Claims About the Nationwide Popular Vote, US Presidential Election

Hillary Clinton's apparent lead in the nationwide popular vote continues to grow as previously uncounted ballots get included. As the additional votes pile up, so do comments about unfairness in the US election system, since these votes will make no difference in the outcome. People are repeating their calls for repeal of the electoral college system. Although we need to work through this debate (again), I think our biggest problems lie not in the electoral college, but in the primary election system, but I digress. In full disclosure, I must admit that I personally could not get myself to vote for either of the leading candidates, and I did not favor any of the third party candidates either. I think we as a nation can do better.

I posed one contrarian view providing some reasoning for retaining the electoral college:
http://roundyeducationblog.blogspot.com/2016_10_01_archive.html

The point of this posting is to comment on how some Clinton voters argue that the system is unfair since she seems to be leading by millions of votes nationwide. If the election were held again, they might claim, given the same vote margins, but with the conclusion determined by the popular vote, that she would have won and would even have had a substantial mandate. This perspective is understandable, but not on firm logical ground.

The popular vote situation in the electoral college system would not be the same as the popular vote in an election set up from the start as one to be decided by the popular vote. There may be millions of conservative voters in Washington, Oregon, California, Illinois, New York, and Massachusetts (for examples), who didn't bother to vote in the present system because they did not see their votes as making any difference, because large majorities in these states vote for Democrats. One could claim the same thing about left leaning voters in red states. More such people may have voted in a different system. My point is that in the present system, the popular vote nationwide is meaningless and possibly misleading. Only if the election were posed from the start as determined by the nationwide popular vote would we actually know what that vote would be. These same claims would hold true if the tables were turned.

Both candidates ran knowing the electoral process. That framework determined their efforts and the outcome. For better or worse, Trump won in that framework.

We should be spending more of our time concentrating on how to make the nation function well regardless of who is leading it.

Paris Climate Agreement and the United States Election Result

Many people who are concerned about climate change view the election of Donald Trump with trepidation. He has denied that climate change includes a substantial response to human activities, arguing that it is a hoax, and he has vowed to open up more federal lands to oil and natural gas drilling. He will probably withdraw from the recent Paris climate agreement. Yet, even if he did all of these things and more, in terms of outcomes actually relevant to the portion of climate change induced by human activities, the talk may be all bark and little bite, and I think the fear is more political than based on facts. My reasoning is that market forces are already changing the carbon emissions patterns of the United States economy. Trends in the costs of different forms of renewable energy, trends in how we use renewables together with fossil fuels, and changes in the types of fossil fuels that dominate the market reduce the depth of my concern.

Most renewable energy sources are intermittent. In order to absorb their production into the electricity grid, we need either the capacity to store the energy when it is produced to release it when it is needed, or we need some backup energy supply that can respond quickly to make up the difference when renewable energy sources are insufficient. When renewables and storage are cheaper than fossil fuels, market pressures will phase out fossil fuels, in spite of any government action (or lack thereof). Some fossil fuels are dirtier than others. Natural gas is on the whole cleaner than coal, even after accounting for leaked methane. Natural gas power plants can also respond far more rapidly than coal power plants to volatility in electricity generation rates from renewables. The cheap natural gas available now due to the fracking boom offsets the market for coal and makes assimilating renewable energy into the grid easier. In fact, some renewable energy firms have used natural gas to allow them to guarantee to grid managers a certain amount of energy production: They provide renewable energy when it is available, and they make up the difference when renewables are insufficient by using fast response natural gas power. Eventually when low cost storage becomes available, the need for electricity from natural gas will diminish.

I am not worried about a dramatic increase in coal production or oil drilling in the Trump presidency, even if he relaxes federal constraints on drilling on federal lands. For now, a glut of oil production has made the price of oil so low that there will be little incentive for oil companies to rapidly expand to new fields. In the meantime, continued investment in private sector battery research for electric cars may lead to the breakthroughs needed to solve the renewable energy storage problem. Thus I think that the best way for government to catalyze transition to a market driven by renewable energy is not to fight fossil fuels, but to invest in development of energy storage technologies. When renewable energy is truly cheaper, the big money will move that way. 

A Discussion of the Young Science of Seasonal Forecasting

Every fall, clients expect private sector meteorologists to make forecasts for the upcoming winter season. These forecasts are intended to provide useful information about the average outcome over the season, such as whether it is likely to be warmer or colder than normal. Such forecasts can be difficult to make, and many forecasters make predictions based on tenuous claims.

Seasonal forecasting is complicated because the average weather in the upcoming season depends both on signals evolving on seasonal or longer timescales, such as El Niño and climate change, and on signals that were originally acting on short timescales but that cause longer lasting changes in the state of the atmosphere or ocean. Phenomena like El Niño provide a kind of anchor pattern to which the weather can often return throughout the season, occasionally allowing skillful predictions to be made on a seasonal level.

In contrast, relatively brief weather events like major midlatitude storms, hurricanes, or Madden-Julian Oscillation (MJO) events can displace warm air from its normal locations to higher latitudes, leading to blocking patterns that can sometimes hold on for weeks at a time, influencing weather around the world. Wind stress associated with weather events can also give the ocean a "kick", thereby moving warm or cold water around. Such "kicker" events can make conditions after them different from conditions if they had not occurred (such as by amplifying or breaking down El Niño). They can move the climate into a new state. Most such transitional events are not usually predictable more than a couple of weeks in advance. Events that can alter the subsequent mean state of the seasonal climate are characteristic of nonlinear or chaotic systems. The best we can hope for in predicting such events beyond part of the next month is to correctly predict whether such events are more or less likely under certain background conditions.

Seasonal forecasters use both global climate models and statistical methods in their quest. Climate models are usually not good at predicting weather event-driven changes in the mean state, but they are good at maintaining the present state. Statistical methods, including data mining in large climate datasets, allow us to find relationships between different signals across the climate system. For example, strong El Niño conditions might be correlated with reduced snow cover in Canada, which then affects the temperature in Canada and the United States. Correlated signals like Canadian snow cover extent and eastern United States temperature can sometimes be applied to make predictions. When the ground is covered with snow, sunlight gets reflected back into space. Snow also creates cold air by emitting longwave radiation into space.

With many interacting variables, however, it can be difficult to determine cause and effect. Usually real weather events respond to multiple interacting causes, some contributing more than others. Snow cover may be reduced because the background pattern favors import of warm air over the continent, which can sometimes continue even after a snow event. If an assumed cause (such as extensive snow cover) is present, but another major factor such as strong southerly wind is also present, using the snow cover alone to predict upcoming temperature patterns would lead to a bad forecast. During strong El Niño events, a rogue storm event might increase Canadian snow cover, contributing toward lower temperatures, but import of warm air characteristic of El Niño can overcome these cooling effects and even melt the snow. Without taking the likelihood of import of warm air into account, a forecaster might suggest a long-term shift in outcomes across the season due to increased snow cover when one might be unlikely.

It is easy and occasionally useful to blame a warm or cold weather event on climate change, El Niño, abnormal Arctic Ice cover, snow extent in Siberia, the distribution of rainfall in the tropics, the North Atlantic Oscillation, sudden stratospheric warmings, tropical cyclone recurvature, the Pacific decadal oscillation, the Madden Julian Oscillation, or many other possible mechanisms. Many forecasters have their favorite indicator. My favorite is organized rainfall patterns in the tropics. I emphasize this indicator not because I think that it causes every major outcome in the atmosphere, but because it constitutes a well-defined energy source that leads to stationary or propagating waves that communicate outcomes to different parts of the world. Tropical rainfall also includes many signals that tend to favor certain sizes, propagation speeds, and lifetimes, implying some level of consistency from event to event. Yet, although rainfall in the tropics does drive changes in patterns in atmospheric circulation, it also responds to that circulation, complicating forecasting because the quantity often labeled as the "effect" can lead to changes in the thing we labeled as the "cause".

Another scientist might prefer to track snow cover in Siberia as a favored indicator in a seasonal temperature forecast. When cold air is available in Siberia, it can be displaced across the frozen Arctic Ocean into Canada, leading to an available source of cold air for the eastern United States. Yet, the presence of snow cover in Siberia does not necessarily imply that cold air will ultimately reach the eastern United States for any extended period of time. The winter of 2015-2016 was an excellent example of an exception (at least considering the season as a whole). Snowcover accumulated early in Siberia, but that winter was the mildest on record across much of the eastern United States. That winter, the structure of the El Niño response pattern favored both extensive snow cover in Siberia in October and warm outcomes through most of the winter in eastern North America.

Many seasonal forecasters fall into the trap of identifying a tracer signal of future outcomes, while not understanding the different pathways whereby different outcomes can occur. Scientists like simple explanations. This type of reductionist thinking often leads to good understanding, even in climate science. In a linear system, the total is just the sum of the parts. On the other hand, in a nonlinear system like the atmosphere, reductionism can occasionally lead to large errors. Brief weather events can sometimes kick off long lasting changes. Interacting signals in the climate system can lead to outcomes that are different from the simple sum of the signals.

Bottom line: When considering a seasonal forecast, think about the arguments being made, the level of confidence in those arguments, and alternate pathways to different solutions. Skill of even the best forecast techniques is relatively poor. In the end, no matter how well informed your seasonal forecast might be, anticipate that occasionally, weather events will alter the signal over the course of the season, leading to seasonal averages that are different from what you anticipated. That outcome does not necessarily mean that the forecast was not based on good information--it might just mean that unpredictable signals in the climate system led to a different outcome.