Comparison of GLOSEA5 and POAMA2.4 Hindcasts 1996 … · Fig. 19 Attributes diagram for predictions...

Bureau Research Report - 011

Comparison of GLOSEA5 and POAMA2.4 Hindcasts 1996-2009 Harry H. Hendon, Mei Zhao, Andrew Marshall, Eun-pa Lim, Jing-Jia Luo, Oscar Alves and Craig MacLachlan

December 2015

COMPARISON OF GLOSEA5 AND POAMA2.4 HINDCASTS 1996-2009

i

Comparison of GLOSEA5 and POAMA2.4 Hindcasts 1996-2009

Harry H. Hendon1, Mei Zhao1, Andrew Marshall1, Eun-pa Lim1, Jing-Jia Luo1, Oscar Alves1

and Craig MacLachlan2 1Bureau of Meteorology Melbourne, Australia

2UKMO, Exeter, United Kingdom

Bureau Research Report No. 011

December 2015

National Library of Australia Cataloguing-in-Publication entry

Author: Harry H. Hendon, Mei Zhao, Andrew Marshall, Eun-pa Lim, Jing-Jia Luo, Oscar Alves and Craig MacLachlan

Title: Comparison of GLOSEA5 and POAMA2.4 Hindcasts 1996-2009 ISBN: 978-0-642-70671-3 Series: Bureau Research Report - BRR011


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Enquiries should be addressed to: Harry Hendon Bureau of Meteorology GPO Box 1289, Melbourne Victoria 3001, Australia Contact Email: [email protected]

Copyright and Disclaimer

© 2015 Bureau of Meteorology. To the extent permitted by law, all rights are reserved and no part of

this publication covered by copyright may be reproduced or copied in any form or by any means

except with the written permission of the Bureau of Meteorology.

The Bureau of Meteorology advise that the information contained in this publication comprises

general statements based on scientific research. The reader is advised and needs to be aware that such

information may be incomplete or unable to be used in any specific situation. No reliance or actions

must therefore be made on that information without seeking prior expert professional, scientific and

technical advice. To the extent permitted by law and the Bureau of Meteorology (including each of its

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not limited to all losses, damages, costs, expenses and any other compensation, arising directly or

indirectly from using this publication (in part or in whole) and any information or material contained

in it.


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Contents

Abstract ......................................................................................................................... 1

1. Introduction .......................................................................................................... 1

2. Model Configurations and Hindcasts ............................................................... 4

3. Mean State Comparisons .................................................................................... 9

4. Australian precipitation and temperature skill................................................ 13

5. Climate modes and their teleconnections ....................................................... 25

6. Conclusions and Recommendations for POAMA Development ................... 43

7. References ......................................................................................................... 48


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List of Tables

Table 1 Accuracy scores averaged over all Australian land points for month 1 and months 2-4. .......................................................................................................................................... 18

List of Figures

Fig. 1 Mean rainfall for February, May, August, and November. Top row is based on AWAP. Middle row is month 1 of GLOSEA5 hindcasts. Bottom row is month 1 of P24 hindcasts. Units are mm/day. ................................................................................. 10

Fig. 2 Mean Tmax for February, May, August, and November. Top row is based on AWAP. Middle row is month 1 of GLOSEA5 hindcasts. Bottom row is month 1 of P24 hindcasts. Units are °C .......................................................................................... 10

Fig. 3 Mean Tmin for February, May, August, and November. Top row is based on AWAP. Middle row is month 1 of GLOSEA5 hindcasts. Bottom row is month 1 of P24 hindcasts. Units are °C .......................................................................................... 11

Fig. 4 Month 1 precipitation bias for all start months relative to GPCP (left column) and CMAP (right column) for GLSOEA5 (top row) and P24 (bottom row). Units are mm/day. ........................................................................................................................ 11

Fig. 5 SST bias for months 1 (left column) and month 2 (right column) for GLOSEA5 (top row) and P24 (bottom row). Units are °C. ............................................................... 13

Fig. 6 As in Fig. 5 but for months 3 and 4. ............................................................................... 13

Fig. 7 Anomaly correlation for precipitation for month 1 for February (top left), May (top right), August (bottom left) and November (bottom right) from GLOSEA5 and P24 hindcasts. Verification is against monthly AWAP ¼ degree analyses that was interpolated onto the native grids of each model. .......................................................... 15

Fig. 8 As in Fig. 7 except for Tmax. ......................................................................................... 15

Fig. 9 As in Fig. 7 except for Tmin. .......................................................................................... 16

Fig. 10 As in Fig. 7 except for months 2-4 for precipitation ........................................................ 16

Fig. 11 As in Fig. 7 except for months 2-4 for Tmax. ................................................................. 17

Fig. 12 As in Fig. 10 except for months 2-4 for Tmin. ................................................................ 17

Fig. 13 Accuracy score for predicting precipitation above median for month 1 for February, May, August, and November for GLOSEA5 (top row) and P24 (bottom row). AWAP analyses, interpolated to native model grids, is used for verification. Units are percentage correct. ........................................................................................ 19

Fig. 14 As in Fig. 13 except for Tmax. ....................................................................................... 19

Fig. 15 As in Fig. 13 except for Tmin. ........................................................................................ 20

Fig. 16 Accuracy score for precipitation above median for months 2-4 for MAM, JJA, SON, and DJF for GLOSEA5 (top row) and P24 (bottom row). ..................................... 20

Fig. 17 As in Fig. 16 except for Tmax ........................................................................................ 21

Fig. 18 As in Fig. 16 except for Tmin. ........................................................................................ 21

Fig. 19 Attributes diagram for predictions of above median rainfall for all land points over Australia or month 1 for February, May, August, and November from GLOSEA5 (top row) and P24 (bottom row). Shaded area indicates forecast contribute

SEASONAL DISTRIBUTIONS OF DAILY HEAVY RAIN EVENTS OVER AUSTRALIA

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positively to the Brier skill score. Forecasts that fall on the sloping line would have perfect reliability. Size of dots is proportional to number of forecasts in each bin. Probability bins of width 10% were used. ...................................................................... 23

Fig. 20 As in Fig. 19 except for Tmax. ....................................................................................... 23

Fig. 21 Attributes diagram for SON (mnths 2-4) for above median prediction of precipitation (left hand column), Tmax (middle) and Tmin (right hand column) for all land points over Australia. GLOSEA5 is top row and P24 is bottom row. Plotting convention is as in Fig. 19. ............................................................................... 24

Fig. 22 Accuracy score for above median rainfall for GLOSEA5 and P24 verified against CMAP for month 1 for November (top row). Attributes diagram for above median rainfall using all points over Indo-Pacific domain is shown in bottom row. ..................... 25

Fig. 23 Anomaly correlation (top row) and rmse (bottom row) for monthly Nino34 and IOD indices using all start months. Correlation for persistence forecast is included. ........................................................................................................................ 26

Fig. 24 Predicted amplitude of the Nino34 and IOD indices from GLOSEA5 and P24 for all start months. Amplitude was computed using individual members and then averaged over all members. .......................................................................................... 27

Fig. 25 As in Fig. 24 except for Nino4 and Nino3 indices. ......................................................... 27

Fig. 26 Amplitude of the IOD index for forecasts starting in May, August and November. ......... 28

Fig. 27 Anomaly correlation for the Nino34 index for forecasts starting in May. ........................ 30

Fig. 28 Regression onto Nino34 index of observed rainfall (top row) and ensemble mean rainfall from GLSOEA5 (middle) and P24 (bottom) forecast for months 2-4 for SON. Units are mm/day. ............................................................................................... 30

Fig. 29 Regression onto Nino34 and IOD indices of observed rainfall (top), GLOSEA5 (middle) and P24 (bottom) for months 2-4 for JJA and SON. Units are mm/day. .......... 31

Fig. 30 As in Fig. 29 except for Tmax and Tmin in SON. Units are C. ....................................... 31

Fig. 31 Anomaly correlation for predicting SAM index for November using forecasts initialized on 1st November and 1st August (top). Persistence forecast is shown in blue. Anomaly correlation for months 2-4 (SON) for forecasts initialized on 1st August (bottom). ............................................................................................................ 33

Fig. 32 Time series of November SAM index (top) and SON SAM index (bottom) from observations and forecasts. .......................................................................................... 34

Fig. 33 Correlation (left) and rmse/spread (right) for predicting daily SAM index using all start months for days 1-35. Top row is for forecasts initialized on 9th, middle row combines forecasts from 1st and 9th and bottom row combines 25th, 1st, and 9th. ........... 36

Fig. 34 Comparison of SAM forecast skill using Gong SAM index (top) and by projecting onto observed EOF of SLP (bottom). ............................................................................ 38

Fig. 35 Bivariate correlation for Wheeler-Hendon MJO index for November starts (left) and August starts (right). ............................................................................................... 39

Fig. 36 As in Fig. 35 except for rmse and spread for November forecasts. ............................... 40

Fig. 37 As in Fig. 33 except for predicting the daily blocking index using all start months for days 1-30. ................................................................................................................ 42


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1

ABSTRACT

Multiweek, monthly and seasonal forecast performance of the UKMO GLOSEA5 systems is

compared to the BoM POAMA2.4 system using available hindcasts for the period 1996-2009.

The GLOSEA5 system is based on a state-of-the-art high resolution coupled model (60km

atmosphere and 25 km ocean), which is a marked improvement compared to P24 (250km

atmosphere and 200 km ocean). GLOSEA5 is based updated versions of the component UM

models that have been improved to better represent key climate processes such as the MJO and

to reduce climate drift.

Our assessment reveals a marked increase in forecast performance from GLOSEA5 for

prediction of regional Australian climate (precipitation and temperatures) compared to P24,

especially for the first month of the forecast. A more modest improvement is detected in months

2-4. GLOSEA5 also better predicts multiweek variations of the Southern Annular Mode and

blocking and El Nino at long lead, all of which are important for improved prediction of

regional Australian climate. Forecast reliability from GLOSEA5 is found to be good despite the

limited ensemble size available from the hindcast set.

Based on this assessment a number of recommendations for future development of the POAMA

system are made, including development of the next version of POAMA based on a coupled

model comparable to GLOSEA5, sharing of hindcasts with the UKMO so as to better sample

the full range of interannual variability, and implementation of the POAMA coupled

assimilation system into the GLOSEA5 model, and. A number of model deficiencies in

GLOSEA5 were flagged as well (eg, too strong IOD and ENSO variability, biased ENSO

teleconnection to Australian temperatures, rainfall bias over Maritime Continent) that are

degrading GLOSEA5 forecast performance for Australia and could be worked on jointly with

the UKMO.

1. INTRODUCTION

Operational multiweek-seasonal forecasting at the Bureau of Meteorology is currently based

on ensemble predictions provided by the POAMA seasonal forecasting system. The POAMA

system version P24 (Hudson et al. 2013), which has been operational since 2013, has proven

capability to predict the main drivers of Australian climate variations, including predicting the

occurrence of El Nino and La Nina 2-3 seasons in advance (Zhao et al. 2014) and the Indian

Ocean Dipole up to one season in advance (Shi et al. 2012), interannual variations of the


2

Southern Annular Mode (SAM) up to 2-3 seasons in advance (Lim et al. 2013), and the

Madden-Julian Oscillation up to 3 weeks in advance (Marshall et al. 2012). The forecast model

also faithfully reproduces the impact of these climate drivers on regional climate in Australia

(e.g., Langford and Hendon 2013). Therefore forecasts from P24 provide for skilful prediction

of some components of regional climate in Australia from weeks to seasons in advance

(Langford and Hendon 2013).

The POAMA seasonal forecast system consists of coupled models of the ocean, atmosphere

and land surface and an initialization/assimilation system that provides initial conditions with

perturbations so as to facilitate ensemble predictions that attempt to account for forecast

uncertainty arising from errors in the initial conditions. The P24 system uses a state of the art

ensemble assimilation/initialization system (Yin et al. 2011; Hudson et al. 2013; Zhao et al.

2014) and also accounts for forecast uncertainty due to model errors by running 3 different

versions of the model (Langford and Hendon 2013). However, the capability of the P24 system

is limited by the relatively coarse horizontal resolution of the component models (~250 km

horizontal resolution in atmosphere and 200 km in ocean; vertical resolution in both

component models is relatively coarse as well). The coarse resolution causes some aspects of

regional climate in Australia to not be well resolved. The coarse resolution of P24 also results

in some of the key climate drivers of relevance to Australian climate, such as the IOD and the

MJO, to not be faithfully represented. Furthermore, many of the physical parameterizations in

the component models in P24 are outdated, largely being based on model developments

upwards of 30 years ago.

The next version of the POAMA system is under development and will include upgrades to the

assimilation/initialization system and to the component models. A major change to the

POAMA system will be adoption of the model components provided by the ACCESS

program, which is a joint modelling partnership between the BoM, CSIRO, UKMO, and

Australian Universities. The first version of the ACCESS coupled model, referred to as CM1

(Bi et al. 2013), is based on the UKMO Unified Atmospheric Model equivalent to the versions

used in HadGEM2 or GA1, the AUSCOM ocean model that is derived from the GFDL

MOM4, the CICE sea ice model, and either the CABLE land surface model (ACCESS 1.3) or

the UKMO land surface model (ACCESS 1.0). More detail of these component models is

provided in Sec. 2. The atmosphere and ocean model resolutions in ACCESS CM1 are roughly

150km and 100km, respectively, which represent a marked increase over POAMA2.4 (250 km


3

and 200 km, respectively). Furthermore, ACCESS CM1 uses 38 vertical levels in the

atmosphere compared to 17 in POAMA2.4, meaning that the stratosphere, and tropospheric

boundary layer, and tropopause are much better resolved.

A preliminary version of POAMA has been developed based on ACCESS CM1 and some

seasonal forecast performance using a rudimentary initialization scheme has been reported as

part of the WIRADA project (Zhao et al. 2013). That assessment was based on a small set of

hindcasts that were initialized without making use of the full POAMA

assimilation/initialization scheme, and so the forecast performance of POAMA based on

ACCESS CM1 is probably a conservative indication of what might be achieved once the full

POAMA assimilation/initialization scheme is implemented. Nevertheless, a key finding of

that study is that at least for the first month of the forecast (before coupled initialization

matters) performance of POAMA based on ACCESS CM1 is expected to only be marginally

improved compared to POAMA2.4. The limited improvement of POAMA3 over POAMA2

was thought to be mainly due to an only modest increase in resolution for POAMA3 over

POAMA2 and the use of relatively older model components in ACCESS CM1. Zhao et al

(2013) further noted that some substantial biases, especially in the mean rainfall over the

Indian Ocean/Maritime Continent, remain in ACCESS CM1 that may hinder seasonal forecast

performance. And, although the ACCESS CM1 system experiences substantially less climate

drift than POAMA2.4, the representation of El Nino, the Indian Ocean dipole and the Madden

Julian Oscillation and their teleconnections to Australian climate were either on par or in some

instances worse than that in POAMA2.4. Substantial development of the UKMO Unified

atmospheric Model that occurred after configuration of ACCESS CM1 addressed some of

these shortcomings and Zhao et al (2013) suggested that these developments be trialled in

POAMA prior to finalizing a version of POAMA based on ACCESS. Furthermore, based on

experience at the UKMO with higher resolution models, Zhao et al (2013) also suggested

trialling POAMA seasonal forecasts systems with substantially higher resolution than used in

ACCESS CM1.

The development of a new, higher resolution version of the ACCESS coupled model is

ongoing and will use updated versions of the UM code. Work is also ongoing to implement the

POAMA assimilation/initialization scheme in the ACCESS CM1 model and forecast

performance will subsequently be investigated. In the meantime, the UKMO has moved to a

high resolution version of their coupled model (GC1) for their seasonal forecast system


4

(GLOSEA5), which now can be considered the state of the art for seasonal prediction

(MacLachlan et al. 2014). The GLOSEA5 system is based on more recent versions of the

UKMO component models than used in ACCESS CM1 (as well as using a different ocean

model). The UKMO intends to upgrade to GLOSEA6 using the most recent version of their

coupled model GC2 based on the Unified model GA6 in February 2015. Nevertheless, the

resolution of GLOSEA6 will be the same as GLOSEA5 system and this is much higher than

we can currently afford at BoM. However, new supercomputing upgrades are to become

available at BoM in late 2016, and the POAMA system should be able to be run at BOM at the

resolution of GLOSEA5/GLOSEA6. Hence, evaluation of the forecasts performance of the

GLOSEA5 system for seasonal prediction of Australian climate can provide a benchmark for

what might be achievable with a POAMA system based on the most recent model upgrades

available from the UKMO and run at the highest affordable resolution.

We report here on some comparison of forecast performance of the POAMA2.4 system with

the GLOSEA5 system based on available hindcasts for the period 1996-2009. The intent of

this comparison is to provide guidance for future development of POAMA based on improved

model components with higher resolution from the UKMO as provided by ACCESS. More

detail on POAMA2.4 and the GLOSEA5 systems, and their available hindcasts for study here

is provided in Sec. 2. Comparison of mean state biases is provided in Sec. 3. Assessment of

prediction of regional Australian climate is provided in Sec. 4. Assessment of prediction of key

drivers of Australian climate is provided in Sec. 5. Recommendations for future development

of POAMA, based on the findings of this study, are provided in Sec. 6.

2. MODEL CONFIGURATIONS AND HINDCASTS

a. POAMA2.4

POAMA is the BoM atmosphere-ocean coupled-model seasonal forecast system. The

atmospheric component of POAMA is the BoM’s Atmospheric Model version 3 (BAM3;

Colman et al. 2005), which is a spectral model with roughly ~ 250 km horizontal resolution on

17 vertical levels (T47L17). The land surface component carries three soil temperature levels

with simple bucket hydrology (Manabe and Holloway 1975, Hudson et al. 2011). The ocean

component is the Australian Community Ocean Model version 2 (Schiller et al. 2002), which

is based on the GFDL modular ocean model MOM2 (Pacanowski 1996). The ocean model has

horizontal resolution of 2° in the zonal direction and 0.5° in the meridional direction in the


5

tropics that increases to 1.5° towards the poles. ACOM2 has 25 vertical levels. BAM3 and

ACOM2 are coupled by the Ocean Atmosphere Sea Ice Soil (OASIS) software (Valke et al.

2000).

The POAMA version used in this study is version 2.4 (P24; e.g., Hudson et al. 2013), which

became the operational forecast model for seasonal outlooks in 2013. P24 forecasts are

initialised with realistic atmosphere and ocean conditions that are generated from separate

atmosphere/land surface (Hudson et al. 2011) and ocean (Yin et al. 2011) data assimilation

systems. The atmosphere-land surface assimilation consists of a nudging of the model

atmospheric fields (u, v, T, and q) towards an existing analysis every 6 hours. For the period

1980-2002, the ERA-40 reanalyses (Uppala et al. 2005) are used, and for the period 2003-

onward the analyses from the BoM’s operational numerical weather prediction are used.

Through this nudging process the land surface is realistically initialised in response to the

model generated surface fluxes.

The ocean assimilation is provided by the PEODAS system, which is a simplified form of an

Ensemble Kalman Filter (Yin et al. 2011). In situ temperature and salinity observations are

assimilated to a central run of the ocean model that is forced by re-analysis surface fluxes. An

ensemble of ocean states is generated by perturbing the surface fluxes within a possible range

of observational errors on intra-seasonal time scales. This ensemble of ocean states is used to

compute the background error covariances for temperature and salinity. Using these

background error covariances, observed data are assimilated to the central run. After each

analysis cycle (three days), the ensemble members are nudged toward the analysis from the

central run.

In P24, perturbed atmosphere-land-ocean initial conditions are created by running an ensemble

of coupled model simulations whereby the central member is constrained to the atmosphere-

land-ocean analysis from ALI and PEODAS. The other ensemble members are free to evolve

but are relaxed back to the central member once per day, with the strength of the relaxation

prescribed so as to allow the ensemble spread to span analysis uncertainty. In this fashion, an

ensemble of perturbed coupled initial conditions are created that span observational

uncertainty and which are suitable for generation of ensemble forecasts for multiweek-seasonal

lead times. Forecast uncertainty due to model error is addressed in P24 by running three

different versions of the coupled model, referred to as P24a, P24b and P24c. P24 uses an

ensemble of 33 members, whereby one unperturbed and 10 perturbed members are run from


6

each of the three model versions. Real time forecasts out to 270 days are initialized twice per

week and hindcasts are created every 5 days (6 times per month).

b. GLOSEA5

GLOSEA5 (MacLachlan. et al. 2014) is based on the UKMO coupled model HadGEM3,

which consists of the Unified Atmospheric Model version GA3 (Walters et al. 2011; Brown et

al., 2012), the JULES versions 3.0 Land surface ( Joint UK Land Environment Simulator

JULES; Best et al., 2011), the NEMO Ocean model version 3 (Madec, 2008; Megann et al.

2014), and the Los Alamos Sea Ice Model (CICE; Hunke and Lipscomb 2010) version 3.

Major upgrades over the GLOSEA4 system include improved model physics, a fully resolved

stratosphere, initialized sea ice and increased horizontal resolution.

A detailed description of the Global Atmosphere 3.0 configuration is given in Walters et al.

(2011). There have been numerous changes to the physical parametrizations since GA2 (and

its predecessor GA1 used in ACCESS CM1): introduction of cloud inhomogeneity, reduction

of spurious drizzle, reduction of spurious deep convection, introduction of the JULES land

surface model (Blyth et al., 2006), and the facility to read iceberg calving ancillary data. The

dynamical core is updated as well (called NewDynamics), which uses a semi-implicit semi-

Lagrangian discretization to solve the fully compressible, non-hydrostatic atmospheric

equations of motion. The stochastic physics scheme Stochastic Kinetic Energy Backscatter v2

is included to represent unresolved processes and provide small grid-level perturbations during

the model integration. Climate forcings for trace gases and insolation are set to observed

values up to the year 2005; after this point the emissions follow the Intergovernmental

Panel on Climate Change (IPCC) RCP4.5 scenario. Climatologies with a seasonal variation are

used for other aerosols (biogenic aerosols, biomass burning, black-carbon, sea salt, sulphates,

dust, and organic carbon fossil fuels). These climatologies have been generated from a climate

simulation using HadGEM2 (except dust which is from a HadGEM1a run). The observational

climatology is used for ozone, which includes a seasonal cycle.

The atmospheric resolution of HadGEM3 for GloSea5 has been increased from 1.88◦ longitude

×1.25◦ latitude in GLOSEA4 (and ACCESS CM1) to 0.83◦×0.56◦ (i.e. from approximately

150 km in midlatitudes to 50 km). The higher-resolution version of HadGEM3 requires a

reduced time step and altered diffusion settings to increase stability.


7

HadGEM3 uses the updated NEMO ocean model version 3 run with horizontal resolution ¼

degree globally and 75 vertical levels (Megann et al. 2014). The higher horizontal resolution

means some of the major closed seas (Great Lakes, Lake Victoria, Caspian Sea and the Aral

Sea) are included. Increased horizontal resolution in the ocean model also means the western

boundary currents are better resolved leading to less bias in the mid latitude storm tracks

(Scaife et al. 2011). Tropical instability waves, which play a key role in the heat budget of the

equatorial Pacific cold tongue, are also better resolved , thereby leading to better of El Nino

and its teleconnections to the extratropics (Dawson et al. 2012). Vertical resolution of the

ocean model is also increased using 75 levels to better resolve the mixed layer and

thermocline (resolution is approximately 1 m near the surface and increases to 200 m at 6000

m depth). A detailed evaluation of the high resolution ocean model in GLOSEA5 will be

reported in a separate MCV study.

The initial atmospheric conditions for the GLOSEA5 forecast members are generated by the

Met Office operational numerical weather prediction (NWP) 4D-Var data assimilation system

(Rawlins et al., 2007). In hindcast mode, reanalyses from the European Centre for

Medium-Range Weather Forecasts (ECMWF) ERA-Interim project (Dee et al. 2011) are

used to initialize the atmosphere and land surface.

GloSea5 uses the Forecast Ocean Assimilation Model (FOAM) Ocean Analysis (Blockley et

al., 2013) to initialize the ocean and sea-ice components for the real-time forecasts. An

equivalent product, the GloSea5 Ocean and Sea Ice re-Analysis, a 23 year (1989 – 2011)

reanalysis, supplies initial conditions for the hind-casts. Both ocean analyses use the new

NEMOVAR (Mogensen et al., 2012; Waters et al., 2014) assimilation scheme developed

jointly by the UK Met Office, Centre Europeén de Recherche et de Formation Avanceé en

Calcul Scientifique (CERFACS), ECMWF, and Institut National de Recherche en

Informatique et en Automatique/Laboratoire Jean Kuntzmann (INRIA/LJK).

The assimilation system (based on the NEMOVAR scheme) used to create the forecast and

hindcast analyses has the same ocean and sea-ice model (NEMO/CICE ORCA 0.25 L75) as

the coupled model used in GloSea5. The ERA-Interim atmospheric re-analysis (Dee et al.,

2011) is used as boundary forcing for the hindcast ocean reanalysis. NEMOVAR is a

multivariate, incremental 3D-Var first guess at appropriate time (FGAT) system. The system

implemented at the Met Office operates on a daily cycle with a 1 day time window and uses an

incremental analysis step. It assimilates satellite and in situ observations of sea-surface


8

temperature (SST), sea- level anomaly satellite data, sub-surface temperature and salinity

profiles, and satellite observations of sea-ice concentration. The temperature, salinity and sea-

level observations are assimilated in a multivariate fashion using balance relationships between

the variables (hydrostatic and geostrophic balance, plus preservation of the temperature –

salinity relationship in density), while the sea- ice concentration is assimilated as a univariate

field. Furthermore, bias correction schemes are implemented to reduce the bias inherent in

satellite measurements of SST (Martin et al., 2007; Donlon et al., 2012) and to reduce the bias

in the (supplied) mean dynamic topography correction required to convert measurements of

sea-level anomaly into sea-surface height (Lea et al., 2008).

Real-time forecasts from GLOSEA5 are created every day, whereby four members are

initialized from the 0000 UTC analyses from the NWP global data assimilation and the ocean

– sea-ice data assimilation system. Two of these members are run out to 210 days (seasonal

forecast members) and two are run out to 60 days (intraseasonal members). Spread between

members initialized on the same date is achieved only through the use of a stochastic physics

scheme (ie no perturbations to the initial conditions).

c. Monthly and Seasonal Hindcasts

For the analysis of month 1 and months 2-4 forecasts, output from the GLOSEA5 hindcasts

was used for the period 1996-2009 for forecasts starts on the 1st of February, May, August and

November, and on the 25th of each month prior. Four members are used from each start time.

We form an 8 member ensemble from GLOSEA5 by using the four forecasts from the 1st and

four forecasts from the previous 25th, throwing away the first 5-6 days of the forecasts. We

refer to the first month of the forecast as lead time 1 (or sometimes lead time 0, but usage

should be clear). We refer to the three-month mean formed from months 2-4 of these forecasts

as lead time 2-4. To facilitate comparison of monthly and seasonal forecasts with P24, we

make a similar 8 member ensemble from P24 using 4 members from a single model version

(P24a) on the 1st of the month and from the 26th of the preceding month.

d. Daily-Multiweek Hindcasts

For evaluation of daily-multiweek forecasts, we form an ensemble from GLOSEA5 as for the

seasonal assessment (eg 4 ensemble members from each start date) but use starts from the

25th, 1st and 9th, taking the 9th of the month as the first day of the forecasts (ie we throw


9

away the first 8 days of forecast from the start on the 1st and the first 13 days of forecast for

the start on the 25th). We note that for starts in May and November, GLOSEA5 hindcasts

provide 8 members for each start time. We include these additional 4 members for May and

November. For comparison with P24, we use the 10 member ensemble from the 11th of each

month from model version P24a.

3. MEAN STATE COMPARISONS

a. Australian Precipitation and Temperature

We begin by examining the mean precipitation (Fig. 1), Tmax (Fig. 2) and Tmin (Fig. 3) over

Australia from the GLOSEA5 and POAMA2.4 hindcasts for month 1 for times in February,

May, August and November for 1996-2009. Similar distributions are seen using lead times 2-4

(not shown). We compare to observations as provided by the 1/4 degree AWAP analyses of

Australian precipitation and temperatures (Jones et al. 2009). The higher resolution afforded

by GLOSEA5 is immediately apparent with Tasmania being resolved in contrast to it

appearing as ocean points in POAMA2.4 (only “land values” are plotted in Fig. 1, 2, and 3 and

so Tasmania is left blank in the P24 plots). For precipitation (Fig. 1), the strong dry bias over

northern Australia in the warm seasons in POAMA2.4 is greatly reduced in GLOSEA5, and

some key regional features, including the strong wintertime maximum on the western side of

Tasmania and along the Great Dividing Range in Victoria and New South Wales, the coastal

maximum along east coast, and the cool season maximum in south west Western Australia are

all resolved in GLOSEA5 and absent in POAMA2.4. Hence, the higher resolution of

GLOSEA5 greatly improves the depiction of mean precipitation throughout Australia.

For Tmax (Fig. 2), GLOSEA5 also provides improved depiction of regional features, including

Tasmania and the costal minimum along the east coast and Great Dividing Range. However,

GLOSEA5 typically suffers from a cold bias relative to the AWAP analyses and is no better

than POAMA2.4 in this regard. For Tmin (Fig. 3), POAMA2.4 exhibits a strong overall warm

bias, which is largely alleviated in GLOSEA5, implying that the diurnal temperature range is

much better depicted in GLOSEA5 as well as key regional features along the Great Dividing

Range and coastal regions. Overall, GLOSEA5 is clearly an enormous improvement over

POAMA2.4 for depiction of the mean climate over Australia.


10

Fig. 1 Mean rainfall for February, May, August, and November. Top row is based on AWAP. Middle row is month 1 of GLOSEA5 hindcasts. Bottom row is month 1 of P24 hindcasts. Units are mm/day.

Fig. 2 Mean Tmax for February, May, August, and November. Top row is based on AWAP. Middle row is month 1 of GLOSEA5 hindcasts. Bottom row is month 1 of P24 hindcasts. Units are °C


11

Fig. 3 Mean Tmin for February, May, August, and November. Top row is based on AWAP. Middle row is month 1 of GLOSEA5 hindcasts. Bottom row is month 1 of P24 hindcasts. Units are °C

Fig. 4 Month 1 precipitation bias for all start months relative to GPCP (left column) and CMAP (right column) for GLSOEA5 (top row) and P24 (bottom row). Units are mm/day.


12

b. Global Precipitation

The bias in global precipitation, using all 4 start months at lead time 1 month, is shown in Fig.

4 relative to the GPCP analyses (left hand column) and CMAP analyses (right hand column).

There are significant differences in the observed climatology based on these two analyses, with

the CMAP analyses generally being wetter in the mean over tropical latitudes than are the

GPCP analyses. Hence, the dry bias over the Maritime Continent in GLOSEA5 is more

prominent when assessed relative to CMAP than to GPCP. Similarly, the wet bias in the

western Indian Ocean in GLOSEA5 is more prominent when assessed relative to GPCP than to

CMAP. Overall, both GLOSEA5 and POAMA2.4 show large biases in the ITCZ’s, which

appear to result from positional errors, but in general, the biases in GLOSEA5 are less than in

POAMA2.4. For instance, the extreme dry bias over the Amazon in POAMA2.4 is absent in

GLOSEA5 and the overall biases in the tropical Pacific are lower in GLOSEA5. In summary,

GLOSEA5 has generally lower precipitation biases throughout the Tropics compared to

POAMA2.4 but significant biases remain in GLOSEA5 that will affect capability to predict

Australian climate (e.g the dry bias over the Maritime Continent means that the teleconnection

of the IOD to Australia will be underdone). That significant biases remain in GLOSEA5,

despite the use of high resolution and significant upgrades to model physics, emphasizes the

daunting task of alleviating precipitation biases. Continued development of the UM

atmospheric model, especially the parameterization of moist processes and clouds, is required.

c. Tropical SST Biases

Mean tropical SST is a key field to correctly simulate in order to faithfully predict ENSO and

its teleconnections. The mean biases at lead times 1 and 2 months are shown in Fig. 5 and at

lead times 3 and 4 months in Fig. 6. POAMA2.4 is well known to exhibit “global cooling”,

with significant tropical wide cold biases developing by lead time 2 and continuing to grow

through lead time 4. Importantly, however, POAMA2.4 exhibits little in the way of a tropical

eastern Pacific cold tongue bias (ie, the cooling is largely uniform). In contrast, GLOSEA5

exhibits little in the way of overall SST drift, which is a marked improvement over

POAMA2.4. However, GLOSEA5 suffers from a prominent, classical, cold tongue bias in the

eastern Pacific, which might affect depiction of the distinction of classical El Nino events from

Modoki or warm pool El Nino events. The cold tongue bias may also impact the depiction of

ENSO dynamics, thereby resulting in biases in the amplitude of El Nino variability (addressed

in Sec. 5 and in a forthcoming MCV report that focuses on GLOSEA5 ocean components).


13

Fig. 5 SST bias for months 1 (left column) and month 2 (right column) for GLOSEA5 (top row) and P24 (bottom row). Units are °C.

Fig. 6 As in Fig. 5 but for months 3 and 4.

4. AUSTRALIAN PRECIPITATION AND TEMPERATURE SKILL

We begin by assessing predictions of Australian precipitation and temperatures in the first

month of the forecast (lead time 1 month). We validate using correlation and accuracy score

against the AWAP ¼ degree analyses. We interpolate the AWAP analyses onto the respective

model grids so that the detail afforded by the high resolution in GLOSEA5 and low resolution


14

in POAMA2.4 are maintained. For correlation we use the ensemble-mean forecast anomaly

and correlate with observed anomaly. For accuracy score, we first create a probability forecast

for being above median. The accuracy score is defined as the number of hits plus correct

negatives divided by total number of forecasts, where an ensemble forecast is scored as a hit if

the ensemble forecast was for greater than 50% of the members predicting above median

rainfall and observed rainfall exceeded median. Similarly, a correct negative is if the ensemble

forecast was for less than 50% chance of exceeding median rainfall and observed rainfall was

less than median. Median thresholds for the forecast are based on the hindcast climatology,

while median values for verification are based on the observed climatology. In this fashion the

forecasts are “calibrated” to have correct median values. The ensemble forecast is formed by

counting the number of forecast members that predict above median rainfall and creating a

probability by dividing by the total number of ensemble members.

a. Correlation

We show correlation skill for the first month of the forecasts for precipitation (Fig. 7), Tmax

(Fig. 8) and Tmin (Fig. 9). Note now that the forecast skill over Tasmania is plotted for P24

results, even though there is no land point in P24 for Tasmania. This score reflects the P24

forecast for the ocean point that covers Tasmania. For precipitation, Tmax and Tmin,

GLOSEA5 exhibits a clear advantage over P24 across Australia, both in terms of higher

correlations and more spatial detail. Rainfall skill tends to be higher in both systems in the cool

seasons especially in the east. For Tmax and Tmin, GLOSEA5 exhibits uniformly higher skill

in all seasons.

Correlation skill for seasonal forecasts (months 2-4 of the forecasts) are shown for

precipitation (Fig. 10), Tmax (Fig. 11) and Tmin (Fig. 12). Forecast skill is lower than for the

first month of the forecasts, and precipitation and Tmin skill drops faster than Tmax skill for

both systems. Nonetheless, there are large areas of positive skill, with GLOSEA5 still

exhibiting distinctive advantages over P24. It is interesting to note than some areas of

relatively strong negative skill show up, which could be due to model systematic errors in

representing remote impacts of ENSO, for instance, or maybe due to sampling errors as a

result of the relatively short hindcast record analysed here (1996-2009).


15

Fig. 7 Anomaly correlation for precipitation for month 1 for February (top left), May (top right), August (bottom left) and November (bottom right) from GLOSEA5 and P24 hindcasts. Verification is against monthly AWAP ¼ degree analyses that was interpolated onto the native grids of each model.

Fig. 8 As in Fig. 7 except for Tmax.


16

Fig. 9 As in Fig. 7 except for Tmin.

Fig. 10 As in Fig. 7 except for months 2-4 for precipitation


17

Fig. 11 As in Fig. 7 except for months 2-4 for Tmax.

Fig. 12 As in Fig. 10 except for months 2-4 for Tmin.


18

b. Accuracy score

Accuracy scores (correct positive and correct negatives) for above median prediction of

precipitation (Fig. 13), Tmax (Fig. 14) and Tmin (Fig. 15) for the first month of the forecast

also reveal a distinctive advantage of GLOSEA5 over P24. Table summarizes the mean

accuracy score over all Australian land points. For month 1, GLOSEA5 accuracy typically

exceeds P24 accuracy by 5-10%. However, for months 2-4 (Figs. 16-18 and Table 1) the

advantage of GLOSEA5 over P24 is much more modest. Accuracy scores for month 2-4 for

Tmax (Fig. 17) are consistently better than for precipitation, with GLOSEA5 showing

consistent improvement over P24. In both systems, scores for Tmin (Fig. 18) are lower than

for Tmax.

Prc Tmax Tmin

e24a glosea5 e24a glosea5 e24a glosea5

0201 0.54 0.62 0.62 0.73 0.62 0.67

0501 0.54 0.62 0.65 0.74 0.64 0.69

0801 0.57 0.64 0.56 0.66 0.54 0.67

1101 0.59 0.63 0.65 0.70 0.61 0.67

MAM 0.54 0.54 0.60 0.60 0.53 0.52

JJA 0.51 0.54 0.55 0.60 0.51 0.51

SON 0.59 0.62 0.58 0.63 0.53 0.51

DJF 0.53 0.50 0.61 0.63 0.56 0.59

Table 1 Accuracy scores averaged over all Australian land points for month 1 and months 2-4.


19

Fig. 13 Accuracy score for predicting precipitation above median for month 1 for February, May, August, and November for GLOSEA5 (top row) and P24 (bottom row). AWAP analyses, interpolated to native model grids, is used for verification. Units are percentage correct.



20


Fig. 16 Accuracy score for precipitation above median for months 2-4 for MAM, JJA, SON, and DJF for GLOSEA5 (top row) and P24 (bottom row).


21

Fig. 17 As in Fig. 16 except for Tmax



22

c. Reliability

Forecast reliability is assessed by constructing attributes diagrams based on predictions of

above median. Forecast probability bins of width 0.10 are used and compiled over all

Australian land points. Comparison of forecast reliability between the two systems is

problematic because the GLOSEA5 hindcasts have limited start times and members and so

reliability estimates are subject to sampling uncertainty (ie the plots are noisy). In order to

facilitate comparison of the two systerms, we chose to use only 8 members from P24 (4 from

the 1st and 4 from 26th) and from only one model version, which will severely reduce the

reliability attained in the operational P24 system that is based on a 33 member ensemble (11

members from 3 model versions) using 2 starts per week. Nonetheless, this comparison is

primarily to give an indication of how well the combination of lagged starts (25th and 1st of the

month) and stochastic physics is performing in GLOSEA5 for providing reliable forecasts.

Reliability for month 1 forecasts is shown in Fig. 19 for precipitation and in Fig. 20 for Tmax

(Tmin is similar). For this comparison, GLOSEA5 outperforms P24 for all 3 parameters and

for all months. This presumably stems from the use of stochastic physic in GLOSEA5 but

only one model version in P24. The reliability achieved with P24 using the full 33 member

ensemble based on 3 model versions is pronouncedly better than that depicted in Figs. 19 and

20. Nonetheless, the GLOSEA5 results are encouraging in that the combination of an

improved model, lagged starts and stochastic physics (even though ensemble is small) are

resulting in uniformly reliable forecasts.

The results from month 1 carry over to seasonal forecasts. We show results for spring season

in Fig. 21. GLOSEA5 forecasts of precipitation and Tmax exhibit good reliability, but Tmin is

overconfident (emphatic) and with little sharpness, perhaps reflecting the systematic error in

mean Tmin in GLOSEA5 discussed in Sec. 3. Reliability of the GLOSEA5 seasonal forecasts

is also uniformly better than P24a, again acknowledging that reliability of the POAMA system

that is run in operations is much better than depicted here due to the use of three model

versions and a larger ensemble.


23

Fig. 19 Attributes diagram for predictions of above median rainfall for all land points over Australia or month 1 for February, May, August, and November from GLOSEA5 (top row) and P24 (bottom row). Shaded area indicates forecast contribute positively to the Brier skill score. Forecasts that fall on the sloping line would have perfect reliability. Size of dots is proportional to number of forecasts in each bin. Probability bins of width 10% were used.



24

Fig. 21 Attributes diagram for SON (mnths 2-4) for above median prediction of precipitation (left hand column), Tmax (middle) and Tmin (right hand column) for all land points over Australia. GLOSEA5 is top row and P24 is bottom row. Plotting convention is as in Fig. 19.

d. Global context

Predictions of Australian precipitation are placed in a global context by examining forecast

skill across the Indo-Pacific. Both forecast systems show highest skill in over the tropical

Pacific, where ENSO variations dominate but GLOSEA5 shows a distinctive advantage over

P24 in the equatorial central and eastern Pacific. An example of the accuracy score for above

median rainfall for November is shown in Fig. 22. GLOSEA5 clearly outperforms P24

throughout the central and eastern Pacific, which presumably reflects better depiction of ENSO

variations in GLOSEA5 despite the cold tongue bias as discussed in Fig. 5. Importantly,

precipitation in this region affects tropical circulation and so can result in improved prediction

of the evolution of ENSO as well as remotely forced teleconnections, although probably not of

direct relevance to Australia because they are too far east. Forecast reliability for above median

rainfall across the Indo-Pacific domain is good in both systems, with GLOSEA5 again

showing a slight advantage.


25

Fig. 22 Accuracy score for above median rainfall for GLOSEA5 and P24 verified against CMAP for month 1 for November (top row). Attributes diagram for above median rainfall using all points over Indo-Pacific domain is shown in bottom row.

5. CLIMATE MODES AND THEIR TELECONNECTIONS

The capability to predict some key modes of climate variability and some of their

teleconnections, specifically ENSO, the IOD, SAM and the MJO are now assessed

a. ENSO and IOD

Forecast skill for ENSO and the IOD are assessed using monthly means for all 4 available start

months from GLOSEA5 (Feb, May, Aug, and Nov). Correlation (top row) and root mean

square error (rmse; bottom row) for the Nino34 index (left column) and DMI (right column)

are shown in Fig. 23. GLOSEA5 shows slightly improved correlation but slight worse rmse for

Nino34 as compared to P24. GLOSEA5 shows a distinct improvement for correlation of IOD


26

compared to P24 but similar rmse. The discrepancy between improved correlation in GLOSEA

but worsened or similar rmse is explained by the simulation of the amplitude of these indices

in the respective systems (Fig. 24, based on the mean standard deviation using each ensemble

member). The GLOSEA5 system simulates stronger then observed Nino34 and IOD

amplitude, whereas the P24 system is slightly damped. Hence, the improved correlations in

GLOSEA5 indicate better depiction of temporal variability, but the worse rmse indicates

poorer performance with amplitude. The overestimation of the Nino34 index in GLOSEA5 is

symptomatic of ENSO variability that is too strong in the eastern Pacific, as seen in Fig. 25

that shows amplitude of Nino4 in GLOSEA is only slightly above normal but Nino3 is much

stronger than observed. This stronger-than-observed ENSO variability in GLOSEA5 may stem

from the strong cold tongue bias in GLOSEA (eg. Figs. 5 and 6) but further investigation is

required.

Fig. 23 Anomaly correlation (top row) and rmse (bottom row) for monthly Nino34 and IOD indices using all start months. Correlation for persistence forecast is included.


27

Fig. 24 Predicted amplitude of the Nino34 and IOD indices from GLOSEA5 and P24 for all start months. Amplitude was computed using individual members and then averaged over all members.

Fig. 25 As in Fig. 24 except for Nino4 and Nino3 indices.

Similarly, for the IOD, GLOSEA5 simulates stronger than observed behaviour which is

primarily due to over-growth of the IOD during the austral winter season (Fig. 26). GLOSEA5

faithfully captures the peak amplitude of the IOD in austral spring and the demise of the IOD

in summer. The stronger than observed IOD variability in GLOSEA5 from early winter might

stem from the rainfall bias in the Indian Ocean (Fig. 4), which would promote an easterly wind

bias in the eastern Indian Ocean, resulting in a spuriously shallow thermocline along

Java/Sumatra, thereby acting to make SST in the eastern pole of the IOD too sensitive to

thermocline variations. However, this speculation needs to be further investigated.


28

Fig. 26 Amplitude of the IOD index for forecasts starting in May, August and November.

The improved predictions of Nino34 in GLOSEA5 (Fig. 23) are seen to primarily occur for

forecasts initialized in May (Fig. 27). This is an outstanding improvement over P24 because

forecast starts in May are at the time of the year when forecast skill for ENSO is the lowest (ie

the "spring predictability barrier"). For this reason alone GLOSEA5 appears to offer a

substantial advantage over P24 for predicting Australian climate because ENSO impacts in

Australia are strongest in winter and spring and so improved prediction of ENSO from May

suggests potential increasing the lead time of skilful predictions of Australian climate.

Of course, it is not sufficient to simply predict the SST anomalies associated with ENSO and

IOD in order to translate into skilful prediction of regional climate: the teleconnections of

ENSO must also be well captured. The depiction of the tropical rainfall anomalies during

ENSO and IOD are shown in Fig. 28 for the SON season based on forecast starts on 1 August.

The rainfall anomalies are shown as the regression onto the predicted Nino34 and IOD indices.

Compared to the observed anomalies (top row of Fig. 28), GLOSEA5 shows a westward shift

of the primary anomaly in the western/central Pacific, despite the fact that the ENSO SST

anomaly in GLOSEA5 shows limited westward bias (Dawson et al. 2012). This westward shift

of the rainfall probably stems from the cold tongue bias in GLOSEA, so that although the SST

anomaly during ENSO is well predicted, the cold tongue bias will act to shift the rainfall

anomalies westward. P24 shows a similar westward bias of the rainfall anomaly, despite the

fact that the P24 ENSO SST anomaly is shifted west (Hendon et al. 2009). However, P24 has

little cold tongue bias (rather the SST bias is more uniform cooling), so the mean state bias in


29

P24 will not act as strongly as in GLOSEA5 to shift the ENSO rainfall anomaly westward.

However, P24 shows limited capability to capture the contracted ITCZ in the eastern Pacific

during El Nino, whereas this feature is well captured in GLOSEA5. The opposite signed

rainfall anomaly in the Maritime Continent and eastern Indian Ocean is weaker in GLOSEA5

than observed or in P24. This is further highlighted by the weaker than observed rainfall

anomalies in GLOSEA5 in these regions associated with the IOD. These weaker anomalies

may reflect the dry bias in this region in GLOSEA5, but further investigation is required.

The rainfall teleconnections to Australia are shown in Fig. 29 for the key winter (JJA) and

spring (SON) seasons when ENSO and IOD impacts are most significant. Both GLOSEA5

and P24 under-represent the impacts of ENSO and IOD during winter, however, it is

encouraging to see a good depiction of the IOD impact along the Great Dividing Range in the

high resolution GLOSEA5. In spring, the observed impacts of ENSO and IOD are stronger and

similar in both systems, although the stronger signal in the SE associated with the IOD

compared to ENSO during SON seems better depicted in P24. This might reflect a better

depiction of the tropical IOD rainfall anomalies in P24 compared to GLOSEA5, which are the

source of the teleconnection in SE Australia depicted in Fig. 29.

Temperature teleconnections of ENSO and IOD for Tmax and Tmin during SON, which is the

season of strongest impact, are shown in Fig. 30. For Tmax, both systems show equally good

teleconnections (strong warm anomalies during both El Nino and IOD), but GLOSEA5 does

provide some improved distinction of the impacts between the far-east and central Australia. In

contrast, for Tmin, GLOSEA5 shows a serious error, with colder than normal Tmin being

depicted across most of Australia during El Nino and IOD, when in reality Tmin should be

above normal across the southern 2/3 of the country. This is well captured in P24. The lack of

land surface initialization in the GLOSEA5 hindcast set may contribute to this error, but

further investigation is required. The sense of the error (Tmin too low during dry condition due

to El Nino and IOD) may indicate too much night time cooling (ie due to a dry land bias)

compared to subsidence warming that would act to keep Tmin higher than normal during dry

El Nino/IOD conditions.


30

Fig. 27 Anomaly correlation for the Nino34 index for forecasts starting in May.

Fig. 28 Regression onto Nino34 index of observed rainfall (top row) and ensemble mean rainfall from GLSOEA5 (middle) and P24 (bottom) forecast for months 2-4 for SON. Units are mm/day.


31

Fig. 29 Regression onto Nino34 and IOD indices of observed rainfall (top), GLOSEA5 (middle) and P24 (bottom) for months 2-4 for JJA and SON. Units are mm/day.

Fig. 30 As in Fig. 29 except for Tmax and Tmin in SON. Units are C.


32

b. SAM

Skill for predicting seasonal variations of the SAM are assessed using the Gong and Wang

(1999) SAM index, which is based on the difference of zonal mean surface pressure between

40S and 65S. We concentrate on the spring season when seasonal skill for the SAM is the

highest. The skill for predicting November- mean SAM from 1 Nov and from 1 Aug is shown

in top row of Fig. 31. Forecast skill is comparable from each system but with GLOSEA5 being

slightly better than P24 from 1 Nov and and P24 better than GLOSEA5 from 1 Aug. The

prediction of seasonal SAM for the SON season from 1st Aug is shown in the lower panel of

Fig. 31. As previously reported in Seviour et al. (2014), GLOSEA5 can provide skilful

predictions of seasonal SAM during SON with at least one month lead time. P24 is marginally

worse, which Seviour et al. (2014) attribute to the lack of resolution in the stratosphere in P24.

However, examination of the time series of observed and predicted SAM indices for

November and SON (Fig. 32) suggest that the improved depiction of the stratosphere in

GLOSEA5 may not be of primary importance for improved prediction of the SAM. For

November SAM predictions from 1st Nov (top of Fig. 32), the predictions from P24 track very

closely the predictions from GLOSEA5. In fact, the correlation of the P24 SAM predictions

with GLOSEA5 is 0.87, which is much higher than either is with reality. This is also true for

the SON prediction from 1st August (correlation P24/GLOSEA5= 0.74). This suggests

predictability is similar in both systems, but can’t be coming from the stratosphere as it is

absent in P24.

Importantly, both systems over-predict low SAM in November 2002, which followed the

strong stratospheric warming and extreme negative SAM in September-October 2002. Seviour

et al. (2014) speculate that improved initialization of the stratosphere should provide

predictability of SAM in November, but without a resolved stratosphere P24 equally over-

predicts the SAM in November 2002. The observed SAM value in October 2002 was strongly

negative, and so both systems may have over-represented persistence in their November 2002

forecasts. We also note that P24 provides a better prediction of the low SAM seasonal value in

SON 2002 after the stratospheric warming in September 2002 than does GLOSEA5, again

suggesting that the resolved stratosphere is probably not the source of predictability.


33

Fig. 31 Anomaly correlation for predicting SAM index for November using forecasts initialized on 1st November and 1st August (top). Persistence forecast is shown in blue. Anomaly correlation for months 2-4 (SON) for forecasts initialized on 1st August (bottom).


34

Fig. 32 Time series of November SAM index (top) and SON SAM index (bottom) from observations and forecasts.

The strong correlation of the ensemble-mean predictions from GLOSEA5 and P24 at short

lead time suggest some other source of predictability is acting in the forecast models (perhaps

spuriously) but it is not due to a resolved stratosphere. No doubt some of the strong

correlation of the ensemble mean predictions from the two systems reflects the elimination of

unpredictable noise by forming ensemble means. Research is ongoing to explore whether

individual forecast members are no better correlated from each system than with reality, which

then could explain the strong correlation of the two ensemble mean forecasts with each other.

The SAM decorrelates with about a 10 day timescale so it is of interest to investigate the daily

predictions of the SAM in the two systems. The GLOSEA5 hindcast set is not optimal for

assessing daily predictions because the start dates are 7-8 days apart and there are only 8

(maximum) ensemble members available for each start date but without any perturbations

(recall GLOSEA5 relies on stochastic physics to provide spread from an individual start time).

Therefore, we assess three sets of ensembles from GLOSEA5 that contain i) 8 members from

the 9th of the month, ii) 8 members from the 9th and 8 members from the 1st, and iii) 8 members


35

from the 9th, 8 members from the 1st and 8 members from the 25th. We use the 10 member

ensemble from the 11th of each month from P24a. Use of less ensemble members for P24 at

longer lead times as compared to GLOSEA5 means that the comparison to GLOSEA5 is not

necessarily fair, but for most of our purpose here, this should not be an issue.

Correlation of the SAM index based on all available start times is shown in Fig. 33. We also

include comparison to the 10 member lagged ensemble from the ACCESS1.3 version of

POAMA (Zhao et al. 2014), which is based on a lag spanning 2.5 days (ie 10 members that are

each 6 hours apart). For GLOSEA5, the effect of using a large-span lagged ensemble is

clearly seen, with forecast skill dropping in going from 8 members from the 9th, 8 members

from the 1st and the 9th, and 8 members from the 25th, 1st and 9th. GLOSEA5 is seen to

provide the most skilful predictions of the SAM for forecasts initialized on the 9th (ie

correlation of 0.5 is achieved at 12-13 days in GLOSEA5 but only at 10 days in P24).

However, for the lagged forecasts, GLOSEA5 is worse than P24 for the first 3-6 days.

More insight into the impact of using long lags in the GLOSEA5 ensemble is gained from

looking at rmse and spread (Fig. 33). For forecasts from a single start on the 9th (top panel in

Fig. 33), ensemble spread in GLOSEA5 is small and grows slowly, only achieving the spread

from P24 (which uses only 1 start time but with perturbations) after ~20 days. Furthermore,

GLOSEA5 is severely under-dispersive (spread is much smaller than rmse). For the lagged

ensemble combining starts on 9th and 1st the spread from GLOSEA is now comparable to P24

from a single start date, with spread roughly tracking rmse. For the lagged ensemble

combining starts on the 25th, 1st and 9th, GLOSEA5 is over-dispersive, and this is reflected in

reduced forecast skill. We also compare to the spread from the lagged ensemble from

ACCESS-POAMA, which is based on 10 members lagged by 6 hours each (so up to 2.5 days).

The ACCESS-POAMA spread is seen to be smaller than P24, as documented in Zhao et al.

(2013), but is seen to be greater than GLOSEA5 based on 1 start time but less than GLOSEA5

based on 2 start times that are 7-8 days apart.


36

Fig. 33 Correlation (left) and rmse/spread (right) for predicting daily SAM index using all start months for days 1-35. Top row is for forecasts initialized on 9th, middle row combines forecasts from 1st and 9th and bottom row combines 25th, 1st, and 9th.

From 9th: Correlation RMSE & spread

From 1st & 9th: Correlation RMSE & spread

From 25th, 1st & 9th: Correlation RMSE & spread


37

In summary, stochastic physics is not an effective mechanism to generate spread for the first

~3 weeks of forecast but appears to be adequate for seasonal forecasts. A lag of ~8 days as

afforded by the GLOSEA5 hindcasts does produce adequate spread (ie spread is comparable to

rmse) but at the expense of drop in forecast skill. The conclusion is that the ensemble strategy

in GLOSEA5 hindcast is not optimal for generating mutliweek forecasts but may be acceptable

for seasonal forecasting. And, the assessment of reliability and spread/rmse in GLOSEA5

based on the hindcasts may not give a true indication of the capability of the real time

GLOSEA5 system, which uses daily lags to form the ensemble, and so by forming ensemble

bases on a few days start times can produce sufficient spread without degrading forecast skill.

However, there are clear advantages of the perturbation approach used in P24, so this

technique should be explored in conjunction with stochastic physics in future versions of

POAMA based on updated model code from UKMO.

We also explored assessing SAM forecasts by projecting predicted sea level pressure (SLP)

onto the leading EOF of monthly mean SLP from observations, which depicts the SAM (e.g.,

Lim et al. 2013). The comparison of forecast scores using the Gong index and the observed

SLP EOF is shown in Fig. 34. Forecast scores for P24 and ACCESS-POAMA are similar

using both methods but the score drops dramatically in GLOSEA5 when the observed EOF is

used. This suggests that there is a significant bias in the structure of the SAM anomalies in

GLOSEA5, but further work is required to fully understand its cause.


38

Fig. 34 Comparison of SAM forecast skill using Gong SAM index (top) and by projecting onto observed EOF of SLP (bottom).

c. MJO

Forecast skill for the MJO is assessed by projecting forecast output of u200, u850, and OLR

onto the leading pair of observed EOFS as defined by Wheeler and Hendon (2004). A bivariate

correlation (e.g., Marshall et al. 2012) is computed for November and August starts (Fig. 35).

For November starts from the 9th (and 1st and 9th), GLOSEA5 and P24 are comparable (ie

correlation of 0.5 is achieved out to ~25 days) but GLOSEA5 based on starts from 25th, 1st, and

9th displays a drop in skill after 10 day. This again indicates that the lag in the GLOSEA5

hindcasts is too large for multiweek forecasts. We also see that GLOSEA5 from starts on the

9th and on the 1st and 9th is appreciably better than ACCESS1-POAMA in November

(ACCESS1 drops to 0.5 correlation at about 15 days compared to 25 days for GLOSEA5).

Gong Index

SLP EOF


39

Fig. 35 Bivariate correlation for Wheeler-Hendon MJO index for November starts (left) and August starts (right).

From 9th: Nov Start Aug Start

From 1st & 9th: Nov Start Aug Start

From 25th, 1st & 9th: Nov Start Aug Start


40

Fig. 36 As in Fig. 35 except for rmse and spread for November forecasts.

From 9th

From 1st & 9th

From 25th, 1st & 9th


41

This increase in skill of GLOSEA5 compared to ACCESS1 is a good outcome for

development of the UKMO models because the MJO was poorly simulated and predicted in

the earlier versions of the UKMO atmospheric models model ( upon which ACCESS1-

POAMA was based). We also see that GLOSEA5 is better than P24 for August starts (and

better than ACCESS-POAMA), which suggests the potential for improved prediction of Asian

summer monsoon variability.

d. Blocking

Skill for predicting atmospheric blocking is assessed using a simple index developed by the

Bureau of Meteorology for use in the Australian region. The index describes the degree of

splitting of the 500 hPa westerly airstream (Pook and Gibson 1999). Here we focus on

blocking at 140°E, which is regarded as a typical longitude for blocking in the Australian

region with widespread influences on seasonal (Risbey et al. 2009) and intra-seasonal

(Marshall et al. 2014) rainfall variability over the continent. Correlation score for prediction of

the blocking index is shown in Fig. 37 (left column). The effect of using a large-span lagged

ensemble is clearly seen over the first ~9 days of the forecast, with skill again dropping in

going from 8 members from the 9th, 8 members from the 1st and the 9th, and 8 members from

the 25th, 1st and 9th. GLOSEA5 provides the most skilful predictions of blocking out to about

15 days initialized on the 9th, however for the lagged forecasts using all three start dates

GLOSEA5 is no better than P24.

RMSE and spread (Fig. 37, right column) again reflect the impact of using long lags in the

GLOSEA ensemble, and bear similar results to those seen for the SAM analysis in Fig. 33. For

forecasts from a single start on the 9th, GLOSEA5 is severely under-dispersive (spread is much

smaller than rmse, and smaller than spread in P24 and ACCESS-POAMA), whereas for the

lagged ensemble combining starts on the 25th, 1st and 9th, GLOSEA5 is over-dispersive. For the

lagged ensemble combining starts on 9th and 1st, the spread from GLOSEA is comparable to

P24 and greater than ACCESS-POAMA, and spread is also roughly equal to rmse. As for the

SAM and MJO analyses, these results for blocking indicate that the lag in the GLOSEA5

hindcasts is too large for multiweek forecasts.


42

Fig. 37 As in Fig. 33 except for predicting the daily blocking index using all start months for days 1-30.

From 9th: Correlation RMSE & spread

From 1st & 9th: Correlation RMSE & spread

From 25th, 1st & 9th: Correlation RMSE & spread


43

The increase in skill out to about 15 days for GLOSEA5 compared to P24 and ACCESS-

POAMA is a good outcome for development of the UKMO models because weather and

climate models have known deficiencies in simulating the observed location and frequency of

blocking highs, in both hemispheres. This can be linked to the climatological biases of the

models, as shown for P24 (Marshall et al. 2014) and for a predecessor to GLOSEA5 (Scaife et

al. 2010), however it is unclear whether vertical, and in particular stratospheric, resolution is

also important for a model’s depiction of blocking (Scaife and Knight 2008, Woollings et al.

2010). Further work is required to fully understand the impact on blocking of mean state biases

and increased stratospheric resolution in GLOSEA5, compared to P24 and ACCESS-POAMA.

6. CONCLUSIONS AND RECOMMENDATIONS FOR POAMA DEVELOPMENT

Based on comparison of available hindcasts for the period 1996-2009 from the high resolution

GLOSEA5 system, we have come to a number of conclusions regarding the expected benefit

of a GLOSEA5-like system over the current P24 system. We have also uncovered a few

shortcomings of the GLOSEA5 system that could benefit from further development of the

model and ensemble systems. Taken together, these form the basis for some recommendations

on the development of future versions of POAMA.

a. Expected benefits of the high resolution GLOSEA5

1. Improved resolution of Australian climate and reduced drift

The increased horizontal resolution of GLOSEA5 shows a clear advantage over the existing

POAMA2.4 system for resolving Australian climate, including depiction of Tasmania, coastal

contrasts, and impacts of the Great Dividing Range. Benefits of high ocean model resolution

are being assessed in a separate study. Adoption of a high resolution POAMA system would

thus be expected to result in provision of improved seasonal forecast products on spatial and

temporal scales that are demanded by POAMA user, which include stream flow forecasts and

crop models that are driven by downscaled precipitation and temperature forecasts.

GLOSEA5 also exhibits reduced dry bias over the Australian continent, meaning that the

model will more realistically simulate impacts of climate variability on regional rainfall,

especially the north-south contrast during the summer monsoon. GLOSEA5 has appreciably

less drift than P24 so that predicted climate variability at longer lead time will be more

faithfully represented.


44

GLOSEA5 delivers appreciably better regional forecasts of precipitation and temperature over

Australia in the first month of the forecast, with more modest gains over P24 for months 2-4.

In this study, we are not able to conclusively attribute this benefit to increased resolution or

other model improvements afforded by GLOSEA5 but the outcome is more important than the

cause. Similarly, GLOSEA5 exhibits acceptable forecast reliability, even based on the

inadequate hindcast ensemble that was available for this study. Again, we do not know how

much contribution to good reliability comes from good forecast skill and how much comes

from the ensemble methods (combination of lagged and stochastic physics). However, we may

anticipate even bigger improvements in forecast performance with GLOSEA5 once the

POAMA assimilation/ensemble generation strategy is implemented (or if lagged ensembles

were formed with shorter lags than provided by the hindcast set).

2. Improved prediction of climate modes

GLOSEA5 exhibits a substantial improvement over P24 in long lead prediction of ENSO/IOD

and ENSO-impacts on tropical rainfall, which is fundamental to improved long lead prediction

of Australian climate because ENSO and the IOD are the greatest source of predictable

Australian climate variability. The increased skill in predicting IOD and ENSO from late

autumn is one of the most outstanding advantages of the GLOSEA5 over P24. The

improvements in predicting and simulating ENSO and its impacts in GLOSEA5 was attributed

by Dawson et al (2013) to increased resolution of both the atmosphere and ocean models used

in GLOSEA5. However, the reason for the pronounced improvement over P24 in autumn

needs further investigation. Furthermore, the improved prediction of the IOD over P24 needs

investigation because GLOSEA5 over-estimates the IOD amplitude for forecasts in the crucial

autumn season when the IOD is most rapidly growing. GLOSEA5 also exhibits improved

prediction of the MJO during winter, which is another outstanding advantage over P24. The

improvements in representation and prediction of the MJO in GLOSEA5 compared to

GLOSEA4 (and compared to ACCESS1) was attributed to atmospheric model improvements

afforded by UM GA3 (MacLachlan et al. 2014), primarily due to improvements to convective

parameterization.

GLOSEA5 also provides for improved short lead prediction of the SAM and blocking (ie for

days 1-15), which could translate into improved week 2 and week 3 forecasts for Australian

rainfall and temperatures due to their strong influences on regional Australian climate. The

reason for improved short lead prediction of the SAM was speculated to be due to improved


45

atmospheric initial conditions afforded by GLOSEA5 as compared to P24. However,

GLOSEA5 only marginally beats P24 for seasonal prediction of the SAM, which we speculate

is not due to improved resolution of the stratosphere because GLOSEA5 forecast correlated

much more strongly with P24 forecasts (that don’t have a stratosphere) than either does with

reality, but further work is required to verify this speculation. The improved prediction of

blocking also needs investigation. For instance, does it stem from improved representation of

the climatology of blocking and if so, is this due to increased resolution?

b. Areas requiring development in GLOSEA5

Although GLOSEA5 exhibits many outstanding improvements over P24, we have uncovered a

few shortcomings of the GLOSEA5 system that could benefit from continued development.

1. Too short hindcast: Although not a model deficiency, the available hindcast set from the

GLOSEA5 system (1996-2009, four start months per year and lagged over 3 start days

with 4 (or 8) members each) is too short to fully assess predictions of extremes and the

full gamut of ENSO variability. It is also too short to calibrate follow-on predictions

(e.g., streamflow forecasts). The short hindcast set stems from the trade-off of going to

a higher resolution (more expensive) system that is updated frequently. But, the

possibility of sharing hindcasts across institutions could be considered in order to create

a longer hindcast set that would be more useful for end-users.

2. Cold tongue bias: GLOSEA5 exhibits a pronounced tropical Pacific cold tongue bias.

Although there is little overall drift in GLOSEA5, the cold tongue bias affects ENSO

teleconnections and so can be contributing to reduced forecast skill over Australia.

Mediating the cold tongue bias is a challenging problem but could be pursued jointly

between CAWCR researchers and the UKMO for inclusion in a future version of

POAMA based on UKMO/ACCESS coupled models.

3. ENSO amplitude too strong: ENSO amplitude, as measured by the variability of the

Nino3 index is substantially overestimated in GLOSEA5, perhaps due to the cold tongue

bias. Too strong ENSO variability will act to degrade Australian climate forecasts

because of the primary role that ENSO plays in Australian climate. Tackling the ENSO

amplitude problem is challenging and might also be best pursued jointly between

CAWCR and UKMO scientists.


46

4. ENSO teleconnection to Australian Tmin: GLOSEA5 simulates negative Tmin

anomalies over Australia during El Nino, which is a serious error (it should be warmer

over Australia during El Nino). Further work is required to understand what is

contributing to this error (eg lack of soil initialization or a dry bias leading to too much

longwave cooling) but needs to be mediated in order to deliver good forecasts of Tmin

over Australia during winter and spring.

5. Indian Ocean-Maritime Continent Rainfall Bias: GLOSEA5 has a distinctive wet bias in

the tropical western Indian Ocean and dry bias in the eastern Indian Ocean/Maritime

Continent, which can contribute to degrading forecast skill of the MJO and the IOD and

their remote impacts in Australia. This rainfall bias is recognized as a serious issue and a

PEG at the UKMO, led by CAWCR researchers, has formed to address it.

6. Non-optimal ensemble strategy: The lagged/stochastic physics approach to ensemble

generation for GLOSEA5 hindcasts, while apparently adequate for seasonal forecasts, is

inadequate for multiweek forecasts (ie too little spread is generated, or sufficient spread

is generated at the loss of forecast skill, thereby resulting in either over-emphatic

forecasts or degraded accuracy). Although the reliability of the real time GLOSEA5

system may be better than the hindcasts set up, we cannot assess this. Clearly adoption

of the POAMA assimilation/ensemble generation into the GLOSEA system would

alleviate some of this problem; however, other simpler methods of perturbing the initial

conditions could also be explored.

7. SAM bias: GLOSEA5 apparently has a biased depiction of the spatial structure of the

SAM, as evidenced by lower skill scores when observed EOF patterns are used to verify

SAM rather than a simple zonal mean index of pressure differences. The nature and

cause of the bias needs to be explored.

Most of the deficiencies in the GLOSEA5 system outlined above stem from deficiencies in the

atmospheric model, which is similar (but more updated) to ACCESS. Tackling these

deficiencies should be a priority for future development projects.

c. Recommendations for POAMA development

A clear outcome of this intercomparison is that the future version of POAMA should be based

on a high resolution coupled model that is comparable in performance to GLOSEA5.

GLOSEA5 sits apart from all other seasonal forecast systems that were operational in 2014,

being by far the highest resolution system. GLOSEA5 is appreciably higher resolution


47

(vertical and horizontal directions) in both atmosphere and ocean model components than the

current ACCESS1 coupled model.

GLOSEA5 also exhibits some significant improvements over models based on earlier versions

of the UKMO atmospheric model, such as ACCESS1.0 (e.g., used in the POAMA3

prototype), including improved depiction of the MJO, improved regional climate over

Australia, and better simulation and prediction of ENSO. Future development of POAMA

should take advantage of these recent developments at the UKMO that went into the

component models of GLOSEA5 (and its successor), including the increased capability

afforded by running both high resolution atmosphere and ocean components (Dawson et al.

2012). Importantly, the ACCESS-POAMA program should position itself so that

improvements to the component models, either developed at UKMO or locally, will get into

future versions of the seasonal forecast model in a more timely fashion.

The stochastic physics option used in GLOSEA5 appears to offer some benefit for seasonal

ensemble generation. This option has yet to be implemented in the ACCESS coupled model.

Future versions of POAMA should implement this option, but further work is also needed to

quantify the additional benefit over that achieved from the POAMA perturbation approach.

The possibility of an expanded collaboration with the UKMO that would be facilitated by

using the same updated coupled model could be explored, for instance, by sharing generation

of a larger hindcast set (members and earlier start dates). A larger hindcasts set is critical to

allow for proper assessment of the potential benefit of seasonal forecasts for agricultural and

other applications. A larger hindcast set would also allow better sampling of predictive

capability of the “flavours” of ENSO and their regional impacts, as well as better sampling of

predictive skill of extreme events, such as heat waves and floods.

Finally, the greatest forecast benefit of GLOSEA5 over P24 occurred in the first month (and

even first 2 weeks) of the forecast. To fully capitalize on this benefit, operational seasonal

climate prediction, with whatever forecast model we end up using, should be done with the

shortest lead possible. Forecast skill drops off monotonically with lead time, so minimizing

the lead time is a guaranteed method to increase forecast skill.


48

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