The Three Themes:The Three Themes:

1.1. Regional Climate Change and Energy Regional Climate Change and Energy ModelingModeling

2.2. Outstanding Scientific ProblemsOutstanding Scientific Problems

3.3. Infusion of Data into ModelsInfusion of Data into Models

Outstanding Science ProblemsOutstanding Science Problems

Vogelmann Miller

JensenWagener

LiuDaumGuo

McGrawSchwartz

LewisChang

ReismanBhatt

Wang

Riemer

Chang

Colle

ZhangMarat K.

Lin

NY BlueNY BlueCenterCenter

Aerosol Modeling

Aerosol Indirect Effect on Clouds

Cloud Processes for Weather

Cloud Climate Feedback and Turbulence

Coastal Turbulent Mixing

Quantifying Aerosol Forcing of Climate Change (McGraw)Quantifying Aerosol Forcing of Climate Change (McGraw)

Performing ensemble climate model runs of several months duration Performing ensemble climate model runs of several months duration with and without aerosols will allow better quantification of this forcing, with and without aerosols will allow better quantification of this forcing, insight into the quantities and processes on which it depends.insight into the quantities and processes on which it depends.

Radiative forcing components from the 2007 IPCC Report: Largest Radiative forcing components from the 2007 IPCC Report: Largest contributions to uncertainty are due to aerosols and aerosol-cloud interaction.contributions to uncertainty are due to aerosols and aerosol-cloud interaction.

••

••

••

BNL APPROACH: REPRESENTING AEROSOLS IN CLIMATE MODELS USING THE QUADRATURE METHOD OF MOMENTS (QMOM)

Illustration of a Simple Case:

Blue Gene provides the ensemble GCM simulation capabilityBlue Gene provides the ensemble GCM simulation capability

Global scale

Regional scale

Mesoscale

Microscale

Particle scale

Multiscale Aerosol Models(Riemer)

0.0·107

0.5·107

1.0·107

1.5·107

2.0·107

2.5·107

3.0·107

3.5·107

4.0·107

4.5·107

5.0·107

5.5·107

6.0·107

6.5·107

n(lo

gd

)p

0.01 0.10 1.00

d in mmp

ifjfic

sjc

total

m

n(lo

g d p

)

Stochastic particle resolved aerosol code

• Explicitly track composition of all particles in a parcel, with random coagulation events, interleaved with chemistry.

• Current serial code is implemented in Fortran 95. – Compute 10 minutes of simulation time using 107 particles and a gravitational

kernel in about 5 minutes of CPU time on a PC.

• Parallel version is needed to enable very large particle numbers (1010 and higher) and faster computation, especially when coupled to chemistry and transport models. – Flexible communication topology using MPI.

Mesoscale aerosol modeling

• Use WRF-chem (standard community model) to model aerosol transport, dynamics and chemistry on the mesoscale.

0

50

100

150

200y

inkm

0 50 100 150 200

x in km

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1*

Saarbrücken

Strasbourg

*

*Karlsruhe

*Stuttgart

*Mannheim

*Basel

g m-3

Aerosol Indirect EffectAerosol-cloud-climate interactions:

Largest uncertainties in climate studies Aerosols: tiny particles in the air Clouds: water drops/ice crystals Require sophisticated models

Microscopic Zoom in

Macroscopic view of clouds

Clouds are microscopic droplets

n(r

)

Droplet Radius (m)

n(r) (cm-3mm-1)

Mean droplet radius~ 10 m

Bulk Cloud PhysicsCloud properties:

mass concentration number concentration radar reflectivity …

Modeled Observed

(Fan et al., 2007)

Size-bin Cloud Physics

Cloud particle sizes: nm to cm

Spectrum:

(Tao et al., 2003)

M1, M2, . . . Mi, . . .

Importance of Clouds in Weather Research (Colle)

Many effects of clouds on climate and weather are largely unknown/uncertain (observations lacking, models at coarse resolution have poor representation of clouds). Most important problem confronting dynamicists and modelers today.

Cloud-resolving (h ~ O(100 m)) simulations of cloud systems are needed to understand cloud dynamics and to improve parameterizations - a computing challenge.

cloud-mixingeddies

cloudscloud

systemsplanetary wavessynoptic systems

meters to100’s meters

102 - 104

meters 105 - 106

meters >106 meters

Composite NEXRAD RadarReflectivity Forecast

(Courtesy of G. Bryan, NCAR/MMM)

4-km WRF Reflectivity Forecast Observed Reflectivity

Vertical Motion (5-km)

Simulations using x = 4 km to x = 125 m

Vertical cross-section of tracer concentration).

x = 4000 m

x = 1000 m

x = 250 m

(Courtesy of G. Bryan, NCAR/MMM)

Fundamental Hurricane Research

Mobile AL Radar

Meso-/Cloud-Scale Model (WRF)Hurricane Katrina Reflectivity at Landfall

29 Aug 2005 14 Z

4 km WRF, 62 h forecast

(Courtesy of B. Skamarock, NCAR/MMM)

Cloud-Climate Feedback (Zhang)

conv. dryingturb. mostening

Conv. moistening

PBL drying

PBL deepens

turb. mosteningconv. recovers

evap. cooling

LES Simulations as Benchmark

(Marat Khairoutdinov)

Plan: SAM LESGCE LESCAM High Resolution and Physical Ensembles

-76 -74 -72 -70 -6836

37

38

39

40

41

42

43

Water mass exchange between the continental shelf and the Gulf Stream (Wang)

Problems

Modeling of Some Key Missing ProcessesSpecific Consequence of Multi-scale Turbulent Interactions

Models

Aerosol ModelsGCE CRM/LES (3d bin microphysics)SAM LES (large domains)WRFPOP