1. METR112-Climate Modeling Basic concepts of climate system Numerical method and parameterization in the model Evaluation and sensitivity study of the model

2. Question from last week: Sun Spot are relatively dark areas on the surface of the Sun where intense magnetic activity inhibits convection and so cools the surface. The number of sunspots correlates with the intensity of solar radiation Foukal et al. (1977) realised that higher values of radiation are associated with more sunspots because the areas surrounding sunspots are brighter, the overall effect is that more sunspots means a brighter sun

3. How can you know the future climate and climate change?

4. Climate system http://www.usgcrp.gov/usgcrp/Library/nationalassessment/overviewtools.htm

5. Atmosphere: composition Even though with small percentage, trace gases such as CO 2 and water vapor act as very important gas composition in the atmosphere

6. Atmosphere: vertical structure Troposphere: where most weather processes take place Note: the height of tropopause is not the same everywhere. The tropopause is lower in high latitude than in tropics

7. Atmosphere: energy budget (Kiehl and trenberth 1997)

8. Atmosphere: general circulation Hadley cell Trade wind Westerlies ITCZ Subtropical high Strom track region Polar Hadley cell

9. Ocean: critical roles in climate system Physical properties and role in climate : The biggest water resource on earth Low albedo  excellent absorber of solar radiation One of the primary heat sources for atmosphere High heat capacity  reduces the magnitude of seasonal cycle of atmosphere Important polarward energy transport Large reservoir for chemical elements for atmosphere

10. Ocean: salinity distribution closely relates to precipitation evaporation From Pickard and Emery: Descriptive Physical Oceanography: An Introduction

11. Ocean: annual cycle of mixed layer In winter, SST is low, wind waves are large), mixed layer is deep In summer, (SST high  water stable), mixed layer is shallow. March is nearly isothermal in upper 100 meters. March-August, SST increases, (absorption of solar radiation). Mixed layer  30 m. August-March, net loss of heat, seasonal thermocline eroding due to mixing.

12. Ocean: surface currents – the gyres http://www.windows.ucar.edu/tour/link=/earth/Water/images/Surface_currents_jpg_image.html Wind drived Coriolis force and location of land affect current pattern Clockwise in NH, anticlockwise in SH The water of the ocean surface moves in a regular pattern called surface ocean currents. The currents are named. In this map, warm currents are shown I n red and cold currents are shown in blue.

13. Surface ocean currents carry heat from place to place in the Earth system. This affects regional climates. The Sun warms water at the equator more thanSun it does at the high latitude polar regions. polar regions. The heat travels in surface currents to higher latitudes.latitudes A current that brings warmth into a high latitude region will make that region’s climate less chilly. Role of ocean surface currents

14. Graph showing a tropical ocean thermocline (depth vs. temperature). Note the rapid change between 400 and 800 meters. Thermocline The thermocline (sometimes metalimnion) is a thin but distinct layer in a large body of fluid (e.g. such as an ocean or lake), in which temperature changes more rapidly with depth than it does in the layers above or below. In the ocean, the thermocline may be thought of as an invisible blanket which separates the upper mixed layer from the calm deep water below.

15. Ocean: thermocline When water is sufficiently cooled, at polar latitudes, by cold atmospheric air, it gets denser and sinks The vertical sinking motion causes horizontal water motion as surface waters replace the sinking water. The large-scale flow pattern that results from the sinking of water in the Nordic and Greenland Seas and around Antarctica is called the oceanic conveyor belt

16. Land: where most human impact are applied Lower boundary of 30% of earth surface lower heat capacity than ocean Higher variability in interaction with atmosphere than ocean surface Moisture exchange Albedo Topography forced momentum change Human impact directly change the land surface Release of CO 2 and other GHGs Release of Aerosol Change the Land surface cover UHI effect

17. The greenhouse gases act as insulation

18. Land: aerosols Aerosol: the small particles in the atmosphere which varying in size, chemical composition, temporal and spatial distribution and life time Source: volcano eruptions, wind lifting of dust, biomass burning, vegetation New result and great uncertainty of the effect of aerosol on climate Small aerosol reflect back the solar radiation Large aerosol can block longwave radiation

19. Land: Landuse changes Land-cover changes alter surface albedo and emissivity water uptake by roots leaf area index canopy interception capacity stomatal resistance roughness length …. These changes affect partitioning of surface energy fluxes boundary layer structure cloud and precipitation formation …. Urbanization is an example of landuse change

20. General climate model – an approach for the future climate Atmospheric GCM is first used in 1950s to predict short-time future weather GCM develops and performs continuously improving since then with helps from updating computational resources and better understanding of atmospheric dynamics Atmospheric and Oceanic Coupled GCMs (e.g., CCSM, HadCM, GISS, CCCS, CFS) are major ways to predict and project future climate A list of GCM and climate modeling programs http://stommel.tamu.edu/~baum/climate_modelin g.html

21. Regional climate model The first generation of regional climate model is developed by Dickinson et.al (1989) and Giorgi et. al (1990) due to the coarse resolution of GCM not able to resolve local process Second generation of RCM (RegCM2) is developed in NCAR (Giorgi et al. 1993) based on MM5 and improved boundary layer parameterizations Third generation of RCM (RegCM3) (Pal et al. 2007) is developed with various improvements in dynamics and physical parameterizations

22. http://www.usgcrp.gov/usgcrp/images/ocp2003/ocpfy2003-fig3-4.htm The past, present and future of climate models During the last 25 years, different components are added to the climate model to better represent our climate system

23. Climate Model NASA Earth Observatory Glossary http://earthobservatory.nasa.gov/Library/glossary.php3?mode=alpha&seg=b&segend=d A quantitative way of representing the interactions of the atmosphere, oceans, land surface, and ice. Models can range from relatively simple to quite comprehensive. Also see General Circulation Model. General Circulation Model (GCM) A global, three-dimensional computer model of the climate system which can be used to simulate human-induced climate change. GCMs are highly complex and they represent the effects of such factors as reflective and absorptive properties of atmospheric water vapor, greenhouse gas concentrations, clouds, annual and daily solar heating, ocean temperatures and ice boundaries. The most recent GCMs include global representations of the atmosphere, oceans, and land surface. Definition

24. Differences between Regional Climate Model (RCM) and Global Climate Model (GCM) 1.Coverage: for selected region, for the globe 2.Model resolution: finer resolution,coarse resolution 1 km-10km60-250km, or larger 3. Model components are different RCMGCM

25. Climate Model: Equations believed to represent the physical, chemical, and biological processes governing the climate system for the scale of interest It can answer “What If” questions for example, what would the climate be if CO2 is doubled? what would the climate be if Greenland ice is all melt? what………………………..if Amazon forest is gone? what…………………………if SF bay area population is doubled?

26. Numerical method: finite difference method i i-1 i+1 i+2 i-2..... Forward Backward Central Exact Definition of derivatives and approximations Forward differences Backward differences Central differences

27. Example: CCSM (Community Climate System Model) Community Climate System Model is a fully coupled climate model of spectral coordinate in the horizontal and 26 layers in the vertical direction. It contains of AGCM(CAM), OGCM(POP), land surface model(CLM) and sea ice model(CSIM). Each model component exchanges information with the others through a flux coupler (cpl) CAM: an improved version of CCM using hybrid coordinates and a Eulerian dynamical core which is separated from the parameterization package CLM: an successor from NCAR LSM by changing the biogeophysical, carbon cycle and vegetation dynamics parameterizations in the LSM POP: almost identical to LANL’s POP1.4.3, only minor changes are made to facilitate the original version server as ocean model component of CCSM2.0.1 CSIM: consists an elastic-viscous-plastic dynamics scheme, and ice thickness distribution, energy-conserving thermodynamics, a slab ocean mixed layer model, and the ability to run using prescribed ice concentrations cpl CISM POP CLMCAM atmosphere land ocean ice

28. Picture taken from http://www.ccsm.ucar.edu/models/atm-cam/ Hybrid Vertical coordinate

29. Model physics in CAM Surface Exchange Atm-Lnd Atm-Ocn Atm-Ice (Moist) precipitation Deep Shallow Stratiform condensation Radiation Shortwave Longwave Turbulence ABL Free atmosphere Zhang-McFarlane (1995) (1994) Hack Zhang et al (2003) Cloud fraction Collins (2001) (Monin-Obkhov similarity theory) ABL depth ( Vogelezang and holtslag 1996)

30. CLM: combination of BATS, LSM &Common Land Model 10 soil layers, up to five snow layers Prognostic variables are: canopy temperature, intercepted water by canopy, soil or snow temperature, water and ice mass in the soil or snow layer and snow layer thickness Mosaic land-cover Same surface data with LSM2, and similar parameterizations with Common Land Model

31. Mosaic sub-grid land-cover treatment Glacier Wet-land Vegetation Lake Grass Bare ground Crop Needleleaf

32. Water balance in CLM Surface evaporation TOPMODEL-like runoff scheme Canopy water budget Soil water budget Snow water budget

33. Canopy water budget: Precipitation arriving at canopy top Direct drainage Canopy drip Evaporation from canopy

34. Canopy temperature: R n,c – H c – L v E c = 0 Newton-Raphson method TcTc Soil and snow temperature: T soil, T snow Crank-Nicholson method F(x)+F’(x)(x n -x n-1 )=0 Radiation balance in CLM

35. Verify the predictions and statistics of predictions Compatibility with observations Various simulations to assure the agreement with basic theoretical understanding Model Inter-comparison studies Compare different models model evaluation-Model uncertainty

36. Multimodel ensembles show systematic discrepancies when comapared with observed mean temperature Lack of broad stratus decks Contours are observed mean surface temperature, color shading show discre- pancy calculated from multimodel ensembles. Typical model error (RMS error in multi-model ensem- ble) surface temperature field. Calculated from IPCC AR4 participating models. Source: Fig. 8.2 of IPCC AR4 chapter 8

37. Multimodel ensemble show significant errors in standard deviations of surface temperatures Contours are observed surface temperature variability,color shading show that of discre- pancy calculated from multimodel ensembles from observations. Source: Fig. 8.3 of IPCC AR4 chapter 8

38. Short and longwave radiation budgets show dominant RMS errors in tropical and subtropical regions based on 12 month climatology Curves show RMS errors in short wave (left panel) and long wave (right panel) radiation Source: Fig. 8.4 of IPCC AR4 chapter 8

39. Simulated precipitation show systematic biases Observed annual mean precipitation in cm Multimodel ensemble of annual mean precipitation in cm 1. Double ITCZ syndrome and lack of SPCZ structure 2. systematic southern hemisphere differences Source: Fig. 8.5 of IPCC AR4 chapter 8

40. Zonal mean wind stress on ocean surface is reasona- bly captured by multi-model ensemble mean quantity Source: Fig. 8.7 of IPCC AR4 chapter 8

41. Zonal mean SST show marginal errors using multi- model ensemble mean quantity Source: Fig. 8.8 of IPCC AR4 chapter 8

42. Different climate projection scenarios suggest unprece- dented increasing trend in global mean temperatures Source: Fig. 10.4 of IPCC AR4 chapter 10