The Impact of Coupled versus Observed SST on Summer Season Predictions over America with the NCEP CFS Land Upgrades

11

The Impact of Coupled versus Observed SST on Summer Season Predictions over America

with the NCEP CFS Land Upgrades

Rongqian Yang, Michael Ek, Jesse Meng and Ken Mitchell

NCEP Environmental Modeling Center NCEP-COLA CTB Joint Seminar Series

24 March 2010

2

Objectives Objectives

A.A. Examine the impact of land upgrades in the next-Examine the impact of land upgrades in the next-

generation Climate Forecast System (CFS) on summer generation Climate Forecast System (CFS) on summer season predictions. Any meaningful improvements?

season predictions. Any meaningful improvements?

B. Examine the relative contribution from land anomaly B. Examine the relative contribution from land anomaly forcing to summer seasonal predictability ( vs.SST forcing to summer seasonal predictability ( vs.SST anomaly forcing).

anomaly forcing).

Motivation Motivation

SST anomalies

SST anomalies : : the foremost source of seasonal predictabilitythe foremost source of seasonal predictability in coupled global models.

in coupled global models.

Land surface anomalies

Land surface anomalies:: the secondthe second most important source most important source of seasonal predictability (e.g. anomalies of

of seasonal predictability (e.g. anomalies of soil moisturesoil moisture, , snowpack, vegetation cover).

snowpack, vegetation cover).

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Outline Outline

 Overview of Overview of Currently Operational CFS Currently Operational CFS Skill Skill

 Next-generation CFS Next-generation CFS Upgrades (with land) Upgrades (with land)

 CFS Experiments CFS Experiments

Part A: Part A: Coupled SST (CMIP) runs and resultsCoupled SST (CMIP) runs and results

Part B: Part B: Observed SST (AMIP) runs and comparison with part AObserved SST (AMIP) runs and comparison with part A

Part C: Part C: Ocean-land-atmosphere interactionOcean-land-atmosphere interaction comparison between A & B comparison between A & B

 Conclusions Conclusions

4

Overview of Overview of

Currently Operational CFS Currently Operational CFS Seasonal Prediction Skill Seasonal Prediction Skill

(next 3 frames)

(next 3 frames)

55

Ops CFS Seasonal

Ops CFS Seasonal SST SST Forecast Skill Forecast Skill : :

Correlation of CFS SST forecast with observed SST: 1982-2003 Correlation of CFS SST forecast with observed SST: 1982-2003

For For April initial conditionsApril initial conditions: 15-member ensemble mean: 15-member ensemble mean 1-Month Lead (valid summer) 6-Month Lead (valid winter)

high correlation skill in tropical Pacific high correlation skill in tropical Pacific

66

Ops CFS Summer Season Ops CFS Summer Season Precipitation

Precipitation Forecast Skill Forecast Skill : :

Correlation of CFS precip forecast with observed precip: 1982-2003 Correlation of CFS precip forecast with observed precip: 1982-2003

For For April initial conditionsApril initial conditions: 15-member ensemble mean: 15-member ensemble mean

Short-lead summer forecast correlation skill is low across bulk of CONUS (lower than longer-lead winter forecast)

77

Ops CFS Summer Season Ops CFS Summer Season Temperature

Temperature Forecast Skill Forecast Skill : :

Correlation of CFS 2m-T forecast with observed 2m-T: 1982-2003 Correlation of CFS 2m-T forecast with observed 2m-T: 1982-2003

For For April initial conditionsApril initial conditions: 15-member ensemble mean: 15-member ensemble mean 1-Month Lead (valid summer) 6-Month Lead (valid winter)

Short-lead summer forecast correlation skill is low across bulk of CONUS

8

Next-generation CFS Upgrades Next-generation CFS Upgrades

 Upgrades to CFS physics Upgrades to CFS physics

– Atmosphere (with co2 trend added), Atmosphere (with co2 trend added) , ocean, ocean , land land model

model , , sea-ice sea-ice

 New 4DDA analysis systems (CFSR) New 4DDA analysis systems (CFSR) – Atmosphere, ocean, Atmosphere, ocean, land land

 Double the CFS resolution Double the CFS resolution

– T126 / L64 T126 / L64 (versus T62 / L28) (versus T62 / L28)

More details about the CFS seeMore details about the CFS see

Saha et al., The NCEP Climate Forecast System, 2006, J. Clim, 19(15), 3483-3517 Saha et al., The NCEP Climate Forecast System, 2006, J. Clim, 19(15), 3483-3517

The CFS experiments presented below incorporate the CFS upgrades (land related upgrades highlighted above in red)

99

 Noah LSMNoah LSM

– 4 soil layers (10, 30, 60, 100 cm)4 soil layers (10, 30, 60, 100 cm) – Frozen soil physics includedFrozen soil physics included

– Surface fluxes weighted by snow cover Surface fluxes weighted by snow cover fraction

fraction

– Improved seasonal cycle of vegetation Improved seasonal cycle of vegetation cover

cover

– Spatially varying root depthSpatially varying root depth

– Runoff and infiltration account for sub-grid Runoff and infiltration account for sub-grid variability in precipitation & soil moisture variability in precipitation & soil moisture – Improved soil & snow thermal conductivityImproved soil & snow thermal conductivity – Higher canopy resistanceHigher canopy resistance

– OtherOther

Land Model

Land Model Upgrade Upgrade

Noah LSM (new) versus OSU LSM (old):

Noah LSM (new) versus OSU LSM (old):

 OSU LSMOSU LSM

– 2 soil layers (10, 190 cm)2 soil layers (10, 190 cm) – No frozen soil physicsNo frozen soil physics

– Surface fluxes not weighted by Surface fluxes not weighted by snow fraction

snow fraction

– Vegetation fraction never less than Vegetation fraction never less than 50 percent

50 percent

– Spatially constant root depthSpatially constant root depth

– Runoff & infiltration do not account Runoff & infiltration do not account for subgrid variability of

for subgrid variability of precipitation & soil moisture precipitation & soil moisture – Poor soil and snow thermal Poor soil and snow thermal

conductivity, especially for thin conductivity, especially for thin snowpack

snowpack

Noah LSM replaced OSU LSM in operational NCEP medium-range

Global Forecast System (GFS) in late May 2005

More details see:

Ek et al., 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys.

Res., 108(D22).

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Land Data Assimilation System

Land Data Assimilation System Upgrade Upgrade

GLDAS/Noah

GLDAS/Noah & Global Reanalysis 2 ( & Global Reanalysis 2 ( GR2/OSU GR2/OSU ): ):

 GLDAS GLDAS : : an an uncoupled uncoupled G G lobal lobal L L and and D D ata ata A A ssimilation ssimilation S S ystem ystem driven by observed driven by observed

precipitation

precipitation analyses (CPC CMAP analyses) analyses (CPC CMAP analyses)

– Executed using Executed using same grid, land mask, terrain field and same grid, land mask, terrain field and four-layer

four-layer Noah LSMNoah LSM as in experimental CFS forecastsas in experimental CFS forecasts – Non-precipitation land forcing is from GR2Non-precipitation land forcing is from GR2

– Executed retrospectively from 1979-2006 (after spin-up)Executed retrospectively from 1979-2006 (after spin-up)

 GR2 GR2 : : a a coupled coupled atmosphere/land assimilation atmosphere/land assimilation system wherein land component is

system wherein land component is driven by model driven by model predicted precipitation

predicted precipitation

– applies the applies the OSU LSMOSU LSM with two soil layerswith two soil layers

– nudges soil moisture based on differences between nudges soil moisture based on differences between model and CPC CMAP precipitation

model and CPC CMAP precipitation

1111

01 May

Similar climatology (% volume) 01 May 1999

Different anomaly (% volume)

Time series of Total SM (mm) over Illinois (81-04)

Annual Cycle (mm)

Noah/GLDAS has higher SM

Soil Moisture

Soil Moisture Difference between GLDAS Difference between GLDAS and GR2 over CONUS

and GR2 over CONUS

(land anomaly forcing comparison) (land anomaly forcing comparison)

90-day ending 01 May 99 Precip Anomaly &

climatology

01 May 1999

Soil Moisture Anomaly disagree.

Noah/GLDAS Closer to Observed Precip

Anomaly over western CONUS

GR2 GLDAS

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How the land upgrades in the new CFS system

perform? Any improvements?

Question?

1313

CFS Land Experiments: Part A

CMIP Runs (Coupled SST) Noah/GLDAS

vs

OSU/GR2

Highly Controlled

• 25-year summer reforecasts (80-04)

• 10 member (ICs from 00Z of Apr 19-23, Apr 29-May 03)

• Same atmospheric ICs, physics, and Oceanic initial states

• Same resolution (T126/L64)

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Verification Methods and Data Verification Methods and Data

 Precip: 1/8Precip: 1/8^thth NLDAS (Mitchell et al.,2004, JGR 109 ) and CMAP NLDAS (Mitchell et al.,2004, JGR 109 ) and CMAP

 SST:1X1 degree daily OI SST (Reynolds et al,2002, J. Clim,15) with CMIPSST:1X1 degree daily OI SST (Reynolds et al,2002, J. Clim,15) with CMIP

 T2m: monthly T126 (Fan and H. van den Dool, 2008, JGR 113)T2m: monthly T126 (Fan and H. van den Dool, 2008, JGR 113)

 Atmospheric data: 2.5x2.5 degree NCEP/DOE GR2Atmospheric data: 2.5x2.5 degree NCEP/DOE GR2

Main Skill Measures: A^nomaly Correlation (AC⁾

Area-averaged AC scores (AAC⁾

Percentage Count of Positive AC (PAC⁾

Anomaly Cross Correlation

Focusing on

CONUS summer season

1515

CFS Noah/GLDAS CFS Noah/GR2

CFS OSU/GR2

JJA CFS AC skill: SST (25 yrs ensemble mean)

The two configurations yield similar SST correlation patterns

0.1 0.6 0.9 0.1 0.6 0.9

Slightly better with Noah/GLDAS

over higher latitudes

Similar SST Performance over Nino 3.4

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Time series of predicted JJA Nino 3.4 SST Anomaly over the 25 yrs

Predicted JJA Niño 3.4 anomaly and Obs

Good Agreement with Observations over most years

Similar performance

over most years &

1999 (defined as 5˚S-5˚N, 120˚W-170˚W)

(defined as 5˚S-5˚N, 120˚W-170˚W)

So the difference in CFS performance (next) is likely due to land upgrades

2.0

0

-2.0 0.5

-0.5

1717

0.6

- 0.6

0.6

- 0.6

JJA CFS AC Skill: precipitation (25 yrs ensemble mean)

Better Performance

over Pacific Northwest and northern Great

Plains

Better performance

over Central great plains and the

Gulf States

Positive AC points (%) are 64.9 (Noah/GLDAS & 58.4 (OSU/GR2)

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CONUS-average Anomaly Correlation:

CFS JJA ensemble mean precipitation forecasts

from the 25-year 10-member reforecasts of the two CFS configurations

Improvement with Noah/GLDAS, but the score is still low

1919

Anomaly Correlation: CFS JJA ensemble mean

Anomaly Correlation: CFS JJA ensemble mean temperaturetemperature forecasts forecasts

from the 25-year reforecasts of 2 T126 CFS tests from the 25-year reforecasts of 2 T126 CFS tests

(10 members each from same late April and early May initial times) (10 members each from same late April and early May initial times)

better performance with OSU/GR2

from Mid-west

to

The Great Plains

20

CONUS-average Anomaly Correlation:

CFS JJA ensemble mean temperature forecasts

from the 25-year 10-member reforecasts of the two CFS configurations

2121

Partition into ENSO

Partition into ENSO Neutral Neutral & & Non-neutral Non-neutral samples using

samples using MJJ MJJ Nino3.4 SST anomaly of Nino3.4 SST anomaly of 0.7C 0.7C as a threshold magnitude. as a threshold magnitude.

10 non-neutral summers:

82,83,87,88,91,92,93,97,99,02 (red: warm, blue: cold)

15 neutral summers:

80,81,84,85,86,89,90,94,95,96,98,00,01,03,04

Further Analysis Further Analysis

 To examine the CFS performance with

different ENSO signals

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10 10 non-neutral non-neutral ENSO years: ENSO years:

JJA

JJA precipitation precipitation AC Score AC Score

Much better performance

than 25-yr avgs

Due to Strong SST signals

Extremely similar geographical patterns from

west of the Mississippi River to the Pacific states

2323

15 15 neutral neutral ENSO years: ENSO years:

JJA

JJA precipitation precipitation AC Score AC Score

Worse performance

than 25-yr avgs As expected

Weak SST impact

Similar to 25-yr avgs

The large differences are mostly over the western CONUS

2424

CONUS-average JJA precipitation AC score

Significance test (T-statistic) shows differences are

significant at 90%

confidence level.

Neutral years

0.04

-0.15 0.00

Significance test (T-statistic) shows differences are not significant at 90%

confidence level.

Non-Neutral years

0.18

0 0.10

2525

10 10 non-neutral non-neutral ENSO years: ENSO years:

JJA

JJA temperature temperature AC score AC score

Due to Strong SST signals

Similar geographical

patterns With both CFS

Slightly better with OSU/GR2

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15 15 neutral neutral ENSO years: ENSO years:

JJA

JJA temperature temperature AC score AC score

Degraded Performance with

both cases

Slightly better over with Noah/GLDAS

Over the Rocky Mountain states and the N. Pacific

states

Disagree over the southern Great

Plains

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CONUS-average JJA temperature AC score

Neutral years

0.2

0

Significance test (T-statistic) shows differences are not significant at 90%

confidence level.

Non-Neutral years

0.5

0 0.2 0.4

0.14

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Question?

Question?

Is the skill gain really from the land upgrades Is the skill gain really from the land upgrades

Noah/GLDAS Noah/GLDAS

or or

better SST?

better SST?

CFS Land Experiments: Part B

Replace the

coupled SST

with

observed SST

(Extremely Controlled AMIP runs)

To answer:

How are the AMIP runs compared to the CMIP runs?

How are the AMIP runs compared to the CMIP runs?

2929

CMIP and AMIP Precip AC Skill Avgd over 25 yrs

AAC

PAC

C-Noah A-Noah C-OSU A-OSU

AMIP OSU CMIP Noah

CMIP OSU

AMIP Noah

Noah performs better in CMIP AMIP loses skill in these regions with both CFS

3030

T2m AC skill averagd over the 25 yrs

AAC

PAC

Noah performs worse with AMIP, no big difference with OSU

AMIP OSU CMIP Noah AMIP Noah

CMIP OSU

3131

Precip AC skill averaged over the 10 non-neutral yrs

AAC

PAC

AMIP runs are better than CMIP with both LSMs during non-neutral yrs, but not statistically

significant (90%)

CMIP Noah

AMIP OSU CMIP OSU

AMIP Noah

3232

Precip AC skill averaged over the 15 neutral yrs

AAC

PAC

Noah is better than OSU, the difference is

statistically significant (90%) ^{in AMIP}

CMIP Noah AMIP Noah

CMIP OSU AMIP OSU

Noah is better than OSU,

statistically significant (90%) in CMIP

3333

T2m AC skill averaged over the 10 non-neutral yrs

AAC

PAC

No big difference with either LSM in both CMIP/AMIP modes – no clear advantage -- Mixed in both AAC and PAC

AMIP OSU CMIP Noah AMIP Noah

CMIP OSU

3434

T2m AC skill avgd over the 15 neutral yrs

AAC

PAC

Both LSMs perform slightly better with AMIP; All differences in T2m are not statistically significant at 90% confidence level

AMIP OSU CMIP Noah AMIP Noah

CMIP OSU

3535

Wet Southwest U.S.

Wet Southwest U.S. Monsoon CaseMonsoon Case Predicted Precipitation Anomaly (in mm)

1999: Predicted

1999: Predicted JJA JJA

-50 50

No big difference over Southwest With Noah/GLDAS

Bad performance over Southwest

With OSU/GR2

CMIP Noah/GLDAS performs better over these areas than AMIP Noah/GLDAS

CMIP Noah/GLDAS AMIP Noah/GLDAS

CMIP OSU/GR2 AMIP OSU/GR2

Obs

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Why the AMIP runs are not as good as CMIP runs?

Perfect SST means the upper limit of predictability or only true with perfect atmosphere and land surface?

Ocean-Land-Atmosphere Ocean-Land-Atmosphere

interaction comparison interaction comparison

Question?

Represented by Represented by

SST SST , , 500 GPH, 500 GPH, and and Soil Moisture Soil Moisture

3737

GR2

CMIP Noah AMIP Noah

CMIP OSU AMIP OSU

SST- 500GPH Anomaly Cross Correlation

All have good agreements with

observations, but

weaker in CMIP and stronger in AMIP

3838

500 GPH AC skill Averaged over 25 yrs

CMIP : Noah better over CONUS; AMIP worse than CMIP over bulk of CONUS (Noah better over ocean)

AMIP OSU AMIP Noah

CMIP OSU CMIP Noah

3939

Comparison of Predicted 500GPH JJA Climatology with GR2

The JJA 500GPH Climatology is

too low compared to GR2 in AMIP

where CMIP shows better agreement with Obs

AMIP OSU CMIP Noah AMIP Noah

CMIP OSU GR2

588

588

588 588

588

40

JJA Soil Moisture and 500 GPH Anomaly Cross Correlation

Negatively Correlated in both GLDAS and GR2

4141

Predicted JJA Soil Moisture and 500 GPH Cross Correlation

CMIP Noah AMIP Noah

CMIP OSU AMIP OSU

Predict wrong signs with AMIP runs

42

Conclusions Conclusions

 The land upgrades in CFS do improve summer season The land upgrades in CFS do improve summer season precipitation

precipitation prediction over CONUS, especially prediction over CONUS, especially during during ENSO-neutral years in both modes

ENSO-neutral years in both modes when the when the land anomaly land anomaly forcings

forcings and and land-atmospheric interactionsland-atmospheric interactions contribute more contribute more to seasonal predictability.

to seasonal predictability.

 AMIP runs are usually assumed to be the upper limit of

potential predictive skill, but they ignore feedbacks from the atmosphere, and lead to degraded large-scale atmospheric circulation performance which contributes to the skill loss over CONUS (on average).