Climate models

1. Climate models Nicholas Trank Popular Seminar 5/1/18

2. Outline • Introduction • Current challenges • Large-eddy simulations (LES) • Context for Climate Change • Conclusion 2

3. Introduction • Why do we care about weather? • Climate change • How does a climate model work? • Intro to cloud formation 3

4. Why do we care about climate models? • Climate models are used to predict all the weather we see around us every day • Having accurate models is important for a wide variety of applications • Not only day-to-day predictions, but also predictions about large-scale changes over the next hundreds of years 4

5. Climate change! • Two weeks ago, the Mauna Loa Observatory recorded its first-ever carbon dioxide reading in excess of 410 parts per million • Parts per million: measure of concentration of CO2 relative to other air molecules • Carbon dioxide hasn’t reached that height in millions of years, and signs indicate the levels are continuing to rise • Implications will be discussed later. First, let’s look at some of the basics of climate models. 5

6. How do climate models work? • Models use mathematical equations to characterize how energy and matter interact in different parts of the ocean, atmosphere, and land • Building a climate model requires identifying the important processes and the equations that represent them, adding initial conditions, and using powerful computers to repeatedly solve them • First type of climate model: General Circulation Model (GCM) 6

7. Main Processes • Atmospheric dynamics has a lot of complex processes to consider, but we can focus on some of the important ones • Solar heating • Low and high pressure system • Turbulent motion of air 7

8. How do climate models work? • We split up the three-dimensional space into a grid of cells. Each cell interacts with the cells adjacent to it, which models the exchange of matter and energy over time • The smaller the grid cell size, the higher the level of detail in the model 8

9. How do climate models work? • Time steps: we are also interested in looking at the evolution of the system over time • Time steps can be in minutes, hours, days, or years, depending on the detail that we want. • Smaller time steps give greater detail, but require much more computing power 9

10. How do climate models work? • Often, choosing parameters like time step and grid size depend on a trade-off between desired resolution and efficient use of computing time. • Many approximations are also made to the equations to reduce computing time, at the cost of reliability / accuracy 10

11. Cloud formation • Clouds are made of miniscule water droplets that are suspended in the air • Although clouds cover 70% of the sky, they contain very little water • If one took all condensed water in clouds and spread it as a liquid layer on Earth, one would get a film 0.1 mm thick 11

12. Cloud Formation • Main mechanism of formation: updrafts • Updrafts are columns of upward moving air • Warm air from the ground moves upward. Thermals can reach an altitude of 2 miles or more • If the updraft contains air that has a high water vapor concentration, the water vapor may condense once the air reaches a higher altitude and form a cloud 12

13. Turbulence • Turbulence is the chaotic changes in fluid motion, in particular with respect to pressure and velocity • We can see turbulence in the way water flows in the ocean, billowing storm clouds, or smoke from a chimney • Turbulence can be very important for describing eddy formation around updrafts 13

14. Turbulence • It is remarkably difficult to represent mathematically because it is so chaotic • Unfortunately, it is also important on certain length scales when considering atmospheric processes 14

15. Current Climate Models • Why do low-lying clouds pose a challenge to climate models? • Current state of climate models • How to identify clouds in data 15

16. Cloud Uncertainties • To predict how clouds respond to warming, we need to predict changes in the minuscule residual of water vapor that condenses when air ascends and cools in turbulent updrafts • Current climate models have a horizontal grid spacing around 50-100 km and a vertical grid spacing in the lower atmosphere of around 200 m • Too coarse! 10-100 m wide turbulent updrafts generate low clouds 16

17. Cloud Uncertainties • Advances in computing will eventually enable climate models to resolve the turbulence controlling clouds • But to resolve low clouds, climate models need grid spacings on the order of 10 m in the lower atmosphere • To achieve that, the number of atmospheric grid cells in climate models needs to increase by 100 million times • Not feasible with current computing resources, but perhaps eventually 17

18. Smaller Simulations • However, although GCM’s may be unable to currently model cloud formation, smaller scale simulations are able to effectively simulate them • Limited-area models called large-eddy simulations (LES) operate with a much smaller resolution, sufficient to resolve the 10-100 meter wide turbulent updrafts that generate low clouds • LES allows for much more accurate modeling of low-level cloud dynamics 18

19. LES • Large-eddy simulations are similar to GCM’s, and compute approximate solutions to the equations of fluid dynamics and thermodynamics • Enhanced computer performance has enabled both larger domains in LES and smaller grid spacings in GCM’s, and their convergence creates new opportunities for understanding low clouds • Type of LES depends on application 19

20. LES • We can embed LES in grid columns of global models. • The large-scale weather systems resolved in a global model drive the LES, and if desired, the LES can feed back into the large scale models • Numerical experimentation can determine useful approaches to relating different properties of the system 20

21. How to distinguish clouds in data • The distinction between updraft and environmental air is not made by nature, so we must apply some requirements to classify the domain • Three main requirements: • Updraft vertical velocity > 0 • Concentration of tracer is high • There must be liquid water 21

22. Decaying Tracer • A layer of tracer is released at ground level, and its location is monitored over time • If a large concentration is found at a high altitude, this means the air quickly rose to that level, suggesting the presence of an updraft • Since the tracer weakens over time, it can only be present at high altitudes if it rises quickly enough 22

23. Sample Tracer Levels 23

24. Some Important Properties • Rate at which air enters the updraft • Rate at which air leaves the updraft • Turbulent kinetic energy: measure of how much kinetic energy is stored in the turbulence of a system • Speed of air in 3 dimensions 24

25. Even More Simplified Model • Another type of model used is a single-column model (SCM) • This condenses LES to only the vertical direction, compressing a lot of the variables • These can be used to inform parameterizations 25

26. What are parameterizations? • In order to merge the output from LES into a GCM, we wish to find simplified approximations to atmospheric equations • Using LES output, SCM’s can be constructed that can then be placed into grid cells in the GCM • Approximations need to still maintain proper behavior, but reduce required computational power • For example: how the rate at which air enters an updraft is related to other properties of the system is still unknown 26

27. Summary of model types • GCM: large-scale, coarse resolution, 3-dimensional • LES: small-scale, fine resolution, 3-dimensional • SCM: small-scale, fine resolution, 1-dimensional Incorporate into Compare data with LES SCM GCM 27

28. Back to Climate Change • Equilibrium climate sensitivity (ECS): the global surface temperature increase that results after CO2 concentrations have doubled and the climate system has stabilized • ECS’s of current climate models are scattered between 2 and 5 degrees Celsius, and this wide range has not changed for 40 years • For context: a change of 1 degree Celsius in the global temperature would raise ocean levels by ~6 m. 28

29. Why so little progress? • The main source of uncertainty is how clouds respond to warming. • Low clouds cool the underlying surface by reflecting sunlight. More low clouds are generated when warmer conditions evaporate more water. • If they reflect more sunlight as the climate warms, they reduce the warming and lower ECS • If they reflect less sunlight, they amplify the warming and raise ECS 29

30. Example of Models 30

31. Predictions • Although the extent to which rising CO2 levels affect the environment varies by model,most predict: • Rising sea levels due to melting sea ice and thermal expansion • Warmer and stronger storm systems • Increases in precipitation • Larger shifts in weather patterns (more extreme) • Less oxygen in oceans, more acidic 31

32. Conclusions • Improvements to climate models would vastly benefit our ability to counter upcoming changes to our climate • Reducing uncertainties in climate projections has an economic value of trillions of dollars within the next decade, but there are only dozens of scientists working worldwide • Policies could better support efforts to Paris Agreement, an agreement within some countries in the UN to keep global temperature rise this century below 2 degrees Celsius 32

33. How to Reduce Uncertainties • Better representations of turbulence and low clouds are crucial • We also need a better understanding of the coupling between water and circulation • Observation data, global climate models, and LES can work together to help improve climate models and identify areas that still need more development 33

34. Questions? 34

35. Bibliography “Climate Models.” NOAA Climate.gov, climate.gov/maps-data/primer/climate-models. Accessed 30 April 2018. Kahn, Brian. “We Just Breached the 410PPM Threshold for CO2.” Scientific American, 21 Apr. 2017, scientificamerican.com/article/we-just-breached-the-410-ppm-threshold-for-co2/. Accessed 30 April 2018. Schneider, Tapio, et al. "Climate goals and computing the future of clouds." Nature Climate Change 7.1 (2017): 3. Stainforth, David A., et al. "Uncertainty in predictions of the climate response to rising levels of greenhouse gases." Nature 433.7024 (2005): 403. Stevens, Bjorn, and Sandrine Bony. "What are climate models missing?." Science 340.6136 (2013): 1053-1054. 35