 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 2023 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. |
 Fig. 2 Total Arctic sea ice volume from PIOMAS showing the volume of the mean annual cycle, and from 2011-2023. Shaded areas indicate one and two standard deviations from the mean. |
 Fig.3 Monthly Sea Ice Volume from PIOMAS for April and Sep |
 Fig 4 Comparison of Daily Sea Ice Volume Anomalies relative to 1979-2023.
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 Fig 5. 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. |
 Fig 6. PIOMAS Ice Thickness Anomaly for April 2025 relative to 2011-2023. |
 Fig 7. CryoSat 2 (AWI, v2.6) sea ice thickness anomaly for April 2025 relative to 2011-2023. |
 Fig 8. Time Series of CryoSat-2 ( AWI v 2.6) and PIOMAS Sea Ice Volume Anomaly for April. |
 Fig 9. Monthly average sea ice thickness in September 2016 from PIOMAS. Click for Animation from 1979 to 2017 |
 Fig 11. PIOMAS Annually Averaged Sea Ice Volume |
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 -2023 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 1979-2023 mean in Fig 2.
Annual Update
2024 finished with an annually averaged sea ice volume that was the 5th lowest on record with 13,600 km 3 with recent years all clustered closely together (See Fig 11). 2017 still holds the annual volume record with 12.800 km 3
April 2025 Monthly Update
Average Arctic sea ice volume in April 2025 was 21.65 103 km3. This value is the 2nd lowest on record for April, about 1. 103 km3 above the low record for April set in 2017. Monthly ice volume was 37% below the maximum in 1979 and 23% below the mean value for 1979-2024. Average March 2025 ice volume was 0.5 standard deviations above the 1979-2024 trend line. Ice growth for April 2025 was at the lower end of the recent decade (Fig 4.) as was the ice thickness. The ice thickness anomaly map for April shows several contrasting features. A negative anomaly in the Lincoln Sea northwest of Greenland and a positive anomaly further east north of the Canadian Archipelago (CA). The out of sync behavior between the different parts of the Last Ice Area (LIA) was previously noted (Moore et al. 2019). Another positive anomaly developed north of Bering Strait possibly setting up a repeat for last year’s sea ice anomaly that extended into the summer of 2024. The positive anomalies are also present in CS2 but the Lincoln Sea anomaly in CS2 is much more localized and there a positive anomaly is present further off shore. PIOMAS only average values in this place. Time series for April ice volume for the last 15 years show no obvious sign of a trend from either data source. The absence of a trends in the sea ice extent and volume time series over the last 10+ years has been the topic of some discussion and puzzlement including here. It highlights the fact that the Arctic is a relatively small area subject to substantial internal variability strongly affecting trends at decadal time scales (see also here). A new paper currently in review in a by England et al. shows that the continued absence of a trend for a 15-year period is consistent with model results from multiple climate models.
Updates will be generated at approximately one-month intervals.
Purpose
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.
Versions
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 -2.8 103 km3/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 November 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.
Data
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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