Dynamics of Climate Variability Climate Change EESC W
Dynamics of Climate Variability & Climate Change EESC W 4400 x Fall 2006 Instructors: Lisa Goddard, Mark Cane Teaching Assistant: Philip Orton Sept. 5, 2006 EESC W 4400 x 1
Objectives: Knowledge • Understand fundamental physical processes underlying climate variability and climate change • Understand how models and predictions work • Understand important influencing factors (in models & predictions) and important assumptions/uncertainties Sept. 5, 2006 EESC W 4400 x 2
Objectives: Skills • Climate science literacy: Read with understanding (i. e. be able to summarize and interpret) articles on the topics covered in this course in journals such as Science and Nature. • Forecast interpretation: Identify influencing factors and uncertainties for climate predictions on time scales, from seasonal-to-interannual forecasts to climate change projections. Sept. 5, 2006 EESC W 4400 x 3
OUTLINE • “Climate” • Models • Systems and Feedbacks Sept. 5, 2006 EESC W 4400 x 4
Climate System Sept. 5, 2006 EESC W 4400 x 5
What is Climate? • Climate is the mean state of the environment, defined over a finite time interval, at a given location and time. - This state can be characterized by the mean values of a range of weather variables, such as wind, temperature, precipitation, humidity, cloudiness, pressure, visibility, and air quality. • The definition of climate also includes the typical range of variability in values of environmental variables (for example – the standard deviation of temperature). • A complete description of the climate system and the understanding of its characteristics and change require the study of the physical properties of the high atmosphere, deep ocean, and the land surface, and sometimes the measurement of their chemical properties. • The study of climate is a quantitative science, involving the understanding of the transfer of energy from the sun to the earth, from earth to space, and between atmosphere, ocean, and land, all under fundamental physical laws such as conservation of mass, heat, and momentum. Sept. 5, 2006 EESC W 4400 x 6
Sept. 5, 2006 EESC W 4400 x 7
Mean Temperature Field Sept. 5, 2006 EESC W 4400 x 8
Regional Temperature Variability Remove mean Sept. 5, 2006 EESC W 4400 x 9
Example: Time Scales of Variability Sept. 5, 2006 EESC W 4400 x 10
Modeling the Climate Sept. 5, 2006 EESC W 4400 x 11
Models • Conceptual Illustrate principal relationships or balances • Empirical/statistical Describe relationship between observed parameters (e. g. sea surface temperature and rainfall) • Numerical/dynamical Based on set of mathematical equations describing physical processes, that allow the system to evolve in time Sept. 5, 2006 EESC W 4400 x 12
How do we model climate? [physically] • Physical/dynamical equations - 3 -D equations of motion (conservation of momentum) - Continuity equation (conservation of mass) Thermodynamic equation (conservation of energy) Equation of state for air Balance equation for water vapor • Parameterizations Small-scale processes that are treated statistically and their effects related to average conditions over much longer periods of time and larger space scales e. g. clouds, radiative transfer, turbulence Sept. 5, 2006 EESC W 4400 x 13
Hierarchy of Climate Models (Physically-based) • 3 -D coupled ocean-atmosphere GCMs (CGCMs) • 3 -D atmosphere-only GCMs (AGCMs) • 2 -D(λ, φ) – “barotropic” or 2 -D(φ, z) – “Energy Balance” models • 1 -D(z) – “Radiative-Convective Models” (RCMs) or “Single Column Models” (SCMs) • 0 -D – Global-Mean Energy Balance Models Sept. 5, 2006 EESC W 4400 x 14
Weather & Climate Prediction Initial & Projected State of Atmosphere Climate Change Decadal Uncertainty Current Observed State Initial & Projected State of Ocean Initial & Projected Atmospheric Composition Sept. 5, 2006 EESC W 4400 x Time Scale, Spatial Scale 15
Systems & Feedbacks • Example 1: Albedo (daisies) & temperature “Daisyworld” Sept. 5, 2006 EESC W 4400 x 16
Example 1 (cont. ) Temperature as Function of Daisy Coverage Sept. 5, 2006 EESC W 4400 x 17
Example 1 (cont. ) Daisy Coverage as Function of Temperature Sept. 5, 2006 EESC W 4400 x 18
Example 1 (cont. ) Equilibrium & Stability Dmax x System of Equations: (1) (2) To x Sept. 5, 2006 D = Dmax – (T-To)2 (1) T = Tmax – αD (2) x Tmax EESC W 4400 x 19
Systems & Feedbacks • Example 2: Albedo (snow/ice) & temperature Snow/Ice coverage Surface temperature As temperature decreases, snow/ice coverage increases (less snow/ice melted, and more precipitation delivered in frozen form) As snow/ice cover increases, temperature decreases (albedo increases, so less solar energy is absorbed by surface) Positive feedback (Snowball Earth, Chp. 12 – Kump et al. ) Potential negative feedback: As temperature drops, atmosphere holds less H 2 O, and precipitation decreases. Also, ice may begin to sublimate. Sept. 5, 2006 EESC W 4400 x 20
- Slides: 20