# Presentation: 558772

## Anatomy of a Successful Parameterization

**Anatomy of a Successful Parameterization**

**Philip Cameron-Smith**
***Lawrence Livermore National Lab.***
**Goals: **
**Convey needs of modelers**
**Encourage ASP parameterizations**

## Outline

**Life-cycle & anatomy of successful parameterizations**
**Examples of parameterizations under development at LLNL**
**My vision of the future: **
**Need tracers to validate emergent properties of GCMs.**

## Parameterizations take a long time to mature, and need to be simple

**GCM = Dynamics + parameterizations**
**10 years/day, 1 CPU, 1 Gflop, 100 km res. **
**	****→**** ****3 operations/parameterization**
**Generally takes years for parameterization to become part of IPCC runs:**
- Observations
- Box or column model
- Off-line model
- GCM
- Battle with other parameterizations (bake-off)
**Parameterizations need an advocate to succeed**

## Good computer-science required.

**Important effect on GCM**
**Fast **
**Scalable**
**Moderate data requirements**
**Well coded and documented:**
- Fortran90
- Comments in code
- Formal documentation
- Low-dimensional standalone code

## Parameterizations must be valid for all possible inputs. 

**Applicable everywhere on Earth**
**Applicable for altered climates**
- Physically based.
**Operates on many GCM scales**
- 10-1000 km;  minutes to millenia.
**Uses available quantities**
**Consistent with other parameterizations**
**Well validated**
- Validateable inside GCM.
**Few tunable parameters (zero is ideal).**

## Tuning is like “the Force”: it has good and  bad sides, and it holds GCMs together.

**Tuning **
- Measurement in a high dimensional chaotic space
- Single Pendulum = Elementary Physics
- Double Pendulum = Chaos
- Implicit & explicit tuning
- Explicit = Find parameter values to best fit obs.
- Implicit = Choose parameterization that gives best GCM 	fit to obs. (ie, includes compensating errors)
- Implicit = Stop looking for bugs when fit to obs. is good.
- Climateprediction.net
- Automatic differentiation

## The formation of secondary organic aerosols (SOAs) in global models requires parameterization.

- Jenkin

- Seinfeld & Pankow

- Many precursors

**Aromatics**

**Terpenes**

- Each can react with OH, NO3, O3 to form many products....

- O3 + -pinene,
- O3 + -pinene
**(1550 rxn; 520 species)**

- Which can be represented by a set of two product reactions....

**(1)  VOC + O****3**** ******
**          ****a****1****P****1**** + a****2****P****2**** + HVP**
**(2) P****1**** + O****3**** ******** **
**          ****b****1****P****3**** + b****2****P****4**** + HVP**
**......****.**
**(n)  ........**
**P****n**** = low vol. product**
**HVP = highly vol. product**

**Notes:**
Chemical mechanism is typically layers deep, and ‘n’ is small, ie less than 10.

## DOE ARM Project: Examine Aerosol Indirect Effects With a 3-D Cloud Resolving Model and ARM Data

- Develop more realistic cloud drop parameterizations with detailed aerosol microphysics.
- Apply these parameterizations into a 3-D cloud resolving model and validate with ARM measurements.
- Validate in GCM using the CCPP - ARM Parameterization Testbed (CAPT).
- Nucleated droplets = f(updraft vel., SO4 conc., tot. aerosol #)
- Old = fixed size dist.;  New = multi-modal size dist.

- Model simulated increases of drop concentrations (cm-3) by anthropogenic aerosols in clouds with T > 253oK. (Chuang et al., 2002)

## Solves for (1) condensational growth and (2) movement of mass in size coordinate
Particle phase state based on minimum Gibbs free energy
Aerosol Species
Current: OC, BC, Sea-Salt, Dust, Sulfate
Future additions: NO3-, NH4+, Na+, Cl-, reactive dust
Aerosol representation
Mass distribution, internally mixed, sectional 

- Solves for (1) **condensational growth** and (2) **movement of mass **in size coordinate
**Particle phase state** based on minimum Gibbs free energy
**Aerosol Species**
- _Current_: OC, BC, Sea-Salt, Dust, Sulfate
- _Future additions_: NO3-, NH4+, Na+, Cl-, reactive dust
**Aerosol representation**
- Mass distribution, internally mixed, sectional

**Notes:**
The aerosol module in IMPACT solves a diffusion equation for the transport of of species between particles and the gas phase.  An advection equation is also solved to shift mass in the size coordinate as the size distribution is altered from condensation/evaporation.  Turbulent transport and advection of particles in the atmospheric domain are solved using standard IMPACT operators (coagulation and nucleation are neglected because they do not significantly influence the mass distribution).

To solve for the transport of species between the particle and gas phase, the equilibrium partial pressure of species at the particle surface must be calculated.  The partial pressure is calculated by assuming that the interior of the particle is in equilibrium (i.e., the particle phase state is determined using a look-up table corresponding to minimum Gibbs free energy conditions).

The current aerosol module contains all of the species listed under “current” and “future additions” except for reactive dust.  However, all of these species are not presently considered in the IMPACT version of the aerosol module because the gas-phase chemistry we have been using does not contain the necessary compounds.  We are planning to update the chemical mechanism so that all species listed can be considered.   

## Significant Experimental Research Required

**Significant Experimental Research Required**

- 5 m

- Ptot ~ 75 Torr; PHNO3=14 Torr; RH = 36 %
**Laskin et al. (2005) *****JGR***

**Deliquescence Relative Humidity:** multicomponent, hydrated salts as a function of temperature
**Particle phase state:** details of non-equilibrium states
**Heterogeneous reactions**
- For example, CaCO3 + 2HNO3 **** Ca(NO3)2 + CO2 + H2O

**Notes:**
A few areas of uncertainty in aerosol modeling are listed.  I have recently done some work on the deliquescence of multicomponent hydrated salts.  While this research is important, it is not of major concern to aerosol modeling because it is very specific to certain particle compositions and deliquescence is an equilibrium concept (and equilibrium may not exist in the atmosphere).

One major area of uncertainty in aerosol modeling is related to the particle phase state.  As mentioned on the previous slide, the partial pressures of species at the surface of particles is currently determined by assuming that the interior of the particle is in equilibrium.  However, evidence exists that suggests particles are not always in their equilibrium state.  Measurements that help us better characterize the phase state of atmospheric particles would be welcome.  However, the measurements required to characterize particle phase state would be challenging, and characterizing the phase state of atmospheric particles may not even be possible considering the vast number of possible particle compositions.  Note that we are not doing research in this area at LLNL.

There has been quite a bit of research on heterogeneous reactions on particles (dust in particular) recently.  These reactions can influence gas-phase chemistry and change particle hygroscopic properties.  The reaction that has received the most attention is between calcium carbonate in dust particles and nitric acid in the gas phase.  In the figure (taken from a paper by Alexander Laskin of PNNL), CaCO3 is reacted with HNO3 for a period of a few hours.  Following the reaction, particles take up considerable amounts of water because the Ca(NO3)2 that forms is very hygroscopic (the original CaCO3 is insoluble).  The uptake of water following the heterogeneous reaction could make dust particles more efficient cloud-drop nuclei and change the way dust interacts directly with radiation.  As part of my thesis work, I am investigating whether the changes to dust properties from heterogeneous reactions could significantly influence cloud properties and the direct forcing.

## SciDAC: Coupled Carbon and Sulfur in Atmosphere, Land, and Ocean

**SciDAC: Coupled Carbon and Sulfur in Atmosphere, Land, and Ocean**

## SciDAC: Coupled Carbon and Sulfur in Atmosphere, Land, and Ocean

## SciDAC: Coupled Carbon and Sulfur in Atmosphere, Land, and Ocean

## Tracers offer alternative constraints on physical processes

- Physical processes move physical quantities,
- Heat, momentum, vorticity, and moisture.
- Physical processes move tracer quantities,
- Chemicals and aerosols.* *
- Some processes affect physical quantities and tracers similarly (*e.g*. advection), and some processes do not (*e.g.* photo-chemical reactions).
- Hence, tracers can offer new insights and constraints on physical processes, especially emergent properties.
- Ideally a tracer will isolate a single process, but unfortunately this is rare.
- Tracers can be run with feedback on or off.

## Interactive Dust Emission in IMPACT (Ginoux et al. 2004 – Wind, Soil Moisture, Topographic lows) 

## Time-Series of Model and Observations (Lassen): 24 hour smoothed

## Species with different lifetimes can provide clock giving “time since emission”

## DMS affects climate.

- Surface Stratification Temperature
- Solar Absorption

- Wind Speed

- Albedo
- Absorption

## DMS emission depends on physical structure of Ocean near the surface. 

- Ship-track profiles of DMS, reported by Bell at UEA

## Summary 

- Anatomy of  good parameterization:
- Important for climate,
- Widely applicable, (all regions, epochs, time scales and space scales)
- Fast (Scalable),
- Documentation (in-code and formal),
- Standalone test-case,
- Validated (including inside GCM).
- Few tunable parameters (idealy zero).
- Good parameterizations still need an advocate.
- Current parameterizations pack a lot into small packages.
- I believe tracers will have a tough but crucial role validating GCMs, especially emergent properties.