• Source Data
• Purification of the OISST
• Land/Sea/Glacier Masks
• Creation of the Sea Ice Concentration (SIC) fields
• Properties of the SIC
• Creation of the SST fields
• SST and SIC Graphics
• SST and SIC Observation Data
• Conclusions

Introduction

To overcome these observation deficiencies, and to improve the quality of the boundary forcing, we have created AMIP II BCS observations (BCSOBS) that are:

• 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 land/sea interface.

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.

Source Data

1. 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]
2. OISST from NCEP (Optimal Interpolation SST) [Reynolds, 1994 #17] - bias corrected and reanalyzed.
3. 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.
 

Field	Source	Period	Frequency	Period	Resolution
SST	GISST 2.2a (UKMO)  OISST (NCEP)	7812-8111  8112-present	monthly  weekly	7812-9512	1x1deg
Sea Ice	Nomura (ECMWF)  Grumbine (NCEP)  Grumbine (NCEP)	7812-9111  9111-9510  9511-present	weekly  daily  daily	7812-9511  9512-present	1x1 deg  1x1 deg  0.5x0.5 deg

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).

The most important properties are summarized as:

1. Different sources for both SST and SIC
2. Different times when the source data change
3. Different sea ice data used in the two SST analyses

Preprocessing of the SST Source Data

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 Arctic, 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:

• 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 boundary.

Masks

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

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." 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

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 and processing.

Data Adjustments

The process is outlined below:

1. 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 analysis.
• 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 1.0.
• move the data to the standard 360x180 1deg grid where (1,1) = 0.5E, 89.5S by:
• 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 intercomparison purposes.

1. 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

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.

 
 

Sea / Region	Comments
Bering Sea (Arctic)	Positive trend in AMIP II ice, GISST no trend, but more ice in 1994-96 period in AMIP II.
Labador Sea (Arctic)	Substantial, ENSO-like interannual variation in both data sets, positive trend in AMIP II ice
Greenland Sea (Arctic)	Negative trend in GISST ice, increase in the 94-96 period, more ice in GISST
Berents Sea (Arctic)	Negative trend in GISST, slight negative trend in AMIP II, interesting interannual variation
Sea of O / Japan (Arctic)	Slight negative trend in both data sets, more ice in AMIP II
ARCTIC OCEAN	Slightly more ice in AMIP II, but strong negative trend in GISST
Indian 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)
Western Pacific Sector (Antarctic)	Large interannual variation, less trend in AMIP II
Ross Sea (Antarctic)	Large positive trends in both data sets, similar 1996 summer ice feature as in the IO sector
Amundson Sea (Antarctic)	Strong interannual variation in both sets, slight but opposite trends
Weddell Sea (Antarctic)	Positive trend in GISST because of greater ice in 1990's
ANTARCTIC OCEAN	More ice and a big jump starting in 1989 in the GISST data leading to a positive bias in GISST

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.

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 masks.

Creation of the SST BCSOBS

• 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)

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

We first define two SSTs :1) SST_{SIC Mask} = 271.38 K ; and 2) SST_{SIC Mask +} = 271.48 K for the rectification. In the final SST, all points in the SIC mask region will equal SST_{SIC Mask} , but first all SST below SST_{SIC Mask +} are set to SST_{SIC Mask +} to eliminate excessively cold points. Next, all points in the glacier regions and the SIC mask region are set to SST_{SIC 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) SST_{SIC 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 (SST_{SIC Mask+}).

Differences in the annual cycle in the ice margins

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:

 
 

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

NOV

DEC

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 SST_{SIC 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 few points.

The corrected 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,

• Consistency with the NCEP and ECMWF reanalyses
• Consistency in time
• A physical consistency between SST and SIC

AMIP II BCSOBS Graphics

AMIP II BCSOBS Data

Conclusion

Your findings and discussion are needed to make this document a true community resource. Please forward them to Mike at fiorino@llnl.gov.

Last update 12 September 1997

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