The specification of sea surface temperature (SST) and sea ice concentration
(SIC) is the most basic and perhaps the most important experimental condition
for AMIP II. The AMIP I boundary conditions
(BCS) suffered from two basic deficiencies: 1) large and unrealistic changes
in sea ice coverage, particularly in the Antarctic; and 2) undefined
points that led to substantial and unintended differences in the actual
SST used in the 30+ models, especially around the coastlines. The first
problem is data related while the second is primarily technical.
To overcome these observation deficiencies, and to improve the quality
of the boundary forcing, we have created AMIP II BCS observations (BCSOBS)
Consistency with reanalysis is desirable as reanalysis will provide the
bulk of AMIP II validation data. However, a considerable effort has gone
into improving the BCS data sets since the reanalysis BCS were set two
years ago. This is particularly true for SIC. While consistency with reanalysis
is desirable, we wanted BCSOBS best representative of the relatively observationally
rich 17-year AMIP II period as opposed to BCSOBS designed for multi-decadal
GCM integrations such as the Climate of the 20th Century (C20C)
Consistent with the BCSOBS used in the ECMWF
and NCEP reanalysis
Most current and best available
Appropriate for climate model applications.
Include the land and glacier/ice-shelf points masks used in the development
Global grids with data defined at all points
Another deficiency in the AMIP I BCS was
a smoothed and reduced amplitude of the SST annual cycle caused by interpolation
of the monthly means (assumed to be valid at the mid point of the month)
to daily values. That is, the monthly mean of the daily-interpolated values
was different than the original observed input monthly mean. Taylor
et al (1996) have developed a procedure for AMIP II to adjust the observations
so that the monthly mean of the daily-interpolated BCS is the same as the
observed monthly mean (BCSOBS). Thus, the SST and SIC BCS for AMIP
II will not be the same as the observations. This paper focuses
solely on the observations.
To avoid technical differences in BCS among the AMIP II participants,
the BCS have valid data at all points. The method for filling undefined
points over land/glaciers is the same algorithm as used in the NCEP reanalysis
for setting SST over land and produces smoothly varying fields across the
The BCS in the NCEP/NCAR
reanalysis and ECMWF
reanalyses were linearly interpolated to daily values to simplify their
use in the assimilation process. When appropriate, we have formed monthly
means using these daily values. The source data for these "daily" values,
however, come from monthly mean, weekly or daily analyses. The change in
frequency of the source data sets was the primary reason for using monthly
means instead of daily or weekly BCSOBS in AMIP II.
There are three basic sources of data for the AMIP II BCSOBS:
The daily OISST and SIC data were kindly provided by NCEP and are identical
to that used in both reanalyses. The GISST2.2a data were provided courtesy
of the Hadley Centre.
satellite-sensed sea ice concentration from A. Nomura (JMA/ECMWF)
[Nomura, 1995 #32] and R. Grumbine (NCEP) [Grumbine, 1996 #34] - passive
microwave radiation converted to sea ice concentration using the NASA-Team
algorithm [Cavalieri, 1992 #92]
from NCEP (Optimal Interpolation SST) [Reynolds, 1994 #17] - bias corrected
GISST2.2a (Global Ice and SST) from the Hadley
Centre of the U. K. Meteorological
Office [Rayner, 1995 #33] - A new, EOF-reconstructed SST used for the
C20C integrations. GISST2.2a is the corrected version of GISST2.2. In the
original GISST2.2, the SST in the sea ice margin was in error.
The properties of the source data for the AMIP II BCSOBS are illustrated
using a time
line and in the following table.
We first note that the observations have three different frequencies and
two horizontal grid resolutions. Furthermore, while most grids had a resolution
of 1x1 deg, some had different origins. Thus, a considerable amount of
data mechanics was necessary to rectify the data to the common grid (e.g.,
shifting of the cyclically continuous in longitude grids to the prime meridian).
GISST 2.2a (UKMO)
The most important properties are summarized as:
Different sources for both SST and SIC
Different times when the source data change
Different sea ice data used in the two SST analyses
Preprocessing of the SST Source Data
Both the GISST2.2a and OISST data sets contained internal sea ice masks
(SST ~ -1.8 C) derived from different sources. Furthermore, these internal
masks differed from that in the Nomura/Grumbine SIC. Thus, the SST and
SIC used in reanalysis could conflict. For example, the OISST might have
a point which is sea ice (-1.8 C) but the SIC data has the point as open
ocean. Conversely, the SIC might define a point as being ice, but the SST
would be warmer than the sea ice temperature.
The problem is illustrated for the daily
analyses of 1 January, 1996. The white boxes show where the original
SIC and SST data were defined as sea ice using 0.55 as the threshold value
for the mask. The blue boxes show where the SIC analysis defines open ocean
but the OISST is sea ice, and the red boxes where the OISST is open ocean
and the SIC analysis indicates sea ice. The greatest areal extent of the
conflicts occur in the southern
ocean although the difference in SST
between the original and purified SST analysis is not large. In the
we find more unfrozen points in the OISST where the SSTs
are considerably warmer that sea ice.
To reduce such conflicts the original daily OISST were adjusted in the
internal sea ice margins to be reflective of an ocean-only analysis. This
"purification" process works as follows:
Finally, there are three missing days in the daily-interpolated SST in
1995: 6,12, and 17 November.
The first and last five rows on the global grid (85-90 deg) were set to
the sea ice temperature of -1.8 C.
The remaining sea ice points were set to undefined
The undefined points were filled in using a Cressman scan analysis procedure
dubbed the "weaver" at NCEP. This is the same routine used to fill or define
SST values over land and insures a reasonable transition across the land/sea
The land sea mask is the same as used in the NCEP OISST.
This mask has more ocean points than would be typically used in a model,
but has the advantage of allowing the SST analysis to have more influence
at the land/sea boundary -- a desirable feature when interpolating to coarser
The glacier mask that came from the NCEP reanalysis is used primarily
to differentiate open ocean from the permanent ice shelves of Antarctica.
The mask was originally 2 deg, but was adjusted manually to be more representative
of higher resolution ice shelves as seen in [Gloersen, 1992 #41].
A graphic of the mask may be found by clicking here.
Creation of the SIC BCSOBS
Although the three SIC source data sets have different sampling frequencies
and horizontal resolution, these data sets are all based on passive microwave
observations from the SSMR and SSM/I instruments aboard the Nimbus 7 and
DMSP satellites. Further, they use a common algorithm to convert the radiation
to sea ice concentration.
The two basic data sets are identified by their creators A. Nomura (JMA
and ECMWF, see [Nomura, 1995 #32]) and R. Grumbine (NCEP, see [Grumbine,
1996 #34]) and the final data set for AMIP II will be called "AMIP II."
For the Grumbine sea ice, the documentation may be found in (http://polar.ncep.noaa.gov/seaice/icegroup.html).
Also, click here
for an excellent starting point on sea ice.
The original satellite data was on a ~25 km polar stereographic grid
and was then interpolated to a lat/lon grid for use in models. Nomura applied
extensive quality control of the lat/lon sea ice concentration to eliminate
bad and missing data. He choose a weekly time interval to insure sufficient
observations in each grid box as the SSMR sampling was less than the SSM/I.
Whereas Nomura started with SIC, Grumbine uses the radiation data directly
and applies quality control to both the source data and SIC product. More
significantly, Grumbine performs his analysis daily.
Intercomparison during the overlap period
We first compare the two data sets in the one month where they overlap
- December of 1991. Grumbine uses an updated algorithm for the radiance
to sea ice conversion and more extensive quality control. Close correspondence
would indicate the resulting sea ice analysis is dominated by the observations
and not the process (i.e., the "what" versus the "how"). In the Northern
Hemisphere we find very good agreement (I have only averaged and rectified
the grids at this point), even when looking in the difference
field. However, the Grumbine field has a slightly higher net concentration,
particularly in Hudsons Bay, but these differences are well within the
error bounds (3-10%).
The agreement in the Southern
Hemisphere is less good and we find consistently lower concentrations
in the difference
field. Also, note the differences in at the glacier/land - ocean interface
- a consequence of different masks. Nevertheless, the difference are still
within error bounds and, as will be shown later, no large discontinuites
were discovered. Thus, we conclude that the Grumbine and Nomura data are
likely comparable and consistent even though the Grumbine data is expected
a priori to be of higher quality because of improved observations
While the intercomparison showed reasonable agreement between the data
sets, some differences in the final product used by reanalysis remained
due to how the polar stereo data was interpolated to the 1 (0.5) deg lat/lon
grid and different land masks. Thus, some minor adjustments were required
to generate the final AMIP II SIC monthly means.
The process is outlined below:
Generation of the monthly mean SIC
average the daily SIC data to form the monthly mean. For Nomura the "daily"
data comes from an interpolation of original weekly analyses to daily values
as in the NCEP reanalysis. In contrast, the Grumbine data is a true daily
set values >= 0.98 to 1.0 to rectify differences in how high ice concentration
is treated (Nomura 0.99 and Grumbine 1.00).
If undefined, set the unobserved values near the North Pole (85-90) to
move the data to the standard 360x180 1deg grid where (1,1) = 0.5E, 89.5S
area-weighted regridding of the 0.5 deg data
shifting in longitude (Nomura (1,1) = (179.5W, 89.5S) ; Grumbine (1,1)
= 0.5W, 89.5N)
fill the undefined points in the Nomura data using successive regridding.
This procedure extends defined data into the undefined regions and gives
smooth transitions at the coastlines. The purpose is to provide reasonable
values when interpolating to a coarser (generally) model grid. The
regrid extrapolation does not change any defined data.
generate masks where SIC > 0.10, 0.15, 0.35, 0.55, 0.70 (1=sea ice, 0 =
open ocean, undefined values at land or glacier points).
generate SIC from the GISST2.2 data using the same algorithm as above for
Calculate "sea ice extent"
Sea ice extent is defined by [Gloersen, 1992 #41] as the surface of the
earth with SIC > 15 % and expressed as millions of km2 . We
calculate sea ice extent in the same (roughly) 12 areas as [Gloersen, 1992
#41] for intercomparison purposes and to verify the data is reasonable.
A similar analysis would have uncovered the more egregious errors in the
AMIP I SIC in the Southern Hemisphere.
Properties of the SIC BCSOBS
An interesting technical feature of the data were infrequent, large areas
of very low SIC (2% and lower) in both sets, e.g., December,
1990 (Nomura) and June,
1995 (Grumbine). For models that use SIC instead of a mask and that
might respond to very low values, a check would be needed. I did not filter
these points out because they could be realistic in the ice margins and
in the regions of low ice concentration near the Ross Ice Shelf as seen
in the 17-year
mean for February (summer time). Further, such a filter algorithm is
not grossly obvious and might not be necessarily appropriate for all models.
To detect trends and temporal consistency we examine the sea ice extent
calculated in a similar way as in the Gloersen et al. 1992 NASA SSMR sea
ice atlas. Because of different land/sea masks and crude area definition
there are differences between the AMIP II sea ice extent and that in the
NASA atlas. However, comparing the Arctic
time series to that on p. 114 of the atlas, we see excellent agreement
in the low ice months but about 15 % greater extent in the winter. This
difference is mostly a consequence of the mask (I have few land points).
Because we need data at all points for interpolation to the model grids,
this discrepancy is considered acceptable. The key point is that character
of the time series is similar and no untoward trends are found in the AMIP
II data. In contrast, the GISST2.2a data shows a serious negative trend.
Another notable feature is the large increase in December of 1995 in
both the Arctic and Antarctic
time series. This is the point where Grumbine shifts to a 0.5 degree
analysis and further investigation and more data are needed to understand
if these data are biased.
The table below provides time series in the other Gloersen et al. areas
and gives some comments on the plots.
Intercomparison of GISST2.2a and AMIP II sea ice extent
Despite some of the "interesting" features (occasional large areas of very
low ice concentration and the differences in the 0.5 versus 1.0 deg analyses),
the basic finding is that the AMIP II SIC, because of good temporal consistent
and better data sources, is more suitable for the 17-year integration than
the GISST2.2a SIC.
||Positive trend in AMIP II ice, GISST no trend,
but more ice in 1994-96 period in AMIP II.
||Substantial, ENSO-like interannual variation
in both data sets, positive trend in AMIP II ice
||Negative trend in GISST ice, increase in
the 94-96 period, more ice in GISST
||Negative trend in GISST, slight negative
trend in AMIP II, interesting interannual variation
of O / Japan (Arctic)
||Slight negative trend in both data sets,
more ice in AMIP II
ice in AMIP II, but strong negative trend in GISST
Ocean Sector (Antarctic)
||AMIP II more consistent, big jump in ice
in the GISST data around 1989, large change in summer time ice in 1996
(shift to 0.5 degree analysis)
Pacific Sector (Antarctic)
||Large interannual variation, less trend in
||Large positive trends in both data sets,
similar 1996 summer ice feature as in the IO sector
||Strong interannual variation in both sets,
slight but opposite trends
||Positive trend in GISST because of greater
ice in 1990's
||More ice and
a big jump starting in 1989 in the GISST data leading to a positive bias
One final note is that the Grumbine SIC, after the change to daily analyses
(December, 1995), has values over Antarctica whereas the Nomura SIC and
the early weekly analyses do not. Thus, plots of SIC after this change
will show quasi realistic values, but the regrid interpolation does not
extend values (100%) to the South Pole and they are remain zero as intialized.
This "feature" should have no affect on models with reasonable land/glacier
Creation of the SST BCSOBS
Unlike SIC, where the concentrations were derived from the same type of
observing platform, two different sources for data were used to construct
the AMIP II SST BCSOBS. These two sources differ in:
and the properties of the resulting SST time series did, not unexpectedly,
show large changes as the source data changed. The processing will be described
in the sections below according to the major decisions in adjusting the
data for AMIP II.
availability of satellite data
treatment of SST in the ice margins
EOF reconstruction in the tropical-midlatitude oceans
Using the original monthly means for the GISST2.2a
data instead of the daily-interpolated fields
from reanalysis (GISST2.2)
The original GISST2.2 data are monthly means and these means were linearly
interpolated to create daily values for the NCEP reanalysis during the
pre-OISST period (November, 1981-present) by assuming that the monthly
means were valid at the midpoint of the month. Full consistency with the
NCEP reanalysis would require averaging these daily interpolated fields,
but there were two problems with this approach: 1) an error in the ice
margins discovered at the UKMO; and 2) damping of the annual cycle (AC).
Thus, we used the corrected (GISST2.2a) monthly means.
To illustrate the AC damping problem, examine the difference between
monthly means averaged from daily values and the original monthly means
by clicking here.
Note the positive differences on the order of 0.5 C but sometimes > 1 C
in the summer hemisphere midlatitudes. This implies a substantial (> 10%)
bias in the amplitude of the annual cycle in the midlatitudes. A similar
but smaller problem was found in the Southern
Hemisphere. In this case, note the strong cool anomalies in the Gulf
Stream and Kuroshio currents.
Rectify the SST to SIC
As noted earlier, different SIC data sets were used in the source SST data
and, as discussed, the first adjustment was to "purify" or preprocess
the source SST data (daily or monthly mean) in its own sea ice zones. However,
because of different values of SST in the SIC mask regions (SIC
> 55 %), averaging of the daily OISST means and different land/sea masks;
different SSTs were found in SIC margins. Hence, the monthly means were
adjusted to the same SST in the SIC mask regions, and in
the SIC margins (10 % < SIC < 55 %) to insure smooth,
and consistent transitions from an ice free to a frozen surface.
We first define two SSTs :1) SSTSIC
Mask = 271.38 K ; and 2) SSTSIC Mask + = 271.48 K for
the rectification. In the final SST, all points in the SIC mask region
will equal SSTSIC Mask , but first all
SST below SSTSIC Mask + are set to SSTSIC Mask + to
eliminate excessively cold points. Next, all points in the glacier regions
and the SIC mask region are set to SSTSIC Mask
. The remaining land-only points (not sea and not glacier), and the points
in the sea ice margin are set to the undefined value. Thus, the SST field
at this point equals: 1) SSTSIC Mask in the sea ice mask
region and at glacier points; 2) the original value where SIC >
10%; and 3) undefined over land and in the sea ice margin.
We next apply the "weaver" algorithm as described in the SST
preprocessing section to set these undefined points to give smooth
transition in the margins. However, the SST in the ice free regions (SIC
> 10%) may still be excessively cold if the point was classified as sea
ice in the original data, but in the adjusted SST it will at least not
be frozen (SSTSIC Mask+).
Differences in the annual cycle in the ice margins
The GISST2.2a and OISST data treat the water temperature in the sea ice
zones very differently. GISST2.2a sets the SST in the SIC > 0 region using
an empirical relationship based on observations. In contrast, the
OISST sets the SST to a constant on the sea ice mask (SIC > 50 % not 55
% as in ERA) zone and the analysis procedure defines the SST in
the marginal ice zones (0 < SIC < 50 %). Thus, the SST could conflict
not only from differences in SIC, but in how the SST was treated in the
ice margins and a serious difference was uncovered. Examining the zonally
averaged SST with the annual cycle removed shows anomalous warm water
in the marginal sea ice regions around 80 N and 70S only during the GISST2.2a
The annual cycle (AC) was then calculated for the three-year period
before (GISST2.2a AC) and after (OISST AC) the change to OISST. The difference
field is striking, e.g., see the January
field. While there is a real change in the AC in the tropics and midlatitude
oceans, a persistent warm bias was found in the high latitude ice margin.
Use the table below to view a particular month:
Intercomparison of the GISST2.2a AC 7812-8111 and the OISST
8212 - 8411
To eliminate the large differences in the AC in the data sets, the GISST2.2a
AC was adjusted to be consistent with the OISST. We are not suggested that
the OISST AC is necessarily better (i.e., more accurate). Rather, temporal
consistency with the dominant source of the SST (OISST) necessitates the
adjustment. The modification is to simply replace the 3-year GISST2.2a
AC with the 3-year OISST AC (i.e., the AC based on the three years before
and after the change to OISST). However, the replacement is weighted so
that only the high latitude AC is changed. Specifically, no adjustment
equatorward of 55 deg and full replacement poleward of 65 deg. An example
of the correction at a specific
longitude and time graphically shows the large differences. Note that
some points are below SSTSIC Mask. . This illustrates how the
adjustment distorted the SIC-SST consistency. Thus, we performed the SIC-SST
rectification again to bring the SIC and SST back in synch, but only for
the GISST2.2a period. Also, the overcorrection only occurred at only a
version of the original
zonal average SST shows that the AC replacement has yielded a consistent
time variation in the high latitudes without affecting the tropics and
midlatitudes. It is also interesting to note the apparent warming trend
near ice margins from 1991-1995 in the Northern Hemisphere. This trend
is not clearly linked with the corresponding SIC
trends in the AMIP II SIC. The big increase in sea ice extent in 1996
and the cooler anomalies is interesting, but may be questioned because
of the change in the SIC analysis grid.
The small SIC-SST intercomparison above suggests that the SST should
be reanalyzed using the more consistent AMIP II-type SIC and that
such a reanalysis should explicitly link the SIC and SST in a more physical/observational
manner (the explicit SIC-SST relationship in the GISST2.2a data is a step
in this direction). Despite the lack of a direct connection between the
AMIP II SIC and SST observations, we believe these data meet the requirements
of the experiment. That is,
While SIC-SST reanalysis (i.e., a comprehensive near surface oceanographic
reanalysis) is clearly needed, these AMIP II BCS observations are probably
the best available for climate model integrations during the 17-year AMIP
A full set of graphics (3 areas (global, nhem and shem) * 2 fields (SIC
and SST) * 2 types (mean and anomaly) * (208 months + 12 months of climatology)
~ 2600 plots) is available by clicking here
(or on the section title). This page is a form that lets you "point and
click" to select a plot.
There are several ways to access the data. The recommended path is by having
PCMDI generate the BCS and the BCSOBS on your grid. See http://www-pcmdi.llnl.gov/projects/amip/amip2/AMIP2EXPDSN/BCS/amip2bcs.html#Introduction
where you will find links to our generation process as well as links to
the data on the 1° grid
in a variety of formats. This document also gives a complete description
of the AMIP II BCS.
Consistency with the NCEP and ECMWF reanalyses
Consistency in time
A physical consistency between SST and SIC
We hope the interactive nature of this document and the extensive plots
will be useful in developing a clearer understanding of the data. No analysis
and discussion of the data can substitute for a modeler / user's direct
Your findings and discussion are needed to make this document a true
community resource. Please forward them to Mike at email@example.com.
Last update 12 September 1997