El Niño-like weather increases California precipitation in response to warming
nature.com
nature.com
https://www.nytimes.com/2015/08/21/science/climate-change-in...
When I look at the weather patterns California has had over the last 5-10 years, I see something that fits within the historical norms for the state.
From the abstract:
"This uncertainty is related to several factors, including relatively large internal climate variability, model shortcomings, and because CA lies within a transition zone, where mid-latitude regions are expected to become wetter and subtropical regions drier."
So yes, it looks like CA is in for both. Increased aridity in the south, with more extreme precipitation hitting the north.
Is it because the atmosphere becomes a better heat sink so it can store more energy? And then consequently release more of it?
More specifically, extreme events in the moderate latitudes are explained by slowdown and amplification of Rosby waves: https://en.wikipedia.org/wiki/Rossby_wave
It seems to me that the patterns fit within the historical norms for over 100 years.
Further, because the lows are bound by 0, the annual average is rather deceptive.
They have no predictive power.
If that were the case, nearly all classic statistics would be powerless. Spoiler alert: it is not.
The global results are better than the regional results, but tend to run hotter than observations, suggesting they don't yet account for the full range of natural variability.
Statistics don't help you predict a coin flip (actually I think tails is ever-so-slightly more likely)
Edit: the point is not that a particular model can't make predictions. Some models will actually be correct. We just don't know which regional models are correct, and because of the range of results, they cannot all be correct. Therefore, as a whole, the regional models have no predictive power.
The stock market is a good example. Lots of people make lots of money betting on their models, but lots of people lose lots of money when their model stops working. There is also a general rising trend underlying the whole system.
The idea that a distribution doesn't have power because it is a distribution is a bizarre opinion to encounter in a community that prides itself on education, computer science, business acumen, machine learning, and informed decision making.
so I guess we have perfect models for economics and social issues and geopolitical issues now.....so that we can make any policy decision.
Uh... yes it does.
We have laws of large number and expected value. You just have to do a binomial distribution and use the data to fit your model.
Statistical learning is a thing. Element of statistical learning is a classic made by several famous statisticians who are responsible for LASSO, RIDGE, Elasticnet regressions.
Bayesian network is also a thing. Likewise with Bayesian Hierarchical Modeling (HM).
A coin flip is actually the easiest thing to predict and for advance statistical learning model such as HM they start out with Binomial Distribution and usually the example is a coin flip.
Why do you say this? For which coin?
Discussion of US penny:
http://www.smithsonianmag.com/science-nature/gamblers-take-n...
But with a coin flip, the initial side up is a bigger bias.
Statistics work really well when we understand the system, the initial state, inputs, and can accurately measure the results.
None of those apply to climate models, especially regional models.
Statistics make it easy to mislead yourself that the precision of your numbers implies correctness - like the joke, How do you know economists have a sense of humor? They use decimal points.
No. Never did.
> Statistics work really well when we understand the system, the initial state, inputs, and can accurately measure the results.
... This is actually the opposite of the truth. When we understand the system fully and can fully enumerate it's interal states and every relevant input, that's when there is no point in statistical modeling.
I really think you fundamentally misunderstood your statatistics education.
The predictions for the current solar cycle are a good example - the predictions were drastically off, the actual values outside the predicted error ranges from the statistical models. It was expected to be a more active solar cycle, based on our understanding of the system, its inputs, and the state from the previous cycle.
Because we didn't (don't) understand the system, our models were wrong.
If your model is not right, leaving out inputs, having the wrong range of variables, etc., it doesn't matter how good your math and statistics are, your model works...until it doesn't.
Then you get to do science and generate a new hypothesis, model that, and try again!
What you can't do is say in 2011, "Models show California will be drier because of global warming" then in 2017, "Models show California will be wetter because of global warming" and claim the models (both?!) have predictive power.
"This will not do. You never will be able to make both of them good for any thing. Take your choice, but you must be satisfied with only one. There is but such a quantity of merit between them; just enough to make one good sort of [model]" --Elizabeth Bennett
(And no, there's not nearly enough "drier" area in the new model to be compatible with the older predictions.)
> In response to increasing concentrations of greenhouse gases (GHGs), climate models from the Coupled Model Intercomparison Project (CMIP) versions 3 and 5 indicate decreases in precipitation in the subtropics and increases in middle to high latitudes.
Some of this depends on geography, and local terrain features. Lake effect precipitation or thermal activity against natural rock faces and outcroppings, which can change with tectonic activity. A silver lining is that new areas become eligible for rain forests, but only across geologic time scales. Not like next decade.
Desertification, on the other hand, progresses sometimes within a generation.
Mass death tends to move faster than flourishing growth, since growth explosions are rare outside of viral growth, algae blooms and invasive species (or biblical plagues like locusts) which tend to be bad things. Asian carp and huge jellyfish are neat, but humans dislike them, despite being generally successful amid new conditions or otherwise invasive.
It reminded me constantly of John Steinbeck on the drought cycle in the Salinas Valley, “During the dry years, the people forgot about the rich years, and when the wet years returned, they lost all memory of the dry years. It was always that way.”
For anyone who hasn't, I highly recommend checking out the drone footage of the Oroville dam. The scale of destruction was massive, and rumor has it even sparked a tiny gold rush after so much bedrock and sediment was churned up.
-- edit I'm not denying climate change, or that humans are a factor (even if relatively small).. I'm against pollution in general, And current projections to my understanding are about 50 years of minor cooling, followed by 150 years of warming to about 2 degrees hotter. It's not a "solved" issue. And I'm fine with anti-pollution stances and policies. I am not okay with using alarmist propaganda to sell anti-pollution, and then setting up systems to reward that behavior.
Bear in mind that the 2-degree figure is 2-degrees celcius __average__ global temperature, not two degrees maximum. The oceans are much cooler than land, and the further inland you go the hotter it gets, so at about 2-degrees average warming you've got maybe +3 degrees in Britain, and about +7 degrees Celcius in central USA, which would have catastrophic effects on food production.
Now add in the pressure of vast numbers of people migrating North away from the equator because they're all literally starving, and you've got a formula for civilisation-ending instability.
[EDIT] Also, bear in mind it's two degrees Celcius, which is about 3.6 degrees Farenheit, I've seen American friends get mixed up on that point, misjudging the severity by a factor of almost two.
GHG = Greenhouse Gas
This modeling prediction is in line with what we've observed:
* https://www.researchgate.net/publication/6191539_Detection_o...
* http://nca2014.globalchange.gov/report/our-changing-climate/...
Climate is not weather, climate is the patterns of weather that are seen. You can ask, "is this year a drought or a rain bonanza" and have it make sense, but it doesn't make sense to say that of the climate; both are possible at different times.
Just two years ago the news was all about how the CA drought was all because of carbon emissions and global warming. At least this paper does not do a full flip-flop and claim the recent precipitation is due to GW. It suggests the future may have higher or lower precipitation and large uncertainty - perhaps large variability due to the region being near a transition. Regardless of what its trying to say, it can be viewed through an AGW lens as "we fucked up last time, but here's why and BTW it's still all from CO2."
My take away is just that modeling this stuff is really hard and they aren't there yet.
For years CA has got worse and worse drought wise - one great year of rain that brought the state back from Exceptional and Extreme drought levels across almost the entire state to still Abnormal or Severe in about 25% of the state is really just a blip.
Like... one year is a data-point, not a pattern, and even if CA gets more rain in the winter it would appear to be getting hotter in the summer which is really not good.
Hasn’t really been the case this year so far.
Plus, again, the rain and weather in winter and spring is record breakingly anomalous this year. So "this year so far" is not really a sane comparison.
The trend overall has been clear. Using this year is like when people say "we had record snowfall in upstate NY this year, so much for global warming, nyuk nyuk". It doesn't follow.
Droughts and heavy rain fit those predictions perfectly.
Politics != Science
[1]https://en.wikipedia.org/wiki/Okavango_River [2]https://www.youtube.com/watch?v=UscvetL9xc0
On the ground it means we'll see more extremes. Droughts, floods, record breaking heat and cold.
That may be the point, but it's false; before, during, and after the worst of the droughts in CA, scientists were projecting that extreme (relative to past patterns) weather patterns would be more common, that particular regions would see more droughts and others more rain, and they California is geographically positioned so as to be subject to both effects, with an uncertain net effect.
The present article, particularly, does not “call for 100 years of floods”.
It doesn't take a scientist to understand that the more heat that gets trapped in our atmosphere, and the more glaciers and ice caps that melt, the worse off we are going to be. I don't need a scientist to tell me that California might have 100 years of this, or Sahara Desert might turn into a tropical rainforest. It is enough for me to know that this will have a devastating impact on ecological systems across the globe, and the vast majority of people live near water, and sea levels are rising.
I live in a state where the majority of the population is rural and most people are farmers. I ask climate change deniers if they "believe" in greenhouses, and they look at me like I am silly. Then I explain to them that what we are doing is creating a very big green house. This usually does the trick.
I tend to watch a few websites every day to keep tabs on what's happening. There is so much crap being said on both sides that I've reverted to just watching the numbers to see where we are.
My list: http://spaceweather.com/ to watch sunspot activity, http://wattsupwiththat.com/reference-pages/sea-ice-page/ to watch arctic sea ice area and extent and arctic mean temperature, http://www.drroyspencer.com/latest-global-temperatures/ for monthly temperatures, http://www.cpc.noaa.gov/products/analysis_monitoring/ensostu... to see where we are in the El Nino/La Nina cycle, http://sealevel.colorado.edu/ to see what the sea level graph is doing.
As a layperson, I always assumed that most weather we experience is in line with historical variability. Climate change causes (or is) small changes in the bias and variance of this weather. Saying that some of the drought is caused by natural variability and some of drought was exacerbated by GHG-induced warming seems like a perfectly consistent and reasonable position.
As a scientist you cannot accept that what you know is the final answer. Our knowledge of the world around us should be constantly subjected to the closest scrutiny, because the answers we have are almost certainly incorrect, or incomplete.
The scientific community is at great fault for not being loud enough about the impact of global warming.