PIOMAS Arctic Sea Ice Volume Reanalysis

Fig.1  Arctic sea ice volume anomaly from PIOMAS updated once a month. Daily Sea Ice volume anomalies for each day are computed relative to the 1979 to 2016 average for that day of the year. Tickmarks on time axis refer to 1st day of year. The trend for the period 1979- present  is shown in blue. Shaded areas show one and two standard deviations from the trend. Error bars indicate the uncertainty of the  monthly anomaly plotted once per year.

Annual Cycle of Ice Volume Anomaly

Fig. 2 Total Arctic sea ice volume from PIOMAS showing the volume of the mean annual cycle, and from 2010-2017. Shaded areas indicate one and two standard deviations from the mean.

 Monthly Ice Volume

Fig.3 Monthly Sea Ice Volume from PIOMAS for April and Sep.

 Daily Average Ice Thickness

Fig 4.Average Arctic sea ice thickness over the ice-covered regions from PIOMAS for a selection of years. The average thickness is calculated for the PIOMAS domain by only including locations where ice is thicker than .15 m.

Monthly September Ice Thickness

Fig 5. Monthly average sea ice thickness in September 2016 from PIOMAS. Click for Animation from 1979 to 2016

PIOMES Ice Thickness Anomaly

Fig 6. PIOMAS Ice Thickness Anomaly for June 2017 relative to 2000-2015.

 Fig 7. PIOMAS Sea Ice Motion Anomaly for January-March 2017 relative to based period 2000-2017


Fig 8. CryoSAT AWI Ice thickness anomaly for April (relative to 2011-2015). Data courtesy Stefan Hendricks (AWI)

Fig 9. Time series of PIOMAS and CryoSAT AWI Ice Volume for April

Fig 10. Time series of PIOMAS and CryoSAT AWI Ice Volume for January

Arctic Sea Ice Volume Anomaly

Sea Ice Volume is calculated using the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS, Zhang and Rothrock, 2003) developed at APL/PSC.  Anomalies for each day are calculated relative to the average over the 1979 -2016 period for that day of the year to remove the annual cycle. The model mean annual cycle of sea ice volume over this period ranges from 28,000 km3 in April to 11,500 km3 in September.  The blue line represents the trend calculated from January 1 1979 to the most recent date indicated on the figure.  Shaded areas represent one and two standard deviations of the residuals of the anomaly from the trend in Fig 1 and standard deviations about the daily 1979-2016 mean in Fig 2.

Average Arctic sea ice volume through June 2017 continues to be lower than all prior years.  June 2017 ice volume was 15.4 km3 km about 500 km3 below the previous record from  June 2012 and about 1000 kmbelow June 2016.  June  volume was 48% below the maximum June ice volume in 1979,  34% below the 1979-2016 mean, and more than 1. 0 standard deviations below the long term trend line. By the end of June, ice volume in 2017 was nearly identical to 2012 indicating a slowing of the ice loss relative to prior years when compared to the earlier months of 2017.

Average ice thickness in June 2017 over the PIOMAS  domain is also the lowest on record (Fig 4.) Average ice thickness from PIOMAS is 10 cm thinner than the last few years and about 120 cm thinner than in 1980.   Note that the interpretation of average ice thickness needs to take into account that only areas with ice thickness greater than 15 cm are included so that years with less total volume can have a greater ice thickness.

Fig 6. Shows a map of the ice thickness anomaly for June  2017 relative to the 2000-2015 base period. Sea ice is thinner almost everywhere except for a persistent thick area reaching north of Svalbard through Fram Strait. This area continuing to  move southward. As in March through May, June 2017 also shows small positive thickness anomalies in Davis Strait. The Eastern Beaufort positive anomaly that was associated with anomalous cyclonic flow, has largely disappeared. A positive ice thickness anomaly has appeared in the Laptev Sea.    A strong negative thickness anomaly is present in the southern Chukchi where the ice is melting out earlier than normal.  Some of the sea ice thickness anomalies seem to be associated with ice motion anomalies showing a counter clockwise pattern centered on the North Pole (Fig 7). Similar thickness anomaly patterns are present in CryoSat data however the area of thick ice reaching from the pole to the Chukchi sea in CryoSat is missing in PIOMAS.  The area of thick ice north of Greenland in PIOMAS is not present in CryoSat (Fig 8). Overall PIOMAS sea ice volume is a bit larger than CryoSat in April (Fig 9) and variability correspondence is less than for January (Fig 10.)

An animation showing the 1979 -2016 September ice thickness evolution can be found here.

Updates will be generated at approximately one-month intervals.


Sea ice volume is an important climate indicator. It depends on both ice thickness and extent and therefore more directly tied to climate forcing than extent alone.  However,  Arctic sea ice volume cannot currently be observed continuously.  Observations from satellites, Navy submarines, moorings, and field measurements are all limited in space and time.  The assimilation of observations into numerical models currently provides one way of estimating sea ice volume changes on a continuous basis over several decades.   Comparisons of the model estimates of the ice thickness with observations help test our understanding of the processes represented in the model that are important for sea ice formation and melt.


Version 2.1

We identified a programming error in a routine that interpolates ice concentration data prior to assimilation. The error only affected data from 2010-2013. These data have been reprocessed and are now available as version 2.1. Ice thickness is generally greater in the Beaufort Chukchi Sea area with the largest differences in thickness during May. Differences in ice volume are up to 11% greater in late spring.

Fig 5. shows the differences in volume between Version 2.0 and Version 2.1 (click to enlarge)

Version 2. 0

This time series of ice volume is generated with an updated version of PIOMAS (June-15,2011).  This updated version improves on prior versions by assimilating sea surface temperatures (SST) for ice-free areas and by using a different parameterization for the strength of the ice. Comparisons of PIOMAS estimates with ice thickness observations show reduced errors over the prior version.  The long term trend is reduced to about -2.8 10km3/decade from -3.6 km3 103/decade in the last version. Our comparisons with data and alternate model runs indicate that this new trend is a conservative estimate of the actual trend.  New with this version we provide uncertainty statistics. More details can be found in Schweiger et al. 2011.  Model improvement is an ongoing research activity at PSC and model upgrades may occur at irregular intervals.  When model upgrades occur, the entire time series will be reprocessed and posted.

Model and Assimilation Procedure

PIOMAS is a numerical model with components for sea ice and ocean and the capacity for assimilating some kinds of observations. For the ice volume simulations shown here, sea ice concentration information from the NSIDC near-real time product are assimilated into the model to improve ice thickness estimates and SST data from the NCEP/NCAR Reanalysis are assimilated in the ice-free areas.  NCEP/NCAR reanalysis SST data are based on the global daily high-resolution Reynolds SST analyses using satellite and in situ observations (Reynolds and Marsico, 1993; Reynolds et al., 2007). Atmospheric information to drive the model, specifically wind, surface air temperature, and cloud cover to compute solar and long wave radiation are specified from the NCEP/NCAR reanalysis. The pan-Arctic ocean model is forced with input from a global ocean model at its open boundaries located at 45 degrees North.


Model Validation and Uncertainty

PIOMAS has been extensively validated through comparisons with observations from US-Navy submarines, oceanographic moorings, and satellites. In addition model runs were performed in which model parameters and assimilation procedures were altered.  From these validation studies we arrive at conservative estimates of the uncertainty in the trend of  ± 1.0 103 km3/decade. The uncertainty of the  monthly averaged ice volume anomaly is estimated as ±0.75  103 km3. Total volume uncertainties are larger than those for the anomaly because model biases are removed when calculating the anomalies. The uncertainty for October total ice volume is estimated to be  ±1.35 103 km3 .  Comparison of winter  total volumes with other volume estimates need to account for the fact that the PIOMAS domain currently does not extend southward far enough to cover all areas that can have winter time ice cover.  Areas in the Sea of Okhotsk and in the Gulf of St. Lawrence are partially excluded from the domain.  Details on model validation can be found in Schweiger et al. 2011  and (here). Additional information on PIOMAS can be found (here)

A comprehensive library of sea ice thickness data for model validation has been compiled and is available (here)

Perspective: Ice Loss and Energy

It takes energy to melt sea ice. How much energy? The energy required to melt the 16,400 Km3 of ice that are lost every year (1979-2010 average) from April to September as part of the natural annual cycle is about 5 x 1021 Joules. For comparison, the U.S. Energy consumption for 2009 (www.eia.gov/totalenergy) was about 1 x 1020 J. So it takes about the 50 times the annual U.S. energy consumption to melt this much ice every year. This energy comes from the change in the distribution of solar radiation as the earth rotates around the sun.

To melt the additional 280 km3 of sea ice, the amount we have have been losing on an annual basis based on PIOMAS calculations, it takes roughly 8.6 x 1019 J or 86% of U.S. energy consumption.

However, when spread over the area  covered by Arctic sea ice, the additional energy required to melt this much sea ice is actually quite small. It corresponds to about 0.4 Wm-2 . That’s like leaving a very small and dim flashlight bulb continuously burning on every square meter of ice. Tracking down such a small difference in energy is very difficult, and underscores why we need to look at longer time series and consider the uncertainties in our measurements and calculations.


The reprocessed PIOMAS ice volume data (version 2.1)  are  available (here).

How to cite PIOMAS Ice volume time series

Volume time series and uncertainties:

Schweiger, A., R. Lindsay, J. Zhang, M. Steele, H. Stern, Uncertainty in modeled arctic sea ice volume, J. Geophys. Res., doi:10.1029/2011JC007084, 2011

Model details:

Zhang, J.L. and D.A. Rothrock, “Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates“, Mon. Weather Rev., 131, 845-861, 2003


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