# Presentation: 921119

## Testing Multiple Cloud Water and Cloud Parameterization Schemes in the ECPC GSM

**Contents**
- Introduction
- Objective
- Tested cloud schemes
- Comparison and validation of cloud schemes with ISCCP, ERBE, GPCP observations and R-2 reanalysis.
- Suggest possible revision to cloud parameterization.
- Conclusion

**Akihiko Shimpo***1**, Masao Kanamitsu***1
**and Song-You Hong***2
* **1* **Experimental Climate Prediction Center, SIO, UCSD*
- *2* **Laboratory for Atmospheric Modeling Research,** **Yonsei University*

## Introduction

- Accurate and physically based parameterizations of clouds and their radiative properties are essential to produce realistic representations of the current climate and possible future climates.
- For example, schemes based on statistics may not be appropriate for global warming simulations.
- Cess et al.(1990) analyzed the results from 19 GCMs and found roughly threefold variation in the global climate sensitivity and concluded that most of this variation is due to differences of cloud feedback processes in the models.
- Cloud radiative properties can be computed directly from cloud water when cloud water is incorporated as a prognostic variable. This has the advantage of providing physically realistic feedback mechanisms.

## Objectives

- Compare cloud water prediction schemes and cloudiness parameterizations in ECPC Global model.
- Focus on stratiform clouds
- Validate simulations with observational data.
- Examine DJF, JJA seasonal mean (10 year average)

## Comparison of simulations with observation / reanalysis

- 6 (4) cloud schemes : **CNTL** / **ZC** / **IS** / **HONG**(3,5,6)
- HONG5 and HONG6 are very similar to HONG3 but are very expensive, thus only HONG3 is used (here after, HONG)
- Cloud amount : high/middle/low/total
- Cloud water path
- Precipitation
- Temperature
- Outgoing Longwave / Shortwave radiation flux at TOA

## Cloud schemes in ECPC G-RSM

- qc : cloud water,  qi : cloud ice
- qr : rain,  qs : snow
- Efficiency : (HONG5)340%, (HONG6)510%

|  | Cloud water prognostic variables(#) | Stratiform
clouds | Convective
clouds | Boundary layer clouds | effici-ency |
| --- | --- | --- | --- | --- | --- |
| CNTL | None (0) | Diagnosed from RH (SS91) | Diagnosed from convective precipitation (SS91) | Diagnosed from inversion strength and RH
(SS91) | 100% |
| ZC | qc/qi (1) | Diagnosed from RH and cloud water content (R95) |  |  | 110% |
| HONG | qc/qi,  qr/qs (2) |  |  |  | 240% |
| IS | qc/qi (1) | Predicted (IS) |  |  | 150% |

## Cloud scheme (1) : CNTL

**No cloud water prediction**
**Precipitation**
- Supersaturation is removed instantaneously as a precipitation.  Evaporation occurs when precipitation falls through the unsaturated atmospheric layer.
**Stratiform cloud amount**
-  diagnosed from RH (Slingo and Slingo, 1991)
**Used by ECPC SFM**

## Cloud scheme (2) : ZC

**Predict cloud water/ice mixing ratio**** **
-    (Zhao and Carr 1997)
-     A(*q**c*) : horizontal advection of *q**c*
*	**S**c, **S**g* :  sources of *q**c *from convection and grid-scale condensation
*	**P* : precipitation production rate from cloud water/ice mixing ratio,
-          evaporation of precipitation, melting snow process
*	**E**c* : cloud evaporation rate      *D**qc* : horizontal and vertical diffusion
**Precipitation production**
- Cloud water -> rain : Sundqvist et al. (1989)
- Cloud ice  -> snow : Lin et al. (1983)

- ∂*q**c */∂t = *A*(*q**c*) + *S**c *+ *S**g* – *P*  – *E**c* + *D**qc*

*P**raut* = *c**0** q**c*{1-exp[-(*q**c*/(*q**cr**b*))2]}

*P**saut* = *a**1** *(*q**c*– *q**ci0*)

## Stratiform cloud amount 
diagnosed from RH and cloud water/ice mixing ratio
    (Randall 1995)

  α : 1.0x103 (same as in R95)

Used by NCEP GFS/CFS

- diagnosed from RH and cloud water/ice mixing ratio
-     (Randall 1995)
-   α : 1.0x103 (same as in R95)
- Used by NCEP GFS/CFS

- Cloud scheme (2) : ZC

## Difference between ECPC and NCEP ZC scheme  

**Cloud water/ice predictive equation**
- Critical value of autoconversion from cloud ice to snow (kg/kg)
**Cloud amount**
- Based on Xu and Randall (1996) for NCEP

*P**saut* = *a**1** *(*q**c*– *q**ci0*)

- NCEP : *q**ci0* = 1.0e-5x (0.01xP)       P(cbar)
- ECPC :* q**ci0* = 5.0e-6
-  values in NCEP and ECPC are the same at 500 hPa

- NCEP

- ECPC

## Predict cloud water/ice mixing ratio
   (Tiedtke 1993; Iacobellis and Somerville 2000)

    SBL :  sources of qc from boundary-layer turbulence
	 ENT : flux divergence due to entrainment processes at the top of  stratocumulus clouds 
Precipitation production
Cloud water / ice -> rain / snow  (Sundqvist et al. 1989)

Predict cloud amount

Used in Scripps SCM, and ECMWF

-    (Tiedtke 1993; Iacobellis and Somerville 2000)
*    **S**BL* :  sources of *q**c *from boundary-layer turbulence
*	** **ENT* : flux divergence due to entrainment processes at the top of  stratocumulus clouds
**Precipitation production**
- Cloud water / ice -> rain / snow  (Sundqvist et al. 1989)
**Predict cloud amount**
- Used in Scripps SCM, and ECMWF

- Cloud scheme (3) : IS

- ∂*q**c*/∂t = *A*(*q**c*) + *S**c* + *S**BL* + *S**g* – *P* – *E**c* – *ENT* + *D**qc*

- ∂*C*/∂t = *A*(*C*) + *S*(*C*)*c* + *S*(*C*)*BL* + *S*(*C*)*g* – *D*(*C*)

*P**raut* = *c**0** q**c*{1-exp[-(*q**c*/(*q**cr**b*))2]}

## Cloud scheme (4) : HONG

**Predict cloud water/ice mixing ratio and rain/snow mixing ratio** (Dudhia 1989, Hong et al. 1998, 2004)
-       *A(q**c**), A(q**r**)* : horizontal and vertical advection of *q**c *and *q**c *
*	**F*(*q**c*), F(*q**r*) : microphysical processes
*	**P* : precipitation production rate,        *D* : horizontal and vertical diffusion
- The microphysical processes in the scheme contain condensation of water vapor into cloud water (ice) at saturation, accretion of cloud by rain (ice by snow), evaporation (sublimation) of rain (snow), initiation of ice crystals, and sublimation or deposition of ice crystals
**Precipitation**
- Cloud water -> rain : Kessler (1969)
- Cloud ice -> snow :

- ∂*q**c */∂t = *A*(*q**c*) + *F(q**c*) + *D**qc*
- ∂*q**r */∂t = *A*(*q**r*) + *F(q**r*) + *D**qr *- *P*

*P**raut* = *a **ρ*(*q**c*– *q**c0*)

*P**saut* = *ρ*(*q**i*– *q**imax*)/ Δ*t*

## Stratiform cloud amount 
diagnosed from RH and cloud water/ice mixing ratio
    (Randall 1995) same as ZC

  α : 1.0x103 (same as in R95)

Used by NCEP WRF/RSM

- diagnosed from RH and cloud water/ice mixing ratio
-     (Randall 1995) same as **ZC**
-   α : 1.0x103 (same as in R95)
- Used by NCEP WRF/RSM

- Cloud scheme (4) : HONG

## Cloud schemes in ECPC G-RSM

- qc : cloud water,  qi : cloud ice
- qr : rain,  qs : snow
- Efficiency : (HONG5)340%, (HONG6)510%

|  | Cloud water prognostic variables(#) | Stratiform
clouds | Convective
clouds | Boundary layer clouds | effici-ency |
| --- | --- | --- | --- | --- | --- |
| CNTL | None (0) | Diagnosed from RH (SS91) | Diagnosed from convective precipitation (SS91) | Diagnosed from inversion strength and RH
(SS91) | 100% |
| ZC | qc/qi (1) | Diagnosed from RH and cloud water content (R95) |  |  | 110% |
| HONG | qc/qi,  qr/qs (2) |  |  |  | 240% |
| IS | qc/qi (1) | Predicted (IS) |  |  | 150% |

## Additional cloud amount calculation

**Convective clouds**
- Derived from convective precipitation as a function of precipitation rate. (SS91)
- Convective clouds are used in **CNTL**, **ZC** and **HONG**
**Boundary layer clouds**
- Derived from inversion strength and relative humidity at inversion base (SS91)
- Boundary layer clouds are used in _**all the schemes**_.
- Both Convective and boundary layer clouds are ***not used in NCEP GFS/CFS***

*C**c* = 0.200+0.120·ln*P**cnv*

*ω>=0 (subsidence) and **∂**θ** / **∂**p* <= -0.055 K hPa-1
*C**BL* = (-16.67 ·*∂**θ** / **∂**p* - 1.167){(*RH**B*-0.55)/0.25}  for  0.55=<*RH**B*=<0.8
*C**BL* = -16.67 · *∂**θ** / **∂**p* - 1.167                               for  *RH**B*>=0.8

## Experimental design

- ECPC G-RSM : Based on the NCEP Seasonal Forecast Model (SFM; Kanamitsu et al. 2002)
- GSM T62L28
- Initial time : 01/Jan/1989, 00Z
- Integration : 11 years (1989-1999), first year removed for validation.
- 1 member
- SST : interpolated daily from NCEP weekly analysis (Reynolds and Smith 1994)

## ECPC GSM model physics

- Convection scheme : RAS (Moorthi and Suarez 1992)
- SW Radiation : Chou (1992)
- LW Radiation : Chou et al. (1999)
- Cloud optical properties (for **ZC**, **IS** and **HONG**):
-     Slingo(1989) for liquid,
-     Ebert and Curry (1992) for ice cloud
- Boundary layer : non-local vertical diffusion
-     (Hong and Pan 1996)
- Land surface model : Noah land surface model
-     (Ek et al. 2003)
**4 cloud schemes : ****CNTL**** / ****ZC**** / ****IS**** /**** HONG**** **

## Observation / Reanalysis data

- ISCCP D2 (Rossow and Schiffer 1999 )
- The International Satellite Cloud Climatology Project
- 1990-1999 (10 years), monthly mean
- Cloud amount (high/middle/low/total), cloud water path
- GPCP
- Global Precipitation Climatology Project
- 1990-1999 (10 years), monthly mean
- Precipitation
- ERBE
- Earth Radiation Budget Experiment
- 1985-1989 (6 years), monthly mean
- Radiation at TOA
- R-2 (Kanamitsu et al. 2002)
- NCEP-DOE Reanalysis 2
- 1990-1999 (10 years), monthly mean
- Temperature, Relative humidity

## High cloud amount

**IS**>**CNTL**, **HONG**>**ZC** : **CNTL** and **HONG** are better. **IS** is overestimated, **ZC** is underestimated.
- TROPICS,OCEAN: (DJF) North (over ITCZ) > South (over SPCZ) in **CNTL**, **ZC** and **HO****NG**
- related with convective precipitation error and convective cloud formulation
- Anvil is calculated from stratiform cloud formula : insufficient in **ZC**, small cloud ice setting.
**IS**:Though overestimated, SPCZ>ITCZ like ISCCP
- NH/SH: Larger in higher latitudes. Overestimated  in **CNTL**, **IS** and **HONG**.
- JJA:similar to DJF.

**DJF**

**CNTL**

**ZC**

**IS**

**HONG**

- [%]

- [%]

- C

## Middle cloud amount

- Similar to high clouds. **IS**>**CNTL**, **HONG**>**ZC** : **CNTL** and **HONG** are better. **ZC** is underestimated.
- LAND : **IS**>**CNTL**>**HONG**>**ZC** :
- TROPICS:Overestimated, however the difference of view between satellite based observation and model simulation should be considered.

**DJF**

**CNTL**

**ZC**

**IS**

**HONG**

- [%]

- [%]

- C

## Low cloud amount

**HONG**,**IS**>**ZC**>**CNTL**: schemes using cloud water show larger cloud amount. Better in summer hemisphere over land in mid latitudes (30-50N/S).** CNTL** shows underestimation.
- OCEAN, SUBTROPICS:underestimated in all. **ZC** is better than others.
- OCEAN,MID-LAT: underestimated in **CNTL** in winter hemisphere, others are close or overestimated.
- Seasonal change:(OBS)Winter>Summer, (**CNTL**,**ZC**,**HONG**)S>W, (**IS**)small change.

**DJF**

**CNTL**

**ZC**

**IS**

**HONG**

- [%]

- [%]

- C

## Total cloud amount

- Underestimated in all schemes. **CNTL** is the smallest. **IS** is closer to OBS than others.
**IS**>**HONG**>**CNTL**,**ZC**

**DJF**

**CNTL**

**ZC**

**IS**

**HONG**

- [%]

- [%]

- C

## Summary of cloud amount comparison

- High cloud:
**IS**>**CNTL**,**HONG**>**ZC**. **CNTL** and **HONG** are better. **IS** overestimates, **ZC** underestimates. Overestimation in **IS** is caused by neglecting falling cloud ice. Underestimation in **ZC** is caused by small value of autoconversion from cloud ice to snow.
- Anvil in **ZC**,** IS** and **HONG** seems to be underestimated.
- Middle cloud
**IS**>**CNTL**,**HONG**>**ZC**. **CNTL** and **HONG** are better. **IS** is overestimated, **ZC** is underestimated. All similar to high cloud.
- Low cloud
**HONG**, **IS**>**ZC**>**CNTL**. Using cloud water show larger cloud amount.
**ZC** is better in tropics over ocean.
- Total cloud
- Underestimated in all. **IS** is closer to observation.

## Cloud water path

- [g m-2]

**ZC** : closest to observation. Underestimated over land in NH in DJF
**IS**:  overestimated ,except for over land in NH mid latitude in DJF
**HONG**:  seasonal change is large. Sum>Win. Over estimation in summer hemisphere. Underestimated over land in NH in DJF like **ZC**.

**DJF**

**ZC**

**IS**

**HONG**

- [g m-2]

## Precipitation

- [mm day-1]

- Double ITCZ problem is seen in all schemes. Overestimation in TROPICS and winter hemisphere.
- TROPICS: **CNTL**, **ZC** and **HONG** show similar precipitation distribution. **IS** seems to be good over land. Over ocean, contrast between ITCZ and SPCZ is better than others.over land, also **IS** is good.
**HONG** shows underestimation, especially over land.
- JJA is similar to DJF

**DJF**

**CNTL**

**ZC**

**IS**

**HONG**

- [mm day-1]

## Zonal mean Temperature 

- Zonal mean Temperature

- Cold bias is common to all schemes in stratosphere.
- Warm bias is seen around troposphere, especially **IS** and **ZC**. This is related with the radiative properties calculated from cloud water content. In **IS**, it is warmer in summer hemisphere. It is interesting that warm bias is not strong in **HONG**, in which cloud water path is between **ZC** and **IS**.
**CNTL** seems to be the best in 4 schemes.
- JJA pattern reverse between NH and SH.

- [K]

**CNTL****-R2**

**ZC****-R2**

**IS****-R2**

**HONG****-R2**

**W**

## Radiation at TOA (Diff from ERBE)

**Radiation at TOA (Diff from ERBE)**

- OLR: Over ocean, overestimated most in **ZC**. Related with least high cloud amount. **IS** is closest to observation, however high cloud amount is overestimated. Over land between 30S-EQ, **CNTL** high cloud amount is the best of all. Insufficient representation of anvil in other schemes.
- OSR: **IS** or **HONG** is the best. The model with total cloud amount matches the best with observation shows best OSR.

- _OSR_

- _OSR_

- _OLR_

- _OLR_

- [W m-2]

**CNTL**

**ZC**

**IS**

**HONG**

## Summary of comparisons

**Cloud water path**:
**IS**>**HONG**>**ZC**. **ZC** is the closest to observation.
- Seasonal change seen in **HONG** is large. Summer > Winter.
**Precipitation**
- Overestimated in all schemes. Tendency to form double ITCZ in all.
- Over land, **IS** shows closer precipitation distribution to observation.
**HONG** shows underestimation over land in mid latitudes.
**Temperature**
**CNTL** has smallest bias.
- Warm bias is seen around tropopause in **IS**. This seems to be related with a lot of cloud water in high latitudes.
**Radiation fluxes at TOA**
- OLR: Over ocean, overestimated most in **ZC**. Related with least high cloud amount. **IS** is closest to observation, however high cloud amount is overestimated. Over land between 30S-EQ, **CNTL** high cloud amount is the best of all. Insufficient representation of anvil in other schemes.
- OSR: **IS** or **HONG** is the best. Total cloud amount which matches best with observation, is responsible.

## Re-examination of cloudiness parameterization 

- Cloudiness is important for:
- Model simulations using 4 cloud schemes showed TOA is sensitive to simulated cloud distributions.
- Simulated cloud distribution closer to observation results in better radiation fluxes at TOA.

## Re-examination of the cloudiness parameterization

- Examine relationships between relative humidity and cloudiness, and cloud water and cloudiness in observation
- Observation used
- ISCCP(D2): cloud amount, cloud water path
- 3 levels : high/middle/low
- NCEP/DOE reanalysis 2 (R2) : relative humidity
- Maximum in each level

## Relationships between RH, C, CWP in observation

- It is found that the correlation between cloud water and cloud amount is higher than that between relative humidity and cloud amount.
- This suggest that there may be an advantage in predicting cloud water than simply diagnosing from relative humidity.
- It also seems to suggest that the combination of relative humidity and cloud water  may not be necessary to obtain cloud amount.
- If successful, the use of cloud water only may be able to generalize the cloudiness of all types.

- RH [%]

- CWP [g m-2]

- NH(30N-60N)

- _High_

- _High_

- _Middle_

- _Middle_

- _Low_

- _Low_

- C [%]

## Relationships between RH, C and CWP in model

- From simulations, the range of cloud amount for a given relative humidity is narrower than that of observation and reanalysis. Sensitivities of cloud amount against relative humidity in models are different from each other.
- The range of cloud amount for a given cloud water is wider than that of observation and reanalysis, except for ZC, which has very small critical value of autoconversion from cloud ice to snow by tuning.

- RH [%]

- NH(30N-60N)

- [g m-2]

**CNTL**

**ZC**

**IS**

**HONG**

- _High cloud_

**OBS**

- CWP

## A proposed formulation of cloud amount using cloud water 

- A proposed formulation of cloud amount using cloud water

- This formulation is similar to Randall (1995)’s formulation, but does not include the term associated with RH. To determine the constant *ß*, ISCCP D1 data (6-hourly) is used.
- The accuracy of predicted cloud water distribution in the model becomes very important since cloud water is an only predictor in the proposed parameterization.   Bias in cloud water may harm this scheme.

*C* = 1-exp( -*ß*•*q**c *)

* **ß* (constant)
-  ISCCP D1 (6 hourly)

- ISCCP(OBS), SS91, R95, NEW

- High cloud amount

## Conclusion (1)

- 4 cloud water schemes are tested in ECPC G-RSM.  All the schemes provided reasonable simulation but with different magnitude of systematic errors.
**CNTL** shows reasonable cloud amount distribution. However, in radiation fluxes, error is larger, especially OSR.
- Use of cloud water in radiation schemes shows better radiation fluxes, especially OSR.
- In **ZC**, high and middle cloud amount are underestimated. It is caused by the small autoconversion from cloud ice to snow. Underestimation of high cloud amount causes largest overestimation of OLR. Precipitation is similar to **CNTL**.

## Conclusion (2)

**IS** shows the best simulation of precipitation, OLR and OSR. Good OSR is caused by the good total cloud amount.  However, good OLR is caused by the *overestimation* of high cloud amount (compensation of errors?).
**HONG** simulates high and middle cloud amounts similar to **CNTL**. Seasonal variation of cloud water is too large. Precipitation is similar to **CNTL** and **ZC** over ocean, however, underestimated over land.
- The cloudiness is strongly “tuneable”.  This is the greatest weakness of the cloudiness parameterization scheme.  More physically based scheme needs to be developed.

## Conclusion (3)

- The sensitivities of cloud amount (C) to relative humidity(RH) and to cloud water (CW) are different for different cloud schemes.  The model’s sensitivity of C to RH is narrower than observation.
- From observation, it is found the correlation between CW  and C is higher than that between RH and C. This indicates that the there is a possible advantage in parameterizing C from CW than simply diagnosing from RH.
- A formulation of cloud amount using cloud water without relative humidity was proposed based on observation.  This scheme seems to work very well if the cloud water is predicted accurately.

## Thanks

- “Harerun” -  JMA’s mascot

## Summary of comparison

- ☺ : close to observation,   O  : overestimation,  U: underestimation

|  | HighC | MiddleC | LowC | TotalC | CW | Precip. | Temp. | OLR | OSR |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| CNTL | ☺ | ☺ | U | U | N.A. | O | ☺ | O | U |
| ZC | U | U | O | U  | ☺ | O | U | O | U |
| IS | O | O | O | ☺ | O | ☺ | O | ☺  | ☺ |
| HONG | ☺ | ☺ | O | U  | O | O /U | U | O | ☺ |

## Incorporation the proposed formulation of cloud amount using cloud water into ECPC GSM

- To incorporate the new formulation of cloud amount into ECPC GSM, the bias of cloud water should be considered. It is important to improve cloud water parameterization, since the proposed cloud parameterization is highly dependent on the value of cloud water.

**OBS**

**HONG**

**HONGNC**

_**Difference of**_ _**Cloud water ice**_ ([HONGNC]-OBS)

_***High cloud amount (Jan 2000)***_

*C* = 1-exp( -*ß*•*q**c *)

## Modified Zhang-McFarlane convection scheme (ZM2)

- ZM2 is a mass flux type convection scheme, but it is based on a new closure assumption (Zhang and Mu, 2005a; Zhang and Wang, 2006; Zhang and Mu, 2005b). The scheme is shown to improve MJO simulation and corrects double ITCZ problem in the NCAR CCM. The original Zhang scheme had very small intra-seasonal variability with very strong double ITCZ, which are apparently corrected in the newer version.
- Source code of ZM2 was provided by Dr. Zhang and it was incorporated into ECPC GSM.
- Averaged precipitation fields of ZM2 is similar to RAS and it is overestimated.
- However, intraseasonal variability of precipitation of ZM2 is larger than RAS and also overestimated.

- Plot by Dr. Mu (SIO)

**OBS**

**RAS**

**ZM2**

_***INTRASEASONAL VARIABLITY (20-80day variance)***_

_***AVERAGE***_