1. Climate Modeling Idania Rodriguez EEES PhD Student Idania Rodriguez EEES PhD Student “Science Explorations Through the Lens of Global Climate Change” Workshop

2. Outline Introduction Motivation Building a Climate Model Model Complexity Model Uncertainty Model Validation Introduction Motivation Building a Climate Model Model Complexity Model Uncertainty Model Validation

3. What is a Climate Model? a mathematical representation of physical, biological and chemical processes that determine climate Mathematical Equations oOrdinary Differential  time-dependent oPartial Diff.  time- & space-dependent oParameterized  1 parameter in terms of 2+ oEmpirical  reproduce observed results Computers are needed to solve the equations arising from climate models Mathematical Equations oOrdinary Differential  time-dependent oPartial Diff.  time- & space-dependent oParameterized  1 parameter in terms of 2+ oEmpirical  reproduce observed results Computers are needed to solve the equations arising from climate models

4. Motivation Humans have affected and will likely continue to affect Earth’s climate Climate models can Help us understand sensitivities of climate system Project future changes from plausible scenarios Illustrate benefits of mitigating impacts caused by these potential changes  as an instrument of climate policy Humans have affected and will likely continue to affect Earth’s climate Climate models can Help us understand sensitivities of climate system Project future changes from plausible scenarios Illustrate benefits of mitigating impacts caused by these potential changes  as an instrument of climate policy

5. Global Mean Radiative Forcing by 2005 (Wm -2 )

6. Cloud Processes Surface Processes Radiative Processes Meteorological Processes Transport Processes Gas Processes Aerosol Processes Building a Climate Model Source: Adapted from IPCC, 2001a

7. Model Complexity From the simplest model… to an AOGCM From the simplest model… to an AOGCM Source: IPCC 2001 EiE E in E out Temp ave [CO 2 ]

8. Model Complexity How do these models differ? Space Resolution How do these models differ? Space Resolution Land/Ocean Atmosphere Energy & MassTransfer 2-Box No land/ocean distinction Land Atmosphere Ocean 3-Box No vertical or horizontal distinction

9. Model Complexity Inclusion/Complexity of Processes Input Properties Inclusion/Complexity of Processes Input Properties Multi-box multi-layer Land Atmosphere Ocean AOGCM Each 3D cell - few o of latitude & longitude / 10-40 layers

10. Model Complexity Space/Time Scale Inputs to a climate model depend on what we want to learn from it… Space/Time Scale Inputs to a climate model depend on what we want to learn from it… Million-year global projection Atmosphere Atmosphere Oceans Oceans Cryosphere (sea ice/glaciers) Cryosphere (sea ice/glaciers) Land surface & biota Land surface & biota Biogeochem cycles Biogeochem cycles Solar input long-term variation Solar input long-term variation Regional climate over next century Atmosphere Atmosphere Oceans Oceans Day to night variations Day to night variations More geographic detail of region More geographic detail of region More temporal detail of region More temporal detail of region

11. Model Complexity Sub-grid scale effects oPartial differential eqns arising from processes cannot be solved analytically oGCMs break the atmosphere, land and oceans on grid boxes & average processes to that scale to approximate solutions But many events relevant to climate happen on smaller scales, e.g. clouds Solution: treat implicitly using “parameterizations” Sub-grid scale effects oPartial differential eqns arising from processes cannot be solved analytically oGCMs break the atmosphere, land and oceans on grid boxes & average processes to that scale to approximate solutions But many events relevant to climate happen on smaller scales, e.g. clouds Solution: treat implicitly using “parameterizations” Grid sky half- covered by clouds Grid sky uniformly lightly covered - 1/2 sunlight

12. Model Uncertainties Future emissions

13. Model Uncertainties Approximations/Simplifications to Chemical, Physical & Biological Processes Example: GATOR-GCMM includes organic chemistry using a condensed mechanism OLE + O 3  CH 3 CHO + H 2 COO + XOP vs. C 2 H 4 + O 3  HCHO + H 2 COO (explicit) Using numerical methods to solve ODEs/PDEs instead of analytical solutions Sub-grid scale effects / Parameterizations Approximations/Simplifications to Chemical, Physical & Biological Processes Example: GATOR-GCMM includes organic chemistry using a condensed mechanism OLE + O 3  CH 3 CHO + H 2 COO + XOP vs. C 2 H 4 + O 3  HCHO + H 2 COO (explicit) Using numerical methods to solve ODEs/PDEs instead of analytical solutions Sub-grid scale effects / Parameterizations C=C

14. Model Uncertainties Poorly understood phenomena o Cloud effects on climate (aerosol indirect) Poorly understood phenomena o Cloud effects on climate (aerosol indirect)

15. Model Validation How can we know the model works? Diverse Techniques Climatic response to volcanic eruptions How can we know the model works? Diverse Techniques Climatic response to volcanic eruptions Volcanoes effect on global temperature Black: actual temperature Yellow: 14 different models Red: average of all simulations Source: AR4WG1, IPCC, 2007

16. Model Validation Modeling past climate trends Source: IPCC, 2001

17. Model Validation Other Techniques Modeling seasonal temperature variations Comparing to geographical patterns oPrecipitation Comparing to effects of climate predicted oAnimal & plant response to climate change Other Techniques Modeling seasonal temperature variations Comparing to geographical patterns oPrecipitation Comparing to effects of climate predicted oAnimal & plant response to climate change

18. Questions? Thank you for your attention