ensemble, and our new monthly ENSO index extends from the present back to the year 1850. We use the input datasets to quantify the uncertainty in the monthly index values. The uncertainty is calculated from datasets that are not completely independent from each other, but comparing our findings to other estimates of uncertainty in SST, well‐documented historical ENSO events, a detailed consideration of literature‐based definitions of ENSO events, and proxy‐based determinations of past ENSO events, we show that our uncertainty estimates from the ensemble of input datasets is comparable both in terms of average magnitude and time‐varying trend with other studies. Similar to past research, we find that ENSO events occur every 4‐5 years on average, and there have been six “Super” El Ninos (1877‐78, 1888‐89, 1972‐73, 1982‐83, 1997‐98, 2015‐16) that statistically rise above all other El Ninos since 1850. Finally, the time span of our work shows that El Nino events were most intense at ends of both the 19th Century and the 20th Century, with a lull in the mid‐1900s, corroborating previous instrumental, written, and proxy records.
Article
Missing or erroneous station metadata are often a source of error in determining the reference level for the reported station pressure in conventional land‐based weather reports: the reported pressure may be inconsistent with any reported mean‐sea‐level pressure, limiting the usefulness of the data for climate or operational forecasting work. Historical Irish observations are used to highlight the issue and demonstrate how changes in the barometer elevation height may be detected and accurately estimated.
Preprint
Full-text available
Storm tides represent a major threat to the low-lying German North Sea coast. Knowledge of extremes is essential for the design of reliable and robust coastal defences. A storm tide that occurred on 12–13 March 1906 along the German Bight coastline still represents one of the strongest events on record. For this event, detailed knowledge of atmospheric and hydrodynamic conditions is still lacking. To assess the potential impact of such an event on today’s coastline, century-long atmospheric reanalysis data together with a manual synoptic reconstruction based on archived weather data were used to drive a tide-surge model and to simulate water levels during the event. Sensitivity experiments were performed to estimate potential amplification of water levels that could have been caused by different time lags between the storm and the astronomical tide. Comparison between the model results and the limited available observational data indicated, that the water levels could be reasonably reconstructed using wind fields from the manual synoptic approach and some of the reanalysis ensemble members. The amplification potential was found to be low because the storm occurred during spring tide and shifts in the phase of the astronomic tide yielded only small changes in total water levels. To summarize, if pressure data are available at relevant locations, historical storm surges can be simulated with reanalysis products and also with a manual synoptic reconstruction.
Article
The extension of the ERA5 reanalysis back to 1950 supplements the previously published segment covering 1979‐present. It features the assimilation of additional conventional observations, as well as improved use of early satellite data. The number of observations assimilated increases from 53 000 per day in early 1950, to 570 000 per day by the end of 1978. Accordingly, the quality of the reanalysis improves throughout the period, generally joining seamlessly with the segment covering 1979‐present. The fidelity of the extension is illustrated by the accurate depiction of the North Sea Storm of 1953, and the events leading to the first discovery of sudden stratospheric warmings in 1952. Time series of ERA5 global surface temperature anomalies show temperatures to be relatively stable from 1950 until the late 1970s, in agreement with the other contemporary full‐input reanalysis covering this period and with independent datasets, although there are significant differences in the accuracy of representing specific regions: Europe being well represented in the early period and Australia less so. The variability of ERA5 precipitation from month to month agrees well with observations for all continents, with correlations above 90% for most of Europe and generally in excess of 70% for North America, Asia and Australia. The evolution of upper air temperatures, humidities and winds show smoothly varying behaviour, including tropospheric warming and stratospheric cooling, modulated by volcanic eruptions. The Quasi Biennial Oscillation is well represented throughout. Aspects to be improved upon in future reanalyses include the assimilation of tropical cyclone data, the spin‐up of soil moisture and stratospheric humidity, and the representation of surface temperatures over Australia. This article is protected by copyright. All rights reserved.
Data
Full-text available
Here we present the first results achieved with the project SIGN (Signatures of environmental change in the observations of the Geophysical Institutes). The project’s main goal is to convert into a digital database the historical meteorological data, recorded since 1856 until 1940 in several annales published by the 3 Portuguese Geophysical Institutes (of Lisbon, Porto and Coimbra) and the Portuguese Meteorology Institute. The different sets of historical data contain monthly, daily and sometimes hourly records of pressure, temperature, precipitation, humidity, wind speed and direction, cloud cover, evaporation and ozone. The published data cover several stations in mainland Portugal, the Azores and Madeira islands and in former Portuguese African and Asian colonies. The main objective is to use the data to study the changes that have taken place in the historical records during the last 150 years, when the recovered data is merged with the post-1941 data stored in the Meteorology Institute digital database. The other aim is to make the data available to the meteorology community at large. Direct observations of pressure data for Lisbon in the 1856-1940 period were prioritised and have been manually digitised, being later subjected to quality control tests. Digital historical records of Lisbon temperature, relative humidity and precipitation data have been obtained through corrected OCR techniques applied to published hourly or bi-hourly tables. Preliminary digital results are also available for several stations in mainland Portugal, Azores and Madeira. Data for the Escola Médico-Cirúrgica station in Porto during the 1861-1898 period are already in digital format. All datasets are subjected to an initial quality control test, to detect wrong values, with more comprehensive tests to be applied at later stages. At the same time, detailed metadata files are being compiled for each station. This work will show the preliminary analysis results for the digital historical database obtained so far.
Article
Full-text available
The Atmospheric Circulation Reconstructions over the Earth (ACRE) Initiative was established with the goal of encouraging and undertaking the research work needed to produce and use reanalyses for climate applications. ACRE works closely with the international surface weather and climate observations community particularly the International Surface Pressure Databank, the international RECLAIM, the International Environmental Data Rescue Organization, and NOAA's NCDC Climate Database Modernization Program. The first ACRE-facilitated reanalysis product, the 20CR Version 2 dataset, has global four-times-daily atmospheric and surface fields spanning 1871-2008. The 20CR generates global gridded wind, temperature, pressure, humidity, and other variables at 2° latitude by 2° longitude horizontal resolution and 28 vertical levels with an ensemble of 56 analyses for each 6-hourly time step. The 20CR also generates quality control information and other metadata about the ISPD observations.
Article
Full-text available
NOAA's National Climatic Data Center (NCDC) initiated the Integrated Surface Database (ISD) project in 1998 to address the problem of scattered climatological data. The goal of the project was to merge several surface hourly datasets into a common format and data model, providing a single collection of global hourly data for the user that was continuously updated and available. Additional benefits of integration included the reduction of subjectivity and inconsistencies among datasets that span multiple observing networks and platforms and standardized quality control (QC) based on reporting time resolution. The result of this effort was a dataset containing data from more than 100 original data sources that collectively archived several meteorological variables. The primary data sources included the Automated Surface Observing System (ASOS), Automated Weather Observing System (AWOS), Synoptic, Airways, METAR, Coastal Marine (CMAN), Buoy, and various other kinds of data.
Article
Full-text available
We present the Met Office Hadley Centre's sea ice and sea surface temperature (SST) data set, HadISST1, and the nighttime marine air temperature (NMAT) data set, HadMAT1. HadISST1 replaces the global sea ice and sea surface temperature (GISST) data sets and is a unique combination of monthly globally complete fields of SST and sea ice concentration on a 1° latitude-longitude grid from 1871. The companion HadMAT1 runs monthly from 1856 on a 5° latitude-longitude grid and incorporates new corrections for the effect on NMAT of increasing deck (and hence measurement) heights. HadISST1 and HadMAT1 temperatures are reconstructed using a two-stage reduced-space optimal interpolation procedure, followed by superposition of quality-improved gridded observations onto the reconstructions to restore local detail. The sea ice fields are made more homogeneous by compensating satellite microwave-based sea ice concentrations for the impact of surface melt effects on retrievals in the Arctic and for algorithm deficiencies in the Antarctic and by making the historical in situ concentrations consistent with the satellite data. SSTs near sea ice are estimated using statistical relationships between SST and sea ice concentration. HadISST1 compares well with other published analyses, capturing trends in global, hemispheric, and regional SST well, containing SST fields with more uniform variance through time and better month-to-month persistence than those in GISST. HadMAT1 is more consistent with SST and with collocated land surface air temperatures than previous NMAT data sets.
Article
A new sea surface temperature (SST) analysis on a centennial time scale is presented. In this analysis, a daily SST field is constructed as a sum of a trend, interannual variations, and daily changes, using in situ SST and sea ice concentration observations. All SST values are accompanied with theory-based analysis errors as a measure of reliability. An improved equation is introduced to represent the ice-SST relationship, which is used to produce SST data from observed sea ice concentrations. Prior to the analysis, biases of individual SST measurement types are estimated for a homogenized long-term time series of global mean SST. Because metadata necessary for the bias correction are unavailable for many historical observational reports, the biases are determined so as to ensure consistency among existing SST and nighttime air temperature observations. The global mean SSTs with bias-corrected observations are in agreement with those of a previously published study, which adopted a different approach. Satellite observations are newly introduced for the purpose of reconstruction of SST variability over data-sparse regions. Moreover, uncertainty in areal means of the present and previous SST analyses is investigated using the theoretical analysis errors and estimated sampling errors. The result confirms the advantages of the present analysis, and it is helpful in understanding the reliability of SST for a specific area and time period.
Article
Historical climatic data from station observations taken in North African and Middle East Mediterranean countries since the second half of the 19th century have been digitized and quality-controlled in the framework of the EU-funded European Reanalysis and Observations for Monitoring (EURO4M) project. Daily maximum and minimum temperatures and precipitation totals, along with sub-daily data for surface air pressure have been recovered by using historical data sources involving book/logbook collections archived in national and international data centres. The new dataset produced comprises climatic time series for 79 stations that have operated in southern and eastern Mediterranean countries. While the developed time series have data gaps, every effort has been made to infill these gaps, to improve assessments of the long-term changes in climate variability in the region.
Article
ERA-40 is a re-analysis of meteorological observations from September 1957 to August 2002 produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) in collaboration with many institutions. The observing system changed considerably over this re-analysis period, with assimilable data provided by a succession of satellite-borne instruments from the 1970s onwards, supplemented by increasing numbers of observations from aircraft, ocean-buoys and other surface platforms, but with a declining number of radiosonde ascents since the late 1980s. The observations used in ERA-40 were accumulated from many sources. The first part of this paper describes the data acquisition and the principal changes in data type and coverage over the period. It also describes the data assimilation system used for ERA-40. This benefited from many of the changes introduced into operational forecasting since the mid-1990s, when the systems used for the 15-year ECMWF re-analysis (ERA-15) and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re-analysis were implemented. Several of the improvements are discussed. General aspects of the production of the analyses are also summarized.