Greater than 100% attribution

Greater than 100% attribution doesn’t return much from a Google search beyond hits from Climate Etc, ATTP and Real Climate. Seems to be a new idea. A related question is what about efficiencies greater than 1? That may bring some problems with some important laws.

50% represents natural factors:
<<<-50%—|100%|—150%>>>

Now remove the natural negatives:
<<<0%—|100%|—100%>>

Naturals turn positive:
>>>50%—-|100%|—-50%>>>

Seems natural factors are defining man made  ones.

<<<-$50k—|$100k|—$150k>>>

<<<$0k—|$100k|—$100k>>>

>>>$50k—-|$100k|—-$50k>>>

Two partners operate an income tax preparation business. In the second case one partners gets all the money. In the third, they split it evenly. In the first case, one partner keeps all the money and the other partner gives him $50,000. A partner paid out $50,000 in refunds for errors made but otherwise billed nothing. The net income is still $100,000. The successful partner got his $150,000 but there was only $100,000 earned. How do we allocate that income to the partners? One answer is <$50,000> and $150,000. Say the bad partner never existed. Our answers are then $150,000 with 100%, $100,000 with 100% and $50,000 with 100%. Replace the bad partner with a good partner clone. All percentages are now 50%. In no cases does the original good partner get any more or less then he earned himself. But his percentages are defined by his partner. In my example above, the bad partner netted to zero overall. And adding up all 3 scenarios means that on average, the good partner’s percentage was 100%. So when natural variability cancels out in the long run, the long term percentage is 100%. In another case, the partners kept no records of who did what, forgot what happened and only know there’s $100,000 in the bank account and it’s time to divide up the money. We know in theory one partner should’ve made so much as he has the plaques on the wall to prove it.

Sandbags for increased traction

Vehicle Weight

“Earlier we noted that the kinetic energy of a vehicle was, in part, dependent on the weight of the vehicle; the heavier the vehicle, the more energy it will have at a given speed. It might seem logical, therefore, that a heavier vehicle may require more distance to skid to a stop than a similar, but lighter vehicle. Contrary to this line of thought, the increased friction generated by a heavier vehicle in a skid, directly compensates for the fact that the heavier vehicle initially had more energy. A heavier vehicle may indeed be more difficult to “lock-up” than a lighter vehicle, but once in a skid, the heavy and light vehicles will require the same distance to stop from the same initial speed. For this reason, vehicle weight is not included in the skid-to-stop velocity formula.

V= / 255 m S

where:

V is velocity (km/h)

m is the friction coefficient

S is skidmark length (metres)”

m is low maybe .1 but assumed constant for comparisons. V then controls S. The engineer says, more mass equals more friction but it all nets to no effect on stopping distance. Loss of m means all attempts to change velocity and/or direction are impaired. Low m means all command inputs to the vehicle must be softened. Assuming m of .7 for dry pavement and .1 for icy pavement, control inputs are 1/7th as much as on ice. Which reminds me of the statement, Don’t ask the truck to do what it cannot do. The truck can still do 65 on a straight. It can only slow or turn 1/7th as well. Its stability is probably only 1/7th of normal. Kind of nuts we even drive at 65.

Understanding Synchronized Chaos

“Low correlation implies sustained output. High correlation implies big peaks and troughs.”

“For people new to wind power, a low correlation is good. A high correlation is bad. Why? If you have 1000x 3MW wind turbines and the correlation of output power between the turbines is high then they will be producing 3GW some of the time, 1.5GW some of the time, and 0GW some of the time – their output power rises and falls in unison.”
http://scienceofdoom.com/2015/09/19/renewables-xii-windpower-as-baseload-and-supergrids/

His first above statement applies to synchronized chaos I think. Low correlation is our average weather. High correlation is the synching that accompanies regime changes. It would apply to a market bubbles as well where everyone wants to buy and then sell. Does weather literally synch? A few nights ago my small lake finally froze over, going from 1% to 100% coverage. I’d guess it happened over 6 hours. Weakly correlated surface water all did something it does once a year, at the same time. It correlated for a few hours and the lake entered its Winter regime. Correlation would also apply to ice sheets. Gains equal losses, that’s weakly correlated. High correlation is significant sustained gains or losses. Some water is now doing something that usually just averages out.