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 -2011 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 28,700 km3 in April to 12,300 km3 in September. The blue line represents the trend calculated from January 1 1979 to the most recent date indicated on the figure. Monthly averaged ice volume for September 2014 was 6,970 km3. This value is 40% lower than the mean over this period, 59% lower than the maximum in 1979, and 0.7 standard deviations above the 1979-2013 trend. While ice volume at the maximum during April was on par with the previous two years, reduction in ice volume during the summer months was less than in previous years. September 2014 ice volume showed the second increase since 2008 and was just slightly above 2009 levels. September 2014 ice volume is consistent with the long term decline of Arctic sea ice volume (Fig 3).
2014 ended with a December ice volume of 15 km3 which was roughly the same as last seen in 2006. This volume was 35% below the maximum in 1979 and 20% below the mean value over the 1979-2014 period.
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 1979-2013 mean in Fig 2. Average ice thickness in September 2014 over the PIOMAS domain was 0.30 m thicker than in 2013 and just below to its 2008 value (Fig 4.).
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 continous basis. Volume estimates using age of sea ice as a proxy for ice thickness are another useful method (see here and here). 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.
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 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)
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
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