Ice Melt AWP

[Tealight]

A few people follow my NRT AWP site and use it as a predictor for future melt. The model though only calculates how much energy went into a grid cell and thus is only good to predict re-freeze. It does not give a good indication about the amount of ice that has melted. Ice-free regions can appear because the ice has melted, or the ice was transported away by winds and currents. If it gets compacted on the other side of the Arctic, it is harder to melt. A smaller area taken up by the ice means less sunlight is available to melt it. This phenomenon is already known in the compaction ratio (area/extent). During the summer, a low ratio will result in more melt.

The new Ice-melt AWP model better visualises this phenomenon by only accumulating AWP if the sea ice concentration (SIC) is above 25%. In case of an ice-free region driven by wind, the new model will remain at zero while the regular AWP model will accumulate huge amounts due to the low ocean albedo. In the Eastern Greenland Sea, the sea ice is continuously replenished and melting throughout the summer. On average this region melts the most ice.

Initially I intended to run this model just as described, but while the maps looked good the values for graphs were not helpful for determining strong melting seasons. Due to its size the central Arctic had by far the most accumulated AWP even though there is still little melt. Instead of an upwards trend in melting over the decades there was a significant downwards trend. This was not a model error. In the past there was simply more ice present to absorb more sunlight. Additionally, less greenhouse gases meant more of the absorbed energy was lost to space instead of melting ice.

To address these two issues, I decided to test the model with a “heat loss to space” component from my Sea Ice Forecast Model for the Sea Ice Prediction Network. The heat loss is 4-6 MJ/m2/day depending on a mean temperature and CO2 level. This level of heat loss is just a third of the measured outgoing longwave radiation for the Arctic.  Its purpose was to improve the forecast model, which is only a local energy model without any heat transfer from lower latitudes. Since the Ice Melt AWP model does not include any heat transfer either it seemed appropriate to use the same level of intensity.

The heat loss adjustment per CO2 level is necessary to avoid over prediction of ice loss in the 1980s and under prediction of ice loss in the 2000s and 2010s. In the model the late 2010s at around 410ppm CO2 have an 11% reduced heat loss compared to the early 1980s at 340ppm.
With these model additions the central Arctic now has the lowest ice melt AWP of all regions and the melting energy has a clear upwards trend over the decades. All years with the lowest sea ice extent/area (2007,2012,2016,2019) are also the ones with the highest Ice Melt values. Apart from comparison to other years I would not put any other use case into the presented values. Especially relating negative values with freezing conditions. It is still not even close to a volume model. For sea ice volume we already have PIOMAS, Cryosat2 and my AMSR2 Snow & Ice Volume.

Maps & Graphs:
https://cryospherecomputing.tk/melt-awp
Datasheets:
https://github.com/NicoSun/ScienceData/tree/master/Ice_Melt_AWP

NRT charts will follow soon

[Phoenix]

Always good to see someone pursuing new ways of analyzing things.

I'm going to ask a math question based upon the following math you shared in the AWP thread last week. The intention of the question is to understand the applicability limits of the model to the current high situation in the CAB.

Formula:
AWPdaily = ((1-SIC) * MJ) + 0.15 * MJ * SIC

Example:
MJ = 20
SIC = 75%
AWP = (1-0.75)*20 + 0.15 * 20* 0.75
AWP = 5 + 2.25
AWP = 7.25

The inference here is that solar energy input to areas with sea ice concentration (SIC) are only 15% of the amount of that with no sea ice concentration.

Since areas with no SIC are intended to represent open water with a known albedo of 0.06, then the energy input to ice covered areas would be (1-.06) * .15 = ~ .14

This seem to imply that an assumption that 86% of potential solar input is not being absorbed in the SIC positive areas either as a result of estimated albedo reflection or average cloudiness.

Oren has made what seems to be a pretty good case in the AWP thread that the AWP model assumptions are not representative of current summer conditions in the CAB with relatively low cloud cover and albedo probably in the 50% range.

Perhaps you can provide some insight as to whether the same AWP model assumptions are being carried over to this new model and how sensitive the model output might be to adjustments to actual current conditions.

[Rod]

<I get your point Rod, but please not like this. O>

[oren]

If it gets compacted on the other side of the Arctic, it is harder to melt. A smaller area taken up by the ice means less sunlight is available to melt it. This phenomenon is already known in the compaction ratio (area/extent). During the summer, a low ratio will result in more melt.

Tealight, thanks for putting your mind into developing more Cryosphere Computing tools. However, I humbly disagree with the above.  Energy taken up by the open ocean inside the Arctic Basin will certainly play a role during the melting season even if the ice is compacted elsewhere. Winds over warmed water will carry more energy into the ice pack, and any movement or dispersion of the compacted ice will result in enhanced melt thanks to the additional energy in the water. The scenario where the ice is compacted to one side of the Basin and is immobile for the next month or two while the water boils around it and there is absolutely zero wind is, well, highly improbable, especially considering the near constant movement evident in reality.
This was the reason behind the High Arctic AWP, which I think is a great tool.

While it is true that low area/extent has the potential for higher extent losses thanks to compaction, this still does not mean it has the potential for more actual melt - it depends on various other parameters.

What your new tool will measure in reality is the mainly the area/extent ratio, as more compacted years will artificially fall in the rankings. I find it much less useful than the original AWP.

[Tealight]

Since areas with no SIC are intended to represent open water with a known albedo of 0.06, then the energy input to ice covered areas would be (1-.06) * .15 = ~ .14

This seem to imply that an assumption that 86% of potential solar input is not being absorbed in the SIC positive areas either as a result of estimated albedo reflection or average cloudiness.

Oren has made what seems to be a pretty good case in the AWP thread that the AWP model assumptions are not representative of current summer conditions in the CAB with relatively low cloud cover and albedo probably in the 50% range.

I never implied that the NSIDC Sea Ice Concentrations are a perfect albedo measurement, far from it. It's just the best that's available to me. In order for the model to work at all we need an albedo measurement for the entire Arctic, every day and for at least a decade to get some long term trends. Satellite images can't provide this information at the same consistency and length.

Storing and computing the data on satellite images are also on an entirely different magnitude. Not something I intend to do on my home computer for daily updates.

What your new tool will measure in reality is the mainly the area/extent ratio, as more compacted years will artificially fall in the rankings. I find it much less useful than the original AWP.

Yes I see it as a better melt indicator than the compaction ratio.

While it is true that low area/extent has the potential for higher extent losses thanks to compaction, this still does not mean it has the potential for more actual melt - it depends on various other parameters.

Heat transfer only occurs on the surface of a body. If you have less surface area, even at the same volume then you energy exchange will be lower. Why do you think ice caps and ice sheets stick around for so long? The continents and oceans around them heat up every summer, but only a fraction of the energy gets to the ice.  The efficieny of energy transfer through the atmosphere is extremely poor.

Let's take the Barnes Ice cap as an example.
Surface area: 6000 km2
thickness: lets assume 0.5km
total volume: 3000km3

compared to this years north american snow cover:
extent: 12,000,000 km2
peak volume: 1600 km3

In just 3 month, from early April until end of June North America experienced snow melt equivalent to half of the Barnes Ice Cap volume. By the end of summer it would be gone if it would be spread out over 12 million km2. But because it's surface area is so low it still lingeres around a few millenia after the last ice age ended.

[oren]

That's true but - the Barnes ice cap does not float in salty ocean water, and it does not move with the winds and currents.
Sea ice surrounded by warmed water will have the nagging tendency to be transported into them.

[interstitial]

Tealight thanks for your site and the other contributions you make.
I wish to better understand your method. The old (I think it is the old one) AWP uses

AWP Daily = ((1-SIC) * MJ) + 0.15 * MJ * SIC as listed on your site.

Are you using MJ based on time of year and lattitude?

or MJ based just on time of year?

or is their some incoming solar radiation product I am unaware of?

On your new model what calculation are you doing to determine outgoing radiation?

CO2 levels and temperature are certainly important but I am curious how temperature is used and interacts with CO2 in your model. Would it make sense for outgoing radiation to be a fraction of incoming radiation and CO2 levels. Thanks for your response in advance.

[Tealight]

Are you using MJ based on time of year and latitude?
or MJ based just on time of year?

Both. My latitude steps are 0.2 degrees of latitude. Slightly smaller than the 25km*25km grid cells. Time of the year is every day.

On your new model what calculation are you doing to determine outgoing radiation?

(5.6703E-8*(Temp(K))^4)*0.25
https://en.wikipedia.org/wiki/Black-body_radiation#Stefan%E2%80%93Boltzmann_law

It is a quarter of a theoretical pure black body radiation for the 2000-2019 arithmetic mean of the DMI 80N temperature. The main purpose of this formula is to get a higher outgoing radiation during mid-summer, a lower one at the beginning of the summer with a gradual rise in April and May. The multiplicator 0.25 is purely chosen by me so that the values give accurate melt predictions in my sea ice forecast model. For this Ice Melt model, you can choose whatever you want. It only changes the absolute values. All graph shapes and percentage differences between years stay the same.

From space measured outgoing radiation is around 0.65 times theoretical pure black body radiation (clear sky). The other 0.40 my factor is lower by are heat import into the arctic from lower latitudes, too high albedo estimation from compact sea ice with melt ponds and likely some other factors. All of these factors are just average approximations. Every weather/climate model has generalisations because it is impossible compute the whole earth at a molecular level.

CO2 levels and temperature are certainly important, but I am curious how temperature is used and interacts with CO2 in your model. Would it make sense for outgoing radiation to be a fraction of incoming radiation and CO2 levels? Thanks for your response in advance.

This is only a local radiation model. No actual temperature or any other weather data.

[Phoenix]

I see that the new product is up at Nico's site here....

https://cryospherecomputing.tk/NRT-icemelt.html

Being CAB centric in my focus, the first place I looked is the CAB regional ice-melt energy anomaly for June and to see how the model stacked up to the record PIOMAS volume loss in the second half of June.

Nico's model showed an accumulated positive anomaly during the second half of June of 5-10 EJ vs the 20 year average. This is enough extra energy to melt an additional 15-30 km3 of ice (at 3 km3 per EJ).

The PIOMAS result showed losses of 100+ km3 more than other robust years, so the difference vs. the 20 year average would almost certainly be greater, perhaps in the range of 150 km3+.

PIOMAS is also just a model and also prone to error so nothing conclusive. But conceptually, it would be interesting to to understand a reconciliation of the differences. I'm guessing this is unrealistic, so I'm just thinking out loud. The daily PIOMAS decline during this period is documented in posts by gerontocrat and oren if that is helpful to explore the environmental conditions associated with the anomalous volume model  losses.