Welcome! You have found the home page of Brian Rose, Assistant Professor in the Department of Atmospheric and Environmental Sciences at the University at Albany.
Our group works on the fundamental dynamics of the climate system, particularly coupled atmosphere-ocean interaction. We are primarily a theoretical group and use a variety of numerical and mathematical modeling.
You can find links above to teaching, publications, code, etc. Newsworthy and noteworthy things will be posted here in our blog. Thanks for visiting!
I just completed 1000 miles on my e-bike
Back in April 2017 I bought a brand-new Trek XM 700+. It's an e-bike with a small electric motor and lithium-ion battery pack that helps me keep up with traffic, climb hills, and battle headwinds with ease. I bought this beautiful but expensive machine in order to spend less time driving and more time biking.
I have always loved riding bikes, but the 15 mile round-trip commute between my home in Delmar and the UAlbany campus was a challenge for me. On my old road bike, I was rarely doing more than 2 days a week of bike commuting. That's partly because my body complained about it, and partly because my commute time was substantially longer on bike than in the car (and I usually arrived at work pretty much soaked in sweat).
Today I reached what feels like a significant milestone: 1000 miles on the e-bike since I bought it six and a half months ago. In a nutshell, my plan has worked. The new bike is comfortable and fast and capable, and now I ride it nearly every day (except when it's pouring rain, and during my recent bout of pneumonia which made it hard to breathe). My bike-versus-car commute times are now comparable, and I'm less sweaty and gross.
The 1000 mile mark got me to wondering about how to properly account for the CO2 emissions caused by my personal commuting choices. The e-bike is a bike first and foremost, so I assumed that its carbon footprint was negligible. But if this correct? I plug the bike in every night to recharge, and this consumes electricity that is generated in part by fossil fuel combustion.
Questions for today
I set out to answer the following:
- How much CO2 emission is attributable to charging the e-bike over those 1000 miles?
- How much CO2 would have been emitted if I had driven my car instead?
- What if I had continued to ride my old zero-emission road bike occasionally instead? Have my net emissions gone up or down?
Carbon footprint of driving my car
I'll start with Question 2 because it's a bit simpler. My commuting car is a 2008 Honda Fit that gets about 32 mpg with my typical mix of highway and suburban driving. My round-trip commute is about 18 miles. So the daily commute consumes 18 miles / 32 mpg = 0.5625 gallons of gasoline.
The EPA provides standards numbers for CO2 emission per gallon of gasoline. The answer is 8.887 kg CO2 / gallon
So my daily commute in the Honda Fit produces 5 kg CO2.
Now the careful reader will have noticed that I claimed a 15 mile bike commute and 18 mile car commute. That's a fact of life around here (and probably in most urban / suburban environments) – there are lots of shortcuts you can take on a bike that cut down on the distance.
So the fair comparison for the 1000 miles of biking is actually to 1200 miles of driving. Those are the miles I avoided in the car since April.
Thus the gasoline emissions avoided by riding the e-bike amount to 1200 miles / 32 mpg = 37.5 gallons of gasoline = 333 kg CO2.
The answer to Question 2 is 333 kg CO2.
Carbon emissions from electricity generation in NY
Working toward answering Question 1, let's first deal with the CO2 emissions associated with generating electricity. The answer to this question varies enormously depending on the power source, of course, and so varies from state to state.
Detailed data are available from the US Energy Information Administration. Using their latest data for 2015, CO2 emissions from electricity generation in NY state is 32,731 thousand metric tons. The net generation is 138,627,721 MWh, and total retail sales are 148,913,655 MWh. (These numbers differ because NY imports substantial electricity from elsewhere).
This works out to 0.236 metric tons CO2 per MWh electricity generated in NY. We'll use this number and assuming that the CO2 emission per MWh is not substantially different for the imported electricity. (Interesting side note, this rate and also the absolute CO2 emission from electricity generation in NY state has declined substantially over the past 30 years – mostly due to a shift from coal to natural gas.)
Since 1 metric ton is 1000 kg and 1 MWh is 1000 kWh, the answer is also 0.236 kg CO2 per kWh produced.
Calculating the carbon footprint of my e-bike
Armed with the above result of 0.236 kg CO2 per kWh electricity produced in NY state, I want to calculate the CO2 footprint of my 1000 miles of e-bike riding. Things I need to know include:
- Amount of stored battery energy consumed
- Amount of electricity actually consumed during recharge (including charging losses)
- Amount of electricity generated per unit electricity consumed at my house (basically transmission losses)
Here my numbers are less firm. But I'll start with the battery. It's rated at 396 Wh (watt-hours). Assuming for the moment that the charging loss is zero, then a full charge costs 0.4 kWh.
I typically use less than half a full charge on each commuting day. I'm going to estimate that under my typical riding conditions I get about 40 miles per charge. So the efficiency is 40 miles / 0.4 kWh = 100 miles per kWh consumed.
I do not know at this point how to properly account for transmission and charging losses. I'm going to just make a blind guess and say it's on the order of 30%. That is, I'm going to assume that 1.3 kWh must be produced in order for me to consume 1 kWh by riding the e-bike.
So using the above number, my 1000 miles of riding consumed 10 kWh electricity, which required the generation of 13 kWh. Now we just multiply this by 0.236 kg CO2 per kWh to get…
The answer to Question 1 is 3 kg. In other words, my e-bike use since April 2017 is responsible for the emission of 3 kg of CO2 to the atmosphere.
The relevant point: this is more than 100 times smaller than the emissions avoided by not driving my car to work.
Guesstimating what my emissions would have been if I hadn't bought that e-bike
Now I'm definitely more in "what if" territory, particularly because I never had a computer on my old road bike to measure my mileage! But my guess is that I'm doing something like 3 times more distance on the e-bike than I used to do, because I ride much more frequently and for longer trips.
So in this hypothetical, I did 333 miles on a zero-emission bike, and 667 * 1.2 = 800 miles in my car. This is 2/3 of the full 1200 mile distance assumed in our first calculation, so the answer to Question 3 is 222 kg CO2. Compare this to the 3 kg we just calculated for the e-bike commute.
My conclusion: for me personally, using a small amount of electricity on the e-bike to get myself riding more frequently has substantially reduced the CO2 emissions associated with my daily travels around town, by more than 200 kg.
The difference in emissions per mile is so great that the e-bike related emissions are indeed essentially negligible. The emissions from a single day of driving to work (5 kg) are larger than my entire season of e-bike use (3 kg). The key to reducing my personal commuting-related emissions is to cut down on miles driven, however possible.
Final thoughts – toward a comprehensive personal carbon footprint
I have focussed narrowly on questions about commuting and the 1000 miles of e-biking I just completed. I don't mean to imply that these numbers are a good measure of my overall energy consumption and carbon footprint. Not at all! Just guessing here, but it's likely that the two largest line items in my personal CO2 emissions budget are:
- air travel (I attend a lot of conferences…)
- heating my home (I live in a sprawling suburban house with poor 1960s era insulation)
Driving my Honda Fit is probably somewhere down the list. Now I'm interested in doing a more thorough personal CO2 audit. Maybe I'll report back on this later.
Comments and critiques of my methodology are welcome! Particularly if you have firmer information about accounting for transmission and charging losses.
Meanwhile, I'm going to keep putting miles on my beloved e-bike. See you on the roads.
Brian's latest paper (with colleagues Tim Cronin from MIT and Cecilia Bitz from UW) is called "Ice Caps and Ice Belts: the effects of obliquity on ice-albedo feedback". It has been accepted for publication in the Astrophysical Journal. The paper looks at the basic rules governing planetary ice extent on Earth-like exoplanets at different obliquities. Click here for a preprint of the accepted manuscript.
Obliquity is the angle of a planet's axis of rotation relative to its orbital plane. On Earth that angle is about 23.5º, and among other things, is the reason we have seasons. Something funny happens for planets at obliquity angles exceeding 55º. When you average over a whole year, the total amount of sunlight is largest at the poles and smallest at the equator.
This paper asks whether such a planet could exist with a stable, long-lived "ice belt" around the cold equator.
We take the well-known analytical "Energy Balance Model" from North (1975). This is one of the simplest models for the pole-to-equator surface temperature distribution and ice latitude on a spherical planet in the presence of poleward heat transport. To adapt this Earth-bound model to the exoplanet context, we do two things:
- Express the model equations non-dimensionally to identify the smallest set of independent planetary parameters
- Flip the model upside down for the case where obliquity exceeds 55º and the annual-average insolation gradient is reversed
We provide a complete analytical solution to the model valid for any obliquity. This solution enables some extensive analysis of the stability of ice caps and ice belts as a function of obliquity and other planetary parameters.
We find that the "ice belt" climate is easily destabilized by the ice-albedo feedback associated with albedo contrasts between the ice-free polar caps and the ice-covered equatorial regions. Consequently planets in a stable ice belt configuration should be substantially more rare than planets with Earth-like stable ice caps.
Cameron Rencurrel has successfully completed and defended his thesis for the MS (Master of Science) degree, and is the second graduate from our group! Cameron is staying to continue on to his PhD.
Cameron's thesis is entitled Understanding Climatic Adjustments to Variations in Tropical Ocean Heat Transport. It is a follow-up study to Rose and Ferreira (2013, J. Climate). The tropical oceans take up vast amounts of energy through air-sea heat fluxes, especially in the equatorial regions dominated by wind-driven upwelling of cold water. Over long time periods, this tropical heat uptake is roughly balanced by heat release from the ocean to the atmosphere in other regions closer to the poles.
Cameron has been studying how and why this energy transport by ocean currents affects the global climate. We subject an aquaplanet GCM to a large array of different spatial patterns and magnitudes of ocean heat transport, and look at how variations in the transport affect aspects of the time-mean climate. We find that an increase in poleward heat transport by the tropical ocean results in a warming of the extra-tropics, relatively little change in the tropical temperatures, moistening of the subtropical dry zones, and partial but incomplete compensation of the planetary-scale energy transport by the atmosphere. This compensation is partially carried out by changes in the tropical Hadley circulation, and is manifested in simultaneous changes in both the mass flux of the cell and its efficiency (the so-called Gross Moist Stability). These dynamical changes are strongly coupled to thermodynamic and radiative processes that govern the global warming.
These experiments provide new insight into mechanisms of past climate changes on Earth, which have been driven in part by tectonic changes in ocean basins and consequent changes in ocean circulation and heat transport.
Publications based on these results are in preparation.
Lance Rayborn has successfully completed and defended his thesis for the MS (Master of Science) degree, and is the first graduate from our group!
Lance's thesis is entitled Understanding the Dependence of Radiative Feedbacks and Clouds on the Spatial Structure of Ocean Heat Uptake. It is a follow-up study to Rose et al. (2014, GRL). Lance used a variety of analysis techniques including radiative kernels to carefully compare the response of several different climate models to specific imposed patterns of ocean heat uptake. In particular, the study aims to draw specific causal links between spatial patterns of heat uptake under transient global warming and cloud processes that shape the overall global climate sensitivity.
A publication based on these results is in preparation.
The paper is Rose and Rayborn, "The effects of ocean heat uptake on transient climate sensitivity". It deals with the phenomenon of time-dependent climate sensitivity, and explores some compelling new ideas about connections between the oceans, atmospheric radiation, and global cloud cover that determine climate sensitivity. Our paper includes substantial review as well as some interesting original results and speculations.
Climate sensitivity here means the amount of warming per unit additional energy flux into the system. When a comprehensive coupled climate model is subjected to a steady radiative forcing (such as an abrupt increase in atmospheric CO2), we typically find that the climate sensitivity increases with time as the model adjusts towards its new, warmer equilibrium state. Why is this? Are new positive feedback processes coming into play as the climate warms? Is it necessary to think about the climate system as fundamentally non-linear?
We argue for a simpler alternative view: the uptake of heat by the oceans tends to be localized to the subpolar regions, and this localized heat sink is something like 2x more effective at altering the global planetary temperature than CO2 – a result that was demonstrated in one of Brian's earlier papers. We show that an apparent increase in climate sensitivity over time is a natural consequence of the gradual waning of this high-efficacy ocean heat uptake as the climate system warms toward its new equilibrium temperature.
We also argue for a robust physical mechanism linking subpolar ocean heat uptake with changes in subtropical low cloud cover, mediated by changes in the stratification of the atmosphere. See also the recent paper by Rose and Rencurrel for more on this! Understanding the constraints how low cloud changes contribute to global warming (now and in the future) is one of the key goals of modern climate science. Our results suggest that some aspects of low cloud changes may be driven in systematic ways by patterns of heat fluxes in and out of the ocean. If this is true, it may mean that errors and uncertainties in future climate model projections may be more reducible and falsifiable than we thought.