Dynamics of Climate Variability & Climate Change Dynamics of Climate Variability & Climate Change EESC W4400x Fall 2006 Instructors: Lisa Goddard, Mark Cane Teaching Assistant: Philip Orton

Sept. 5, 2006EESC W4400x2 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, 2006EESC W4400x3 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, 2006EESC W4400x4 OUTLINE “Climate” Models Systems and Feedbacks

Sept. 5, 2006EESC W4400x5 Climate System

Sept. 5, 2006EESC W4400x6 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, 2006EESC W4400x7

Sept. 5, 2006EESC W4400x8 Mean Temperature Field

Sept. 5, 2006EESC W4400x9 Regional Temperature Variability Remove mean

Sept. 5, 2006EESC W4400x10 Example: Time Scales of Variability

Sept. 5, 2006EESC W4400x11 Modeling the Climate

Sept. 5, 2006EESC W4400x12 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, 2006EESC W4400x13 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, 2006EESC W4400x14 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, 2006EESC W4400x15 Weather & Climate Prediction Climate Change Uncertainty Time Scale, Spatial Scale Current Observed State Initial & Projected State of Atmosphere Initial & Projected Atmospheric Composition Decadal Initial & Projected State of Ocean

Sept. 5, 2006EESC W4400x16 Systems & Feedbacks Example 1: Albedo (daisies) & temperature  “Daisyworld”

Sept. 5, 2006EESC W4400x17 Temperature as Function of Daisy Coverage Example 1 (cont.)

Sept. 5, 2006EESC W4400x18 Daisy Coverage as Function of Temperature Example 1 (cont.)

Sept. 5, 2006EESC W4400x19 Equilibrium & Stability Example 1 (cont.) System of Equations: D = D max – (T-T o ) 2 (1) T = T max – αD (2) (1) (2) x x D max T max ToTo x

Sept. 5, 2006EESC W4400x20 Systems & Feedbacks Example 2: Albedo (snow/ice) & 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. Surface temperature Snow/Ice coverage