The Stratosphere-resolving Historical Forecast Project

(SHFP)

Amy H. Butler NOAA/NCEP Climate Prediction Center, USA

Co-authors: Adam Scaife, Andrew Charlton-Perez, Alexander Lawes, Natalia Calvo, and Michael Sigmond

What is a historical forecast, or hindcast?

• Model runs are initialized on a given date with observed data from the historical record, then run forward in time with no additional observed input (i.e., forecast mode)

• Ensemble members can be created by initializing model with dates/times that are slightly different

• Operationally, hindcasts allow calibration and estimation of skill of future forecasts.

SHFP Goals

Purpose: • To quantify improvements in actual predictability by initializing and resolving the

stratosphere in seasonal forecast systems

• To compare with existing seasonal to interannual forecast skill and to provide a hindcast data set that may be used to:

• Demonstrate improvements in currently achievable season forecast skill for a range of variables and lead times

• Understand improvements under particular scenarios such as El Nino and years with an active stratosphere

• Justify changes in operational seasonal forecast approaches and methods as a subproject of the WGSIP Climate Historical Forecast Project: http://www.wcrp-climate.org/wgsip/chfp/index.shtml

Model Hindcast Requirements

Stratosphere- resolving models: • A domain extending to 1hPa (~50km) or higher • At least 15 model levels between the tropopause and 1hPa • Models with non-resolving stratospheres are also encouraged to

participate

Once model is initialized, no “future” information is used to create the forecasts. Atmospheric initial states: • NCEP-NCAR or ECMWF reanalysis, initialized each May and Nov of each

year, 1989-present (longer records welcome) • At least 6-member ensemble • Each ensemble member should run for at least 3-6 months.

Data

Model output can be found at: http://chfps.cima.fcen.uba.ar/index.html

Model (# members) Model resolution Time period Output Levels above sfc ARPEGE_z00k** (11) T63, L91 1979-2007, NDJ 1000, 925, 850, 700, 500, 400, 300,

200, 50, 30, 10

ARPEGE_z00l (11) T63, L31 1979-2007, NDJ 1000, 925, 850, 700, 500, 400, 300, 200, 50, 30, 10

CCCma-CanCM3 (10) T63, L31 1979-2008, NDJFM 850, 500, 200, 100, 50

CCCma-CanCM4 (10) T63, L35 1979-2008, NDJFM 850, 500, 200, 100, 50

NCEP CFSv1** (7) T62, L64, 24 layers above 100 hPa, .2hPa

1981-2006, NDJFM 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10

NCEP CFSv2** (8) T126, L64, 24 layers above 100 hPa, .2hPa

1982-2009, NDJFM 1000, 850, 700, 500, 300, 200, 100, 70, 50, 30, 10

CMAMlo (10) T63, L41, ~31km 1979-2008, NDJF 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10

CMAM** (10) T63, L71, ~100km 1979-2008, NDJF 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1, .5, .4

JMAMRI-CGCM3 TL95, L40 1979-2009, NDJFM 850, 500, 200

L38GloSea4 N96, L38 1989-2002, DJFM 850, 200

L85GloSea4** N96, L85 1989-2009, DJFM 850, 200

POAMA (p24a,b,c) T47, 17 sigma levels 1980-2009, DJFM 850, 500, 200

ENSO-stratosphere Pathway

• Teleconnection associated with ENSO is thought to increase planetary wave driving into the stratosphere (perhaps through linear interference).

• Changes in stratospheric vortex may lead to subsequent changes in tropospheric climate in late winter, particularly over the North Atlantic and Europe.

• Models with better resolved stratospheres may simulate this relationship better.

• See, for example: Cagnazzo and Manzini 2009, Ineson and Scaife 2009, Bell et al. 2009, Garfinkel et al. 2008, 2012, Fletcher and Kushner 2010, etc.

Initial Scientific Questions

•Do models that better resolve the stratosphere better predict NH wintertime climate:

• during all winters? • during active ENSO winters? • during winters with major stratospheric activity?

•Do seasonal forecast models intrinsically capture the ENSO-stratosphere relationship?

Two Techniques

#1. Use hindcasts to evaluate skill of each winter ensemble-mean forecast , as a function of lead time (verifying against MERRA for same time period). #2. Use hindcasts as a large ensemble of individual runs to diagnose mechanistic relationships internal to the model (model fairly quickly loses initial atmospheric information and converges to model climatology).

Correlations with MERRA November values (Lead 1), MSLP Only significant (>95%) correlations shown

ARP

EGEz

00k*

ARP

EGEz

00l

CanC

M3

CanC

M4

CFSv

1*

CFSv

2*

CMA

M*

CMA

Mlo

ARP

EGEz

00k*

ARP

EGEz

00l

CanC

M3

CanC

M4

CFSv

1*

CFSv

2*

CMA

M*

CMA

Mlo

Correlations with MERRA December values (Lead 2), MSLP

Nov Mar

All NH extratropics (>20N), 1982-2006, MSLP

Do we gain predictability during ENSO winters?

Define ENSO years based on standardized NDJ Niño 3.4 index from ERSSTv2b (linearly detrended, 1950-2010). El Niño winters (> 1 stdev): 1957, 1963, 1965, 1972,1982, 1986, 1987, 1991, 1994, 1997, 2002, 2009 La Niña winters (< -1 stdev): 1955, 1973, 1975, 1984, 1988, 1998, 1999, 2007

Nov Mar

All NH extratropics (>20N), 1982-2006, MSLP

Nov Mar

All NH extratropics (>20N), for EN/LN years only (11 winters)

Increase in anomaly correlation greatest for leads 3-5 months.

Nov Mar

Atlantic Basin (>20N, 270W-30E), 1982-2006, MSLP

Nov Mar

Atlantic Basin(>20N, 270W-30E), for EN/LN years only (11 winters)

Lower RMSE means the

forecast is more accurate.

Nov Mar

The models are not skillful at predicting mean sea level-pressure in the extratropics for a lead >1 month. High-top models may have slightly more

accurate predictions of Atlantic-sector MSLP at leads 1-2 months.

NH extratropics Atlantic Basin

Lower RMSE means the

forecast is more accurate.

Nov Mar

NH extratropics Atlantic Basin

EN/LN winters only

There is some improvement in the accuracy of the model forecasts (relative to the climatological forecast) in the Atlantic sector during ENSO years for all leads.

Strongest La Nina

Strongest El Nino

2nd Strongest El Nino

(MSLP for everywhere >20N) Model-average

2nd – 3rd Strongest La Nina

So… ENSO does enhance MSLP predictability at longer lead times in the NH extratropics, including over the Atlantic region- at least for the strongest events. High-top models seemed to have higher accuracy in ENSO years than low-top models by a small margin for leads 1-3 months. But…. is this related to the stratosphere at all? Or are the improvements coming from the troposphere?

Correlations with observed MERRA November values (Lead 1), 50 mb heights Only significant (>95%) correlations shown

ARP

EGEz

00k*

A

RPEG

Ez00

l Ca

nCM

3

CanC

M4

CFSv

1*

CFSv

2*

CMA

M*

CMA

Mlo

Correlations with observed MERRA December values (Lead 2), 50 mb heights Only significant (>95%) correlations shown

ARP

EGEz

00k*

A

RPEG

Ez00

l Ca

nCM

3

CanC

M4

CFSv

1*

CFSv

2*

CMA

M*

CMA

Mlo

Nov Mar

60-90N, 1982-2006, 50mb height anomalies

Nov Mar

60-90N, 1982-2006, 50mb height anomalies, for EN/LN years only (11 winters)

The stratosphere shows only marginal increased skill during ENSO winters, with the biggest increase for 3-month leadtime.

• Consider particular El Niño/La Niña years that had greater predictability and see whether the stratosphere played a role.

• Look at surface temperature, vertical coupling, 500 mb heights, Z*. • Quantify if models with better stratospheric prediction or a stronger

ENSO-stratosphere relationship have better tropospheric skill scores. • Consider whether model spread is lower during ENSO events in the

stratosphere, and if this translates into lower spread in tropospheric prediction.

• Consider role of ENSO amplitude (e.g., Toniazzo and Scaife 2006). • Look at NAO/AO prediction in relation to ENSO. • More closely consider El Niño vs La Niña.

Let’s consider whether the models capture the ENSO-stratosphere relationship in a statistical manner (ie., internal to the model)….

Still need to…..

Observed ENSO Response Polar cap (65-90N) geopotential height anomalies

(linear trends removed, 1980-2009 climatology) regressed onto

standardized NDJ-mean Niño 3.4 index

and composites of Jan-Feb mean sea level pressure anomalies for

NDJ Niño 3.4 > 1 stdev, 1950-2010

NCEP/NCAR

20C

1979-2010 1950-2010 NCEP/NCAR

20C

MERRA

Even reanalysis products aren’t entirely consistent on ENSO-stratosphere relationship….

NCEP/NCAR 20C El

Niñ

o (1

0 ev

ents

) La

Niñ

a (8

eve

nts)

Jan-Feb mean sea level pressure anomalies (1981-2009 climatology), 1950-2010

Simulated ENSO Response in SHFP Polar cap (65-90N) geopotential height bias-corrected anomalies

(linear trends removed) regressed onto

Standardized NDJ-mean Niño 3.4 index

Each model has slightly different set of years being considered. Regressions are calculated for individual model members and then

averaged (so many more samples than in observations).

ARP

EGEz

00k*

ARP

EGEz

00l

CanC

M3

CanC

M4

CFSv

1*

CFSv

2*

CMA

M*

CMA

Mlo

N

NR

20C

ARP

EGEz

00k*

ARP

EGEz

00l

CanC

M3

CanC

M4

CFSv

1*

CFSv

2*

CMA

M*

CMA

Mlo

N

NR 20C

ARP

EGEz

00k*

ARP

EGEz

00l

CanC

M3

CFSv

1*

CFSv

2*

CMA

M*

CMA

Mlo

N

NR 20C

CanC

M4

Strat-Trop Coupling Jan 65-90N geopotential height anomaly at 50mb correlated to anomalies at all other months/levels

ARP

EGEz

00k*

ARP

EGEz

00l

CFSv

1*

CMA

M*

CMA

Mlo

N

NR

MER

RA

CanC

M3

CanC

M4

CFSv

2*

Many questions remain…

• Do we gain predictability during winters with stratospheric events? Can this be explored with monthly-means and with ICs for Nov only? (Andrew and Alexander are exploring this topic).

• How do models respond to QBO? Do we gain predictability there? Most (?) models are not able to maintain the QBO winds they are initialized with…

• How often do models capture extreme stratospheric events? How often, when a model predicts an extreme event, does one actually occur? Daily data at even one stratospheric level is essential for this kind of analysis….

• Do models with better stratospheric predictability have better tropospheric skill scores?

This work has only just begun! There is hope that more models will join in the project and more data will be added (data centers: stratospheric output

would be extremely useful for this project, as well as daily z10 or u10).

If you want to get involved, I can help coordinate efforts. Email me at:

amy.butler@noaa.gov

Thanks to Leigh Zhang, Emily Riddle, Emily Becker, Huug Van den Dool, and Craig Long at CPC for their assistance/guidance in this analysis.