A version of the BAM's AMIP2 simulation over the Australian region by hjkuiw354

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									                                           BMRC RESEARCH REPORT NO. 96
                                                           FEBRUARY 2004




          A version of the BAM’s AMIP2 simulation over
                    the Australian region: contrasting its
                  performance with other AMIP2 models
                                                           Huqiang Zhang




BUREAU OF METEOROLOGY RESEARCH CENTRE | AUSTRALIA
CONTENTS


Abstract     .     .     .      .      .      .     .   .   .   1


1.    Introduction .     .      .      .      .     .   .   .   3

2.    Model description and validation data         .   .   .   5

3.    Surface climatology simulation          .     .   .   .   6

4.    Simulation of surface climate variability .   .   .   .   8

5.    Model predictability      .      .      .     .   .   .   10

6.    Discussion and conclusions       .      .     .   .   .   11

Acknowledgments          .      .      .      .     .   .   .   13

References         .     .      .      .      .     .   .   .   13

Figure captions    .     .      .      .      .     .   .   .   17

Figures      .     .     .      .      .      .     .   .   .   19
   A version of the BAM’s AMIP2 simulation over the
Australian region: contrasting its performance with other
                     AMIP2 models

                               Huqiang Zhang

      Bureau of Meteorology Research Centre, Melbourne, Australia


                                    Abstract


       In this report, results from a version of the BMRC Atmospheric Model
       (BAM)’s AMIP2 simulations over the Australian region are analysed and
       compared with the results from other AMIP2 models. We focus the analysis on
       assessing how well the regional climate is simulated with observed sea surface
       temperatures (SSTs). As the version of the model uses a simple land-surface
       representation with a bucket-type soil hydrology, the impacts of such a land-
       surface model on the model’s performance, including model predictability, are
       also investigated. The Bureau of Meteorology (BoM) observational rainfall,
       temperature and surface evapotranspiration datasets are used in the model
       validation, including calculation of Linear Error in Probability Space (LEPS)
       score in assessing the skill of the models in simulating surface climate
       variability during the 17-year AMIP2 period (1979-1995). Preliminary lag-
       correlation analysis is conducted and results show that this model, with simple
       bucket-type soil hydrology, performs in a very similar way compared with other
       models with a bucket-type soil hydrology scheme. It has a more rapid decay
       rate in the retention of soil moisture anomalies, which may lead to soil moisture
       conditions having a weaker influence on forecasting surface climate anomalies.
       Results from this study demonstrate that a number of areas need to be
       addressed in the process of advancing BMRC land-surface modelling and
       studies.
1. Introduction

Zhang et al. (2002) have reported an analysis of 16 Atmospheric General Circulation Model (AGCM)
simulations of the Australian climate from the Atmospheric Model Intercomparison Project Phase II (AMIP2).
It is hoped that comparing model simulations with observations and contrasting results among models can help
us to identify physical processes which need to be improved in the model to better simulate current climate and
predict its future. In the development of the current BMRC Atmospheric Model, one area drawing a lot of
attention is the representation of land-surface processes in the model. This report aims to explore the impact of
using a simple land-surface scheme with a bucket-type soil hydrology on this AGCM’s performance, by
contrasting the model simulation with others in the AMIP2 project. Indeed, the study of Sperber et al. (1999)
included the results from a version of BMRC AGCM in AMIP1 simulations with two different land-surface
schemes: a simple land-surface model with a bucket soil hydrology and a sophisticated surface scheme with
canopy representation and multi-layer soil model. Results tended to suggest that rainfall interannual variability
over the Sahel and Nordeste region was better simulated in the model with a complex surface scheme. Here, the
aim is to contrast the new version of BAM AMIP2 simulations with other models, with the focus on the
Australian region. This study benefits from the participation of the AMIP2 sub-diagnostic project 12, which is
dedicated to evaluating AGCM simulations of surface energy and water budgets and their components, and to
assessing the role of land-surface parameterisations in AGCM simulations. Phillips et al. (2002) outlined the
overall scientific plans of AMIP2 subproject 12.


        As pointed out in Zhang et al. (2002), there are sound scientific reasons prompting us to study the
impacts of the model land-surface representation on its simulation and predictability. Within the bucket-type
soil hydrology (Manabe 1969), runoff only occurs when volumetric soil moisture is above the field water
holding capacity and the surface evaporation directly affects soil moisture with no hydraulic diffusion process
of water movement within the soil. Schlosser et al. (1997) found that the bucket model’s soil moisture
variability was limited by its prescribed field capacity (bucket depth). Scott et al. (1997) found that in regions
with dense canopy coverage, land-surface models without canopy representation showed a relatively slowed
response of evapotranspiration to precipitation forcing due to the lack of a canopy interception component to
simulate the rapid process of canopy transpiration of intercepted water. In contrast to the simple bucket-type
soil model, multi-level soil hydrological models are developed to represent different time scales of the
feedbacks between land and atmosphere. Usually, such models have a top thin layer to represent a rapid

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response to atmospheric forcing and some deep layers to simulate the restoring process of soil moisture supply
from deep soil to the upper layer, even including root water uptake affected by root density and distribution,
canopy transpiration and in some schemes even horizontal runoff. Surface runoff can occur when the upper
layer is saturated even though the deep layer may still be unsaturated.


        In recent years, research has been conducted to assess whether different complexity in land-surface
parameterisations can affect atmospheric model simulations and predictability. Chen et al. (1997) demonstrated
that schemes with simple bucket-type soil hydrology (without stomatal constraint) behaved differently from
others in simulating surface energy partition against observations. Desborough (1999) developed a multi-mode
land-surface scheme with different complexity in surface energy balance but the same soil hydrology
component, which followed the bucket philosophy of Manabe (1969), to explore the impacts of surface energy
representation on model simulations. In CHASM’s soil hydrology component, its bucket depth can vary with
root-zone depth. Desborough et al. (2001) used the scheme to study such impacts on a version of BMRC
AGCM’s simulations. Zhang et al. (2001) applied the same scheme but with fixed bucket depth to study the
impacts on a regional model’s simulation over the Australian region. Recently, Pitman and McAvaney (2004)
further assessed the potential impacts of different complexity of land-surface energy balance representation on
the projection of climate change over the Australian region in a global warming scenario.


        Meanwhile, a number of studies investigated the potential impacts of land-surface modelling in model
variability and predictability. Koster and Suarez (1996) investigated the influence of soil moisture retention on
precipitation statistics. Scott et al. (1997) studied the timescales of land-surface evapotranspiration responses in
land-air feedback processes. Robock et al. (1998) evaluated soil moisture simulations from the AMIP1 project
and found interannual variations of soil moisture were poorly simulated by AMIP1 models, and models with
150 mm field capacity did not simulate the high soil moisture values in high latitudes. Koster and Suarez
(2001) reported on a study of soil moisture memory in climate models by constructing a soil moisture
autocorrelation equation with components representing the nonstationary effects of atmospheric forcing,
evaporation, runoff, and the correlation of atmospheric forcing with soil moisture condition.


        In the analysis of 16 AMIP2 model results from Zhang et al. (2002), a range of model differences have
been described and some model discrepancies could be linked to the complexity in the model’s land-surface
schemes. For instance, results tend to suggest that models using a small number of soil layers (1 or 2) in the
calculation of surface energy balance generate poorer simulations of Tmax and Tmin over the region than those


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with more soil layers. Results also showed that models with a bucket-type soil hydrology scheme behave
differently from the others in the auto and lag correlation analyses. As only two of the 16 models used in the
study of Zhang et al. (2002) employed the simple bucket soil model, analysing the version of BAM’s AMIP2
results could re-assess the findings from that study. It can also provide useful information in improving the
land-surface modelling in BAM.


        This report is structured similar to the report of Zhang et al. (2002). Section 2 briefly describes the
version of the BAM model and the validation datasets used in this analysis. Section 3 presents the skill of the
model in simulating surface climatologies and section 4 presents the model skill in simulating the variations of
surface climate over this period. The results from lag correlation analyses are presented in Section 5 to
demonstrate the impacts of this particular land-surface parameterisation on the model predictability. Finally,
discussions and preliminary conclusions from the current analysis are presented in Section 6.


2. Model description and validation data

The atmospheric model used in this study is a version of the Bureau of Meteorology Research Centre's
Atmospheric Model (BAM), which is still under intensive development and improvement as the unified
atmospheric model for operational weather, seasonal forecasting and climate applications. The version of the
BAM configuration is similar to the one described by Zhong et al. (2001). She reported a 10-year climatology
of the model AMIP1 simulation for the period of 1979-1988 and concentrated on the large-scale global features
and a comparison of model performance using different vertical resolutions. In this report, the results are from a
17-level model integration.


        The version of the model uses the boundary layer parameterization and vertical diffusion as described
by McAvaney and Hess (1996) and evaporation over the ocean in both is enhanced by employing a modified
exchange coefficient at low wind speeds (Miller et al. 1992). It uses a modified version of the Fels-
Schwarzkopf radiation scheme (Fels and Schwarzkopf 1975, 1981) for longwave radiation calculation and the
short-wave radiation scheme of Lacis and Hansen (1974). The penetrative convection is a version of the “mass
flux” scheme formulated by Tiedtke (1989). Shallow convection is parameterized following Tiedtke (1988).
The major difference between the version of BAM and precedent BMRC AGCM is that it uses a physically-
based prognostic stratiform cloud scheme (Rotstayn 1998) and cloud optical properties are also adapted from
Rotstayn (1998). Seventeen-year (1979-1995) AMIP2 simulations using this version of BAM have been
conducted and submitted to PCMDI (personal communication with B. McAvaney, BMRC, 2002).
                                                        5
        In the model, soil temperature is based on heat storage from three layers with a zero flux assumed in the
calculation of soil temperature in the bottom layer. A single-layer “bucket” model (Manabe 1969; Manabe and
Holloway 1975) is used to represent soil moisture storage. Although vegetation is not explicitly included in the
model, surface albedo and roughness length vary over land according to vegetation type. Snow fraction is
calculated in the model as a function of snow depth and local roughness length of the vegetation. The existence
of snow affects surface albedo, roughness length, and evaporation. Both precipitation and snowmelt contribute
to the calculation of soil moisture. The evapotranspiration efficiency beta is a function of the ratio of soil
moisture to the field capacity. Runoff occurs if this ratio exceeds unity.


        As used in Zhang et al. (2002), the Australian Bureau of Meteorology (BoM) observed rainfall and
temperature datasets are employed for validation of the model simulated surface climate. These latter data,
originally formed on 0.25o by 0.25o grids, have been transformed to the common T62 Gaussian grids as used in
Zhang et al. (2002). The BoM evapotranspiration climatology (Wang et al. 2001) is used in the evaluation of
surface evaporation simulations.


        Besides the calculation of standard measurements, such as root-mean-square-error (RMSE), biases and
spatial and temporal correlations between model results and observations, the Linear Error in Probability Space
(LEPS) skill score (Potts et al. 1996) is also used in this study. This score is related to the difference between
the position of the simulation and the observation in the cumulative probability distribution space of the
particular climate variable under consideration. This skill score has been used in the verification of the BoM
statistical seasonal forecasting system (e.g., Jones 1998; Drosdowsky and Chambers 2001) and in the
assessment of the BMRC experimental AGCM seasonal forecasts (Frederiksen et al. 2001).


3. Surface climatology simulation

Figure 1 shows the 17-year precipitation climatologies in four seasons (DJF, MAM, JJA, SON) from the model
simulations and compares them with the observed climatology averaged over the period 1950 to 1999. Firstly,
the seasonal variations of precipitation distribution are reasonably captured in the model with heavy rain in the
northern and eastern regions in DJF associated with the summer monsoon. In the southern part of the continent,
maximum rainfall occurs in the austral winter. The dry climate over central Australia is eminent. However,
compared with observations, the model underestimates rainfall climatology over a large part of the continent by
10 to 20 mm month-1. The only overestimation is seen over the eastern coast region in DJF.
                                                        6
        The reasonable simulation of the rainfall spatial distribution and its seasonal migration is demonstrated
in the spatial correlation of rainfall climatology in Fig. 2. In order to compare with the performance of the other
models, results of the sixteen model simulations from Zhang et al. (2002) are drawn in the background, together
with the averages and the poor-man ensembles as described in Zhang et al. (2002). Results tend to suggest that
the simple surface scheme does not severely detriment the model performance. In Zhang et al. (2002), it was
also hard to see any direct impact of the complexity of land-surface on the model rainfall climatology in this
region. This is also true from the calculation of the areally averaged root-mean-square-error (RMSE) in Fig. 3.
The 10 to 20 mm month-1 precipitation biases from this model simulation do not show the model as the outlier
(Fig. 3). In fact, the mean RMSE from the model is even lower than the averaged results from the 16 models.


        With the relatively reasonable performance of the model simulation of precipitation climatology, it is
interesting to know how surface evaporation is simulated in the model, considering that precipitation is one of
the most important factors in determining the surface energy partition in dry regions. In Fig. 4, seasonally
averaged surface evaporation is compared with the observed climatology from Wang et al. (2001). Surface
evaporation is low over a large part of the continent throughout the year. Surface evaporation exceeding 80
Wm-2 is only observed over the northern and eastern part of the continent in the summer monsoon rainy season.
Comparing the model simulations with observations suggests that the model underestimates surface evaporation
over almost the whole continent except some individual grid points in the coastal region, which may be related
to the land-sea masking in the model. Such underestimation of surface evaporation is consistent with the lower
precipitation climatology in the model (Fig. 1). Nevertheless, the areally averaged RMSE in Fig. 6 clearly
demonstrate that the model performs poorly compared with the majority of the 16 AMIP2 models from Zhang
et al. (2002). Spatial correlations of evaporation climatology between the model simulations and observations
(Fig. 5) also demonstrate the relatively poor simulations from the version of BAM used here. Recalling that the
model precipitation simulation is not the outlier compared with other models (even though it has consistently
negative biases in the seasonal rainfall climatology) measured by areally averaged RMSE and spatial correlation
calculations, leads us to believe the simple land-surface representation with a bucket-type hydrology in this
version of BAM also contributes to such results.


        The monthly averaged daily maximum surface air temperature (Tmax) climatology is analysed in Fig. 7.
Tmax exhibits significant shifts seasonally following the sun and is generally cooler in the south and warmer in
the north and central regions. The observed seasonal variation of Tmax is reasonably captured in the model. The


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satisfactory simulation of the Tmax seasonal variation is reflected by the high spatial correlations between
observed and simulated Tmax climatology in Fig. 8. However, the mean bias calculation shows that Tmax is
overestimated over a large number of the regions throughout the year. In particular, there are around 5oC
positive biases over large areas in the central and western regions in DJF and SON. The biases are relatively
low in the winter season. Contrasting the model results with other AMIP2 models from Zhang et al. (2002),
results from averaged RMSE (Fig. 9) indicate that the model has relatively poor skill compared with others, but
it is not identified as the outlier. The model results in the austral winter season are quite similar to most models.


        Results from monthly averaged daily minimum surface air temperature (Tmin) are presented in Fig. 10.
Tmin also show seasonal variations following the sun and the spatial distribution of observed Tmin climatology is
similar in the model simulation and observations. However, this model overestimates the Tmin climatology by
more than 5oC over almost the whole continent in DJF, MAM and SON. In JJA, its simulation still shows large
positive biases over the northern half of the continent. Such results are not satisfactory when comparing the
areally averaged RMSE with other models in Fig. 12. Clearly, this BAM version is one of the outliers
comparing the results from Zhang et al. (2002), even though the spatial distribution of Tmin climatology is
satisfactory (Fig. 11).


        It should be pointed out that the surface temperature simulations in the model are affected by two
processes: one is the amount of radiative forcing reaching the ground and the other is the partition of the surface
radiative energy into latent heat and sensible heat as well as ground heat flux. The former is closely linked to
the cloudiness in the model simulations and the latter is more closely linked to the land-surface scheme.
Meanwhile, Tmax and Tmin results are also determined by diurnal variations in the model. Therefore, the results
in the model Tmax and Tmin simulations are not solely related to the representation of surface energy balance in
the simple land-surface model used in the version of BAM. In a separate study, the model simulations of cloud
coverage and its diurnal cycle are being studied against satellite observations (personal communication with Z.
Wu, BMRC, 2002). The linkage between the model simulation of Tmax and Tmin with cloud coverage and its
diurnal cycle will be explored in detail in a separate study.


4. Simulation of surface climate variability

Section 3 concentrated on the evaluation of the model surface climatology over the Australian region. Similar to
Zhang et al. (2002), in this section we assess the model skill in simulating monthly precipitation and Tmax and
Tmin anomalies for the 17-year AMIP period. The model simulated anomalies refer to the 17-year climatology
                                                         8
from the model simulation. As in Zhang et al. (2002), LEPS skill scores are calculated and the cumulative
probability distribution of such anomalies is derived from the model 17-year simulation, together with the
distribution derived from the observations. The model skill is compared with the averaged LEPS skill scores
and the poor-man ensemble results from the 16 AMIP2 models analysed in Zhang et al. (2002).


        As shown in Fig. 13, the model exhibits rather limited skill in simulating rainfall anomalies over this
region. This is also commonly seen in the results from other AMIP2 models (Zhang et al. 2002), which is
reflected in the averaged LEPS skill scores from the 16 AMIP2 models in Zhang et al. (2002). Although there
is no coherent picture of where the model has skill, it tends to show some skill in the eastern part of the region.
In addition, it has some relatively high skill in MAM seasons, which is also seen in the results from poor-man
ensembles from Zhang et al. (2002). However, relatively high skill from the statistical seasonal forecasting
system developed in BMRC (Drosdowsky and Chambers 2001) occurs in austral winter and spring seasons,
which is also identified in the analysis of Zhang et al. (2002).


        The model does not show satisfactory skill in simulating the variation of Tmax (Fig. 14), with only
moderate skill seen in some parts of the eastern region. In the summer season (DJF), the model simulates the
Tmax variation poorly in the southeast part of the continent and only displays modest skill in the western corner.
Only in the austral spring season does the model have some skill in the southern part of the continent.
Contrasting the model performance with other AMIP2 models in Zhang et al. (2002) tends to suggest that the
model belongs to the group showing poor skill in the 17-year simulations. Results from the poor-man
ensembles from the 16 models in Zhang et al. (2002) exhibit relatively high skill in the austral autumn and
winter seasons (MAM and JJA), in agreement with the skill from the BoM statistical model (Jones 1998).


        The most skilful simulations of Tmin variation occur in MAM and SON (Fig. 15). In DJF, the model
only shows some skill in the northeast region. Its skill increases in MAM with a large area of positive skill in
the central and northern region. The model does not have large skill in JJA, while the poor-man ensembles from
Zhang et al. (2002) exhibit reasonable skill over most parts of the continent in the austral winter season. In
agreement with the poor-man ensembles in SON, the model has skilful simulations of Tmin variation in the
northern part of the continent. Averaged LEPS skill scores shown in Fig. 15 also indicate that most AMIP2
models do not show coherent skill in the simulation of Tmin variation. However, comparing the LEPS skill score
in JJA and DJF with other models shown in Zhang et al. (2002) suggests that the BAM performance is in the
group of those showing lower skill scores.


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        Overall, the skill of the model simulation of surface climate variations in the 17-year AMIP2 period is,
by and large, poor in the Australian region. This seems to be the common feature seen in other AMIP2 results
in Zhang et al. (2002), with no single model identified as outperforming the rest. However, results tend to
suggest that the model’s Tmax and Tmin simulations are less skilful compared with most other models.


5. Model predictability

In recent years, a number of studies have investigated the potential impacts of land-surface processes on model
predictability (e.g., Koster and Suarez 1996; Scott et al. 1997; and Koster and Suarez 2001). Zhang et al.
(2002) did some preliminary analysis of the impacts of land-surface modelling on model predictability by lag-
correlation calculations of 16 AMIP2 models. They found that models with bucket-type soil hydrology behave
differently from the models using a multi–layer soil hydrological model. As noted by Koster and Suarez 2001,
soil moisture memory is also linked to the nonstationary effects from the atmospheric forcing in the host
AGCM. Therefore, the feature of soil moisture memory may also be model-dependent. Given that the version
of BAM used in this study also employs a bucket-type model, conducting similar analysis of the model 17-year
AMIP2 simulations not only helps us to gain further understanding of the behaviour of the model as well as the
impacts of this simple land-surface representation on the model performance, but also strengthens the analysis
of Zhang et al. (2002).


        As in Zhang et al. (2002), the seasonal cycle is removed before calculating lag-correlation between two
variables. Lag correlations up to twelve months are calculated in this study. We firstly analysed the soil
moisture auto-correlation to study the time scale of the retention of soil moisture anomalies in the model. Figure
16 shows the soil moisture auto correlation averaged over the Australian continent. The BAM performance is
presented as the heavy solid line and two AMIP2 models from Zhang et al. (2002) employing bucket soil
schemes are presented as dashed lines. The rest of the AMIP2 models from Zhang et al. (2002) are shown in
light solid lines. A clear feature is that the BAM version performs remarkably similarly to the two AMIP2
models with a bucket soil hydrological scheme. The time scale of the retention of soil moisture anomalies is
much shorter than other models. This means there are rapid responses between land-surface and the atmosphere
in the models with a simple bucket hydrology. If soil acts as a low-pass filter to smooth the high frequency
fluctuation of surface meteorological forcing (e.g., Delworth and Manabe 1989), then results shown in Fig. 15
demonstrate that this process is weak in these models. This will inevitably affect the predictability in the model.


                                                        10
        Figure 17 serves as an example of how the soil moisture processes in the model can affect the model
predictability. It shows the auto correlation of surface air temperature from the BAM simulation and that from
the 16 AMIP2 models in Zhang et al. (2002). The auto correlation coefficient in the first three months drops
quicker in BAM and the two models with bucket soil hydrology than in the rest, implying that surface air
temperature anomalies are less predictable in these three models with the simple bucket scheme.


        To gain further understanding of the behaviour of the BAM model, Fig. 17 presents the lag correlations
between precipitation forcing and soil moisture variations over the Australian region. Again, this version of
BAM shows very similar features as seen from the other two bucket schemes, but behaves differently from the
rest. At zero-lag, soil moisture responds strongly to the precipitation forcing in the three models with bucket
hydrology. However, after a month, the influence of previous precipitation forcing on the simulated soil
moisture condition is much weaker in these three models. In contrast, the rest of the AMIP2 models tend to
show the impacts of rainfall anomalies on the soil moisture condition at longer than two-month scales. This,
again, is attributed to the rapid interactions between land-surface and the atmosphere in the simple bucket soil
scheme, as well as the lack of vegetation representation in the surface scheme.


6. Discussion and conclusions

In this report, a version of the BMRC Atmospheric Model’s (BAM) performance over the Australian region is
evaluated against observational data. To assess the impacts of the use of a simple land-surface scheme with a
bucket-type (Manabe 1969) soil hydrology on the model surface climate simulations, the model results are
contrasted with the analysis of the other 16 AMIP2 models presented in Zhang et al. (2002). Note that model
sensitivity experiments were conducted in a previous version of the BMRC AGCM in the AMIP1 simulations
by using a simple and a complex land-surface scheme as reported in Sperber et al. (1999), but how the model
performance over the Australian region was affected was unclear. As part of the process of developing the
BAM system, such impacts are being studied and this report serves as a preliminary analysis of the AMIP2
experiments from a version of BAM. The study is also dedicated to study the impacts of the bucket soil
hydrology in the simple land-surface scheme on the model predictability.


        The Australian Bureau of Meteorology (BoM) observational rainfall, temperature and surface
evapotranspiration datasets have been used in this study. The skill of the model simulations is assessed by the
calculations of climatological biases, RMSE and spatial correlations. The Linear Error in Probability Space


                                                      11
(LEPS) score is calculated to show the skill of the model in simulating surface climate anomalies for the 17-
year AMIP2 period (1979 to 1995).


        Overall, results show that the fundamental features of the surface climatology in the Australian region,
including seasonal variations and spatial distributions of rainfall, evaporation and Tmax and Tmin, are reasonably
captured in the model. However, the model systematically underestimates rainfall climatology across the
continent. Comparing the areally averaged RMSE of precipitation climatology with the other 16 AMIP2
models, suggests that the model precipitation climatology is not one of the outliers. Nevertheless, the model
does perform as either one of the outliers or as belonging to the group with poorer skill in simulating
evaporation, Tmax and Tmin climatology. This is likely to be linked to the simple surface scheme with a bucket-
type soil hydrology in the model. To what extent the results are linked to cloud coverage, as well as diurnal
variations in the model, will be further explored in a separate study.


        Lag-correlation analysis demonstrated that soil moisture variations in the version of BAM behave very
similarly to the other two AMIP2 models with a bucket soil scheme, but differently from the rest in Zhang et al.
(2002). This model with a simple bucket-type soil scheme has rapid decay rates in soil moisture anomalies,
which contribute to weaker auto correlations of surface climate (e.g., surface air temperature) anomalies. The
influence of rainfall anomalies on the model’s soil moisture variation is also weak compared with others using
more complex soil hydrological models. This analysis reinforces the findings from Zhang et al. (2002) that
land-surface modelling has the potential to affect AGCM predictability on seasonal and even longer time scales
(Zhang and Frederiksen 2003).


        The current study is part of the effort to firstly understand the land-surface processes in the BAM
system, identify the areas which need further improvement and then conduct more research towards a better
representation of land-surface processes. This study clearly demonstrates the need to improve soil hydrological
processes in the model, as well as the surface energy and water partition. With the potential of implementing a
relatively complex land-surface scheme in the future BAM system, model performance with a simple and a
complex surface scheme will be compared and analysed in the future.




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Acknowledgments:

The author greatly appreciates the efforts in developing the unified BMRC Atmospheric Model by the BMRC
Model Development Group and other individuals involved. Dr. B. McAvaney conducted the version of BAM
AMIP2 experiments and J. Sisson assisted in processing the data. The author also benefits from ongoing
collaboration with scientists at ANSTO led by Prof. A. Henderson-Sellers. Dr. D. Jones and Mr. G. de Hoedt
from the National Climate Centre are thanked for providing Bureau of Meteorology observational data.
Comments from Drs B. McAvaney and M. Harvey during the internal review process are appreciated.




References:
Chen, T.H. and co-authors. 1997. Cabauw experimental results from the Project for Intercomparison of Land-
       surface Parameterization Schemes. J. Climate, 10, 1194-1215.

Delworth, T.L., and Manabe, S. 1988. The influence of potential evaporation on the variability of simulated
       soil wetness and climate. J. Climate, 1, 523-547.

Desborough, C.E. 1999. Surface energy balance complexity in GCM land surface models. Climate Dynamics,
       15, 389-403.

Desborough, C.E., Pitman, A.J. and McAvaney, B. 2001. Surface energy balance complexity in GCM land
       surface models, Part II: coupled simulations. Climate Dynamics, 17, 615-626.

Drosdowsky, W. and Chambers, L. 2001. Near-global sea surface temperature anomalies as predictors of
       Australian seasonal rainfall. J. Climate, 14, 1677-1687.

Fels, S.B. and Schwarkopf, M.D. 1975. The simplified exchange approximation: a new method for radiative
        transfer calculations. J. Atmos. Sci., 32,1475 - 1488.

Fels, S.B. and Schwarzkopf, M.D. 1981. An efficient, accurate algorithm for calculating CO2 band cooling
        rates. J. Geophys. Res., 86, 1205-1232.

Frederiksen, C.S., Zhang, H., Balgovind, R.C., Nicholls, N., Drosdowsky, W. and Chambers, L. 2001.
        Dynamical seasonal forecasts during the 1997/98 ENSO using persisted SST anomalies. J. Climate,
        14, 2675-2695.

Lacis, A.A. and Hansen, J.E. 1974. A parameterization for the absorption of solar radiation in the Earth's
        atmosphere. J. Atmos. Sci., 31, 118-133.

Jones, D.A. 1998. The prediction of Australian land surface temperatures using near global sea surface
        temperature patterns. BMRC Research Report, No. 70. Bureau of Meteorology, Melbourne, Australia,
        44pp.

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Koster, R.D. and Suarez, M.J. 1996. The influence of land surface moisture retention on precipitation statistics.
        J. Climate, 9, 2551-2567.

Koster, R.D. and Suarez, M.J. 2001. Soil moisture memory in climate models. J. Hydrometeo., 2, 558-570.

Louis, J.F. 1979. A parametric study of vertical eddy fluxes in the atmosphere. Boundary Layer Meteorol., 17,
         187-202.

Manabe, S. 1969. Climate and the ocean circulation I: the atmospheric circulation and the hydrology of the
      earth’s surface. Mon. Wea. Rev., 97, 739-774.

Manabe, S. and Holloway, J.L. 1975. The seasonal variation of the hydrologic cycle as simulated by a global
      model of the atmosphere. J. Geophys. Res., 80, 1617-1649.

McAvaney, B.J. and Hess, G.D. 1996. The revised surface fluxes parameterisation in the BMRC AGCM.
      BMRC Research Report, No. 56, Bureau of Meteorology Research Centre, Melbourne, Australia,
      27pp.

Miller, M.J., Beljaars, A.C.M. and Palmer, T.N. 1992. The sensitivity of the ECMWF model to the
        parameterization of evaporation from the tropical ocean. J. Climate., 5, 418-434.

Phillips, T.J. and co-authors. 2000. Validation of Land-Surface Processes in AMIP Models: A Pilot Study,
         PCMDI Report No. 63, PCMDI, Livermore, CA, USA, 22pp.

Pitman, A.J. and McAvaney, B.J. 2004. Impact of varying the complexity of the land surface energy balance on
        the sensitivity of the Australian climate to increasing carbon dioxide, Climate Research, 25, 191-203.

Potts, J.M., Folland, C.K., Jolliffe, I.T. and Sexton, D. 1996. Revised “LEPS” scores for assessing climate
         model simulations and long-range forecasts. J. Climate, 9, 34-53.

Robock, A., Schlosser, C.A., Vinnikov, K.Y, Speranskaya, N.A., Entin, J.K. and Qiu, S. 1998. Evaluation of
       the AMIP soil moisture simulations. Global and Planetary Change, 19, 181-208.

Rotstayn, L. 1998. A physically based scheme for the treatment of stratiform clouds and precipitation in large-
        scale models. II: Comparison of modeled and observed climatological fields. Q. J. R. Meteorol. Soc.,
        124, 389-415.

Scott, R.L., Entekhabi, D., Koster, R. and Suarez, M. 1997. Timescales of land surface evapotranspiration
        response. J. Climate, 10, 559-566.

Schlosser, C.A., Robock, A., Vinnikov, K.Y., Speranskaya, N.A. and Xue, Y. 1997. 18-year land-surface
        hydrology model simulations for a midlatitude grassland catchment in Valdai, Russia. Mon. Wea. Rev.,
        125, 3279–3296.

Sperber, K. R. and Participating AMIP Modelling Groups. 1999. Are revised models better models? A skill
        score assessment of regional interannual variability. Geophys. Res. Letters, 26, 1267-1270.

Tiedtke, M. 1988. Parameterization of cumulus convection in large-scale models, in Physically Based
        Modelling and Simulation of Climate and Climate Change, edited by M.E. Schlesinger, I, Kluwer
                                                       14
        Academic, Norwell, Mass., 375-431.

Tiedtke, M. 1989. A comprehensive mass flux scheme for cumulus parameterization in large-scale models.
        Mon. Wea. Rev., 117, 1779-1800.

Wang, Q.J., McConachy, F.L.N., Chiew, F.H.S., James, R., de Hoedt, G.C. and Wright, W.J. 2001. Climatic
        Atlas of Australia: Maps of Evapotranspiration.
Available on http://www.bom.gov.au/climate/averages/climatology/evapotrans/text/et-txt.shtml.

Zhang, H., Henderson-Sellers, A., Pitman, A.J., McGregor, J.L., Desborough, C.E. and Katzfey, J. 2001.
       Limited-area model sensitivity to the complexity of representation of the land-surface energy balance.
       J. Climate, 14, 3965-3986.

Zhang, H. and coauthors. 2002. Land-surface modelling and climate simulations: results over the Australian
       region from sixteen AMIP2 models, BMRC Research Report. No. 89, 18pp.

Zhang, H. and Frederiksen, C.S. 2003. Local and non-local impacts of soil moisture initialisation on AGCM
       seasonal forecasts: A model sensitivity study. J. Climate, 16, 2117-2137.

Zhong A., Colman, R., Smith, N., Naughton, M., Rikus, L., Puri, K. and Tseitkin, F. 2001. Ten-year AMIP1
       climatologies from a version of the BMRC Atmospheric Model, BMRC Research Report. No. 83,
       33pp.




                                                     15
Figure captions:


Fig. 1: Observed surface climatology over the Australian region from the model’s AMIP2 (1979-
1995) simulation and the BoM’s observations (1950-1999). (a), (d), (g) and (h) are the simulated
precipitation climatology (mm month-1) in DJF, MAM, JJA and SON, respectively; (b), (e), (h) and
(k) are the observed precipitation climatology (mm month-1) in DJF, MAM, JJA and SON; (c), (f), (i)
and (l) are the biases from the model climatology against the observed (mm month-1) in DJF, MAM,
JJA and SON, respectively.


Fig. 2: Spatial correlations of precipitation climatology simulated by BAM and other 16 AMIP2
models from Zhang et al. (2002) against BoM observations over the Australian region. The solid line
with open circles represents the BAM simulation. The heavy dashed line represents averages of all 16
models. The heavy solid line represents results from poor-man ensembles of the 16 models.


Fig. 3: Root-mean-square-error (RMSE) of precipitation climatology simulated by the BAM and
other 16 AMIP2 models from Zhang et al. (2002) against the BoM observations (mm month-1) over
the Australian region. The solid line with open circles represents the BAM simulation. The heavy
dashed line represents the averaged RMSE of all the 16 models. The heavy solid line represents the
RMSE of results from by the poor-man ensembles of the 16 models.


Fig. 4: As Fig. 1 but for monthly evaporation (mm month-1).


Fig. 5: As Fig. 2 but for monthly evaporation.


Fig. 6: As Fig. 3 but for monthly evaporation.


Fig. 7: As Fig. 1 but for monthly mean daily maximum surface temperature (oC).


Fig. 8: As Fig. 2 but for monthly mean daily maximum surface temperature.

                                                 17
Fig. 9: As Fig. 3 but for monthly mean daily maximum surface temperature (oC).


Fig. 10: As Fig. 1 but for monthly mean daily minimum surface temperature (oC).


Fig. 11: As Fig. 2 but for monthly mean daily minimum surface temperature (oC).


Fig. 12: As Fig. 3 but for monthly mean daily minimum surface temperature (oC).


Fig. 13: LEPS score of the BAM simulation of precipitation anomalies in the 17-yr (1979-1995)
period. The BoM observational dataset (1950 to 1999) is used in the calculation. LEPS score in the
diagram is divided by 10 with a range of –10 to 10. (a), (d), (g) and (j) are the BAM’s LEPS scores in
DJF, MAM, JJA, and SON; (b), (e), (h) and (k) are the averaged LEPS scores from the 16 AMIP2
models in Zhang et al. (2002) in DJF, MAM, JJA, and SON; (c), (f), (i) and (l) are the LEPS scores
from the poor-man ensemble of the 16 AMIP2 models in Zhang et al. (2002).


Fig. 14: As Fig. 13 but for Tmax.


Fig. 15: As Fig. 13 but for Tmin.


Fig. 16: Area-averaged auto correlations of soil moisture anomalies over the Australian region. The
heavy solid line represents the BAM simulation. The two dashed lines represent results from two
AMIP2 models with bucket-type soil scheme and the rest of the AMIP2 models in Zhang et al. (2002)
are represented by light solid lines.


Fig. 17: As Fig. 16 but for auto correlations of surface air temperature.


Fig. 18: As Fig. 16 but for the lag correlations between precipitation and soil moisture, with soil
moisture lagging precipitation.


                                                 18
Figure 1
   19
                                             Precipitation Climatology
                                               (correlation with BoM data)
                        1
                       0.9
                       0.8
                       0.7
                       0.6
                       0.5
                       0.4




                COR


20
                       0.3




     Figure 2
                       0.2
                                                                                     amip2 models
                       0.1                                                           ensemble
                                                                                     average
                        0                                                            BAM
                      −0.1
                      −0.2
                             J   F   M   A      M      J   J          A      S   O   N     D
                                                       MONTH
                                                     Precipitation Climatology
                                                         (rmse with BoM data)


                               140                                               amip2 models
                                                                                 ensemble
                                                                                 average
                               120                                               BAM




           −1
                               100

                               80




  21
                               60




Figure 3
            RMSE (mm month )
                               40

                               20

                                 0
                                     J   F   M   A      M     J   J         A    S     O        N   D
                                                              MONTH
Figure 4
  22
                                            Evaporation Climatology
                                             (correlation with BoM data)
                       1
                                                                           amip2 models
                      0.9                                                  ensemble
                                                                           average
                      0.8                                                  BAM

                      0.7

                      0.6
                      0.5




                COR



23
     Figure 5
                      0.4
                      0.3

                      0.2
                      0.1

                       0
                            J   F   M   A      M     J   J          A      S     O        N   D
                                                     MONTH
                                              Evaporation Climatology
                                                 (rmse with BoM data)
                         80
                                                                        amip2 models
                                                                        ensemble
                                                                        average
                                                                        BAM
                         60




           −2
                         40




  24
Figure 6
            RMSE (Wm )
                         20




                          0
                              J   F   M   A      M    J   J         A   S     O        N   D
                                                      MONTH
Figure 7
  25
                                             Tmax Climatology
                                            (correlation with BoM data)
                       1

                      0.9
                      0.8

                      0.7

                      0.6
                      0.5




                COR


26
                      0.4                                                 amip2 models




     Figure 8
                                                                          ensemble
                      0.3                                                 average
                                                                          BAM
                      0.2
                      0.1

                       0
                            J   F   M   A    M      J   J          A      S     O        N   D
                                                    MONTH
                                                Tmax Climatology
                                                (rmse with BoM data)


                           14                                          amip2 models
                                                                       ensemble
                                                                       average
                           12                                          BAM


                           10

                            8




                RMSE (K)



27
                            6




     Figure 9
                            4

                            2

                            0
                                J   F   M   A   M    J   J         A   S     O        N   D
                                                     MONTH
Figure 10
   28
                                               Tmin Climatology
                                             (correlation with BoM data)
                        1

                       0.9
                       0.8

                       0.7

                       0.6
                       0.5




                 COR


29
                       0.4




     Figure 11
                       0.3                                   amip2 models
                                                             ensemble
                       0.2                                   average
                                                             BAM
                       0.1

                        0
                             J   F   M   A    M      J   J          A       S   O   N   D
                                                     MONTH
                                                 Tmin Climatology
                                                 (rmse with BoM data)


                            14                                          amip2 models
                                                                        ensemble
                                                                        average
                            12                                          BAM


                            10

                             8




30
                 RMSE (K)
                             6




     Figure 12
                             4

                             2

                             0
                                 J   F   M   A   M    J   J         A   S     O        N   D
                                                      MONTH
Figure 13
 31
Figure 14
  32
Figure 15
   33
                         Soil Moisture Autocorrelation
                          (averaged over Australian region)

       0.9

       0.7

       0.5
COR




       0.3

       0.1

      −0.1                            AMIP2 models
                                      bucket scheme
      −0.3                            bucket scheme
                                      BAM
      −0.5
             0   1   2    3    4     5 6 7 8             9    10 11 12
                                   Lag (MONTH)



                                   Figure 16




                                      34
                     Surface Temperature Autocorrelation
                          (averaged over Australian region)

       0.9

       0.7

       0.5
COR




       0.3

       0.1

      −0.1                            AMIP2 models
                                      bucket scheme
      −0.3                            bucket scheme
                                      BAM
      −0.5
             0   1    2   3    4     5 6 7 8             9    10 11 12
                                   Lag (MONTH)



                                   Figure 17




                                          35
                 Lag correlation between precip and soil moisture
                           (averaged over Australian region)

       0.9

       0.7

       0.5
COR




       0.3

       0.1

      −0.1                             AMIP2 models
                                       bucket scheme
      −0.3                             bucket scheme
                                       BAM
      −0.5
             0     1   2    3   4     5 6 7 8             9    10 11 12
                                    Lag (MONTH)



                                    Figure 18




                                      36
BMRC Research Reports


Holland, G.J., Berzins, I.A. and Merrill, R.T. 1985. The cyclone game: simulation of a tropical cyclone warning centre.
   BMRC Research Report No. 1, Bur. Met. Australia.
McBride, J.L. and Holland, G.J. 1986. The BMRC Australian monsoon experiment: objectives and scientific basis.
   BMRC Research Report No. 2, Bur. Met. Australia.
Spillane, K.T. and Lourensz, R.S. 1986. The hazard of horizontal windshear to aircraft operations at Sydney Airport.
   BMRC Research Report No. 3, Bur. Met. Australia.
Spillane, K.T. 1987. Convection in an inversion limited boundary layer. BMRC Research Report No. 4, Bur. Met.
   Australia.
Glowacki, T.J. and Seaman, R. 1987. A general purpose package for univariate two-dimensional data checking and
   analysis. BMRC Research Report No. 5, Bur. Met. Australia.
Williams, M. 1987. Relations between the Southern Oscillation and the troposphere over Australia. BMRC Research
   Report No. 6, Bur. Met. Australia.
Karoly, D.J., Kelly, G.A.M. and Le Marshall, J.F. 1987. The Australian Southern Hemisphere climatology data tape.
   BMRC Research Report No. 7, Bur. Met. Australia.
Keenan, T.D. and Martin, S.C. 1987. AMEX Radar Atlas. Volume 1 AMEX Phase 1. BMRC Research Report No. 8, Bur.
   Met. Australia.
Keenan, T.D. and Martin, S.C. 1987. AMEX Radar Atlas. Volume II AMEX Phase II. BMRC Research Report No. 9,
   Bur. Met. Australia.
Lajoie, F.A. 1988. Circulation asymmetries around a tropical cyclone and its future direction of motion. BMRC Research
   Report No. 10, Bur. Met., Australia.
Pike, D.J., Leslie, L.M., Mills, G.A., Glowacki, T., Hubbert, G. and McIntosh, P. 1988. Report on performance of
   regional forecast models January-June 1987. BMRC Research Report No. 11, Bur. Met. Australia.
Hart, T.L., Bourke, W.P., McAvaney, B.J., Forgan, B.W. and McGregor, J.L. 1988. Atmospheric general circulation
   simulations with the BMRC global spectral model: the impact of revised physical parametrisations. BMRC Research
   Report No. 12, Bur. Met. Australia.
Drosdowsky, W. 1988. Lag relations between the Southern Oscillation and the troposphere over Australia. BMRC
   Research Report No. 13, Bur. Met. Australia.
Pike, D.J., Leslie, L.M., Mills, G.A., Glowacki, T., Hubbert, G. and McIntosh, P. 1989. Report on performance of
   regional forecast models July-December 1987. BMRC Research Report No.14, Bur. Met. Australia.
Bourke, W. 1989. Working Group on Numerical Experimentation survey of model boundary condition data sets. BMRC
   Research Report No. 15, Bur. Met. Australia.
Hess, G.D. and Guymer, A.E. 1989. Verification of a Markov technique for the short-term prediction of rainfall at eight
   Australian stations. BMRC Research Report No. 16, Bur. Met. Australia.
Pike, D.J., Leslie, L.M., Mills, G.A., Glowacki, T., Hubbert, G. and McIntosh, P. 1989. Report on performance of
   regional forecast models January-June 1988. BMRC Research Report No.17, Bur. Met. Australia.
Blomley, J.E., Smith, N.R. and Meyers, G. 1989. An oceanic subsurface thermal analysis scheme. BMRC Research
   Report No. 18, Bur. Met. Australia.
Naughton, M.J. and Balgovind, R.C. 1990. BMRC Implementation of the NCAR CCM Modular Processor. BMRC
   Research Report No. 19, Bur. Met. Australia.
Hess, G.D. and Spillane, K.T. 1990. A survey of dust devils in Australia. BMRC Research Report No. 20, Bur. Met.
   Australia.
Jasper, J.D. (editor). 1990. 'The role of sea surface temperatures in numerical modelling of the atmosphere': papers
   presented at the first BMRC modelling workshop, July 1989. BMRC Research Report No. 21, Bur. Met. Australia.
Pike, D.J., Leslie, L.M., Mills, G.A., Glowacki, T., Hubbert, G. and McIntosh, P. 1990. Report on performance of
   regional forecast models July-December 1988. BMRC Research Report No.22, Bur. Met. Australia.
Pike, D.J., Leslie, L.M., Mills, G.A., Glowacki, T., Hubbert, G. and McIntosh, P. 1991. Report on performance of
   regional forecast models January-December 1989. BMRC Research Report No. 23, Bur. Met. Australia.
Colman, R.A. and McAvaney, B.J. 1991. Experiments using the BMRC general circulation model with a heat balance
   ocean. BMRC Research Report No. 24, Bur. Met. Australia.
Rikus, L. 1991. The role of clouds in global climate modelling. BMRC Research Report No. 25, Bur. Met. Australia.
Davidson, N.E., Puri, K., Bowen, R., Fraser, J., Wadsley, J, and Wong, H. 1991. BMRC real time analyses of the
   tropospheric circulation during TCM90. BMRC Research Report No. 26, Bur. Met. Australia.
Jasper, J.D. (editor) 1991. 'Data assimilation systems': papers presented at the second BMRC modelling workshop,
   September 1990. BMRC Research Report No. 27, Bur. Met. Australia.
Lavery, B.M., Davidson, N.E., Karoly, D.J. and McAvaney, B.J. 1991. A climatology of the western Pacific region based
   on the Australian tropical analysis system. BMRC Research Report No. 28, Bur. Met. Australia.
McAvaney, B.J., Fraser, J.R., Hart, T.L., Rikus, L.J., Bourke, W.P., Naughton, M.J. and Mullenmeister, P. 1991.
   Circulation statistics from a non-diurnal seasonal simulation with the BMRC atmospheric GCM: R21L9. BMRC
   Research Report No. 29, Bur. Met. Australia.
Colman, R.A., McAvaney, B.J., Fraser, J.R. and Dahni, R.R. 1992. Mixed layer ocean and thermodynamic sea ice models
   in the BMRC GCM. BMRC Research Report No. 30, Bur. Met. Australia.
May, P.T. 1992. Wind profiler measurements in the tropics. BMRC Research Report No. 31, Bur. Met. Australia.
Pitman, A.J., Yang, Z.-L., Cogley, J.G. and Henderson-Sellers, A. 1992. Description of bare essentials of surface transfer
   for the Bureau of Meteorology Research Centre AGCM. BMRC Research Report No. 32, Bur. Met. Australia.
Jasper, J.D. and Meighen, P.J. (eds). 1992. 'Modelling weather and climate': papers presented at the third BMRC
   modelling workshop, November 1991. BMRC Research Report No. 33, Bur. Met. Australia.
Power, S.B., Moore, A.M., Post, D.A., Smith, N.R. and Kleeman, R. 1992. On the stability of North Atlantic deep water
   formation in a global ocean general circulation model. BMRC Research Report No. 34, Bur. Met. Australia.
Power, S. and Kleeman, R. 1992. Multiple equilibria in a global ocean general circulation model. BMRC Research Report
   No. 35, Bur. Met. Australia.
Nicholls, N. 1993. Climate change and the El Niño B Southern Oscillation: report of the workshop on climate change and
   the El Niño B Southern Oscillation held at the Bureau of Meteorology Research Centre Melbourne, Australia 31 May
   and 4 June 1993. BMRC Research Report No. 36, Bur. Met. Australia.
Power, S.B., Colman, R.A., McAvaney, B.J., Dahni, R.R., Moore, A.M. and Smith, N.R. 1993. The BMRC coupled
   atmosphere/ocean/sea-ice model. BMRC Research Report No. 37, Bur. Met. Australia.
McAvaney, B.J. and Colman, R.A. 1993. The AMIP experiment: the BMRC AGCM configuration. BMRC Research
   Report No. 38, Bur. Met. Australia.
Jasper, J.D. and Meighen, P. J. (eds). 1993. 'Modelling severe weather': papers presented at the fourth BMRC modelling
   workshop, 26-29 October 1992. BMRC Research Report No. 39, Bur. Met. Australia.
Mullenmeister, P. and Hart, T. 1994. The UNIX verification programs. BMRC Research Report No. 40, Bur. Met.
   Australia.
Jones, D.A. 1994. A numerical vortex finding, tracking and statistics package. BMRC Research Report No. 41, Bur. Met.
   Australia.
Power, S. 1994. A report on two BMRC workshops: modelling and observing sea-ice in a coupled environment,
   November 19th, 1993 and physical oceanography in Melbourne, December 10th, 1993. BMRC Research Report No.
   42, Bur. Met. Australia.
Bender, L.C. and Leslie, L.M. 1994. Evaluation of a Third Generation Ocean Wave Model for the Australian Region.
   BMRC Research Report No. 43, Bur. Met. Australia.
Keenan, T., Holland, G., Rutledge, S., Simpson, J., McBride, J., Wilson, J., Moncrieff, M., Carbone, R., Frank, W.,
   Sanderson, B., Tapper, N. and Hallett, J. 1994. Science Plan B Maritime Continent Thunderstorm Experiment
   (MCTEX). BMRC Research Report No. 44, Bur. Met. Australia.
Pudov, V.D. and Holland, G.J. 1994. Typhoons and Ocean: results of experimental investigations. BMRC Research
   Report No. 45, Bur. Met. Australia.
Jasper, J.D. and Meighen, P.J. (eds). 1994. 'Parametrisation of physical processes': papers presented at the fifth BMRC
   modelling workshop, November 1993. BMRC Research Report No. 46, Bur. Met. Australia.
Dahni, R.R. and Stern, H. 1995. The development of a generalised UNIX version of the Victorian Regional Office's
   operational analogue statistics model. BMRC Research Report No. 47, Bur. Met. Australia.
Power, S., Kleeman, R., Colman, R. and McAvaney, B. 1995. Modelling the surface heat flux response to long-lived SST
   anomalies in the North Atlantic. BMRC Research Report No. 48, Bur. Met. Australia.
Seaman, R.S. 1995. Bias detection in the Australian sea level pressure observing network. BMRC Research Report No.
   49, Bur. Met. Australia.
May, P.T., Holland, G.J., Keenan, T.D., Le Marshall, J.F., Colquhoun, J., Buckley, B., Love, G., Elliot, J., Dear, J. and
   Potts, R. 1995. Towards a comprehensive, very-short-range forecasting system for the Greater Sydney Region:
   observing system. BMRC Research Report No. 50, Bur. Met. Australia.
Sanderson, B., Tang, Y.M., Holland, G., Grimshaw, R. and Woodcock, F. 1995. A tropical cyclone maximum envelope
   of waters (MEOW) technique. BMRC Research Report No. 51, Bur. Met. Australia.
Meighen, P.J. and Jasper. J.D. (eds). 1996. 'Data assimilation in meteorology and oceanography': papers presented at the
   sixth annual BMRC modelling workshop, October 1994. BMRC Research Report No. 52, Bur. Met. Australia.
Keenan, T.D. and Manton, M.J. 1996. Darwin climate monitoring and research station: observing precipitating systems in
   a monsoon environment. BMRC Research Report No. 53, Bur. Met. Australia.
Meighen, P.J. and Jasper. J.D. (eds). 1996. 'Numerical methods': papers presented at the seventh annual BMRC modelling
   workshop, 4-6 December 1995. BMRC Research Report No. 54, Bur. Met. Australia.
Ebert. E.E. 1996. Results of the 3rd algorithm intercomparison project (AIP-3) of the global precipitation climatology
   project (GPCP). BMRC Research Report No. 55, Bur. Met. Australia.
McAvaney, B.J. and Hess, G.D. 1996. The revised surface fluxes parametrisation in BMRC: formulation. BMRC
   Research Report No. 56, Bur. Met. Australia.
Keenan, T., Puri, K., Mills, G. and Bowen, R. 1996. Regional BMRC analyses of atmospheric circulation during
   MCTEX. BMRC Research Report No. 57, Bur. Met. Australia.
Hess, G.D., McBride, J.L., Drosdowsky, W. and Whitby, F. 1996. A meteorological investigation into the Qantas flight
   69 incident of severe turbulence. BMRC Research Report No. 58, Bur. Met. Australia.
Keenan, T.D. and Glasson, K. 1996. 'MCTEX C-band polarimetric atlas and data summary'. BMRC Research Report No.
   59, Bur. Met. Australia.
Keenan, T.D. and Le Marshall, J. 1996. Satellite data summary and GMS imagery during MCTEX. BMRC Research
   Report No. 60, Bur. Met. Australia.
Meighen, P.J. and Jasper. J.D. (eds). 1997. 'Symposium on climate prediction and predictability': papers presented at the
   eighth modelling workshop, 12-14 November 1996. BMRC Research Report No. 61, Bur. Met. Australia.
Potts, R., Monypenny, P. and Middleton, J. 1997. An analysis of winds at Sydney Kingsford Smith airport and their
   impact on runway availability. BMRC Research Report No. 62, Bur. Met. Australia.
Sanderson, B. 1997. A barotropic ocean model for calculating storm surges. BMRC Research Report No. 63, Bur. Met.
   Australia.
Meighen, P.J. and Jasper. J.D., (eds). 1997. 'Improving short-range forecasting': abstracts of presentations at the ninth
   annual BMRC modelling workshop, 8-10 October 1997. BMRC Research Report No. 64, Bur. Met. Australia.
Drosdowsky, W. and Chambers, L. 1998. Near global sea surface temperature anomalies as predictors of Australian
   seasonal rainfall. BMRC Research Report No. 65, Bur. Met. Australia.
Power, S.B., Tseitkin, F., Colman, R.A. and Sulaiman, A. 1998. A coupled general circulation model for seasonal
   prediction and climate change research. BMRC Research Report No. 66, Bur. Met. Australia.
Power, S., Tseitkin, F., Mehta, V., Lavery, B., Torok, S. and Holbrook, N. 1998. Decadal climate variability in Australia
   during the 20th century. BMRC Research Report No. 67, Bur. Met. Australia.
Keenan, T., Kondratiev, V., Buis, B. and Christmas, R. 1998. The Bureau of Meteorology Research Centre (BMRC)
   portable automatic weather station: description and operation. BMRC Research Report No. 68, Bur. Met. Australia.
Meighen, P.J. (ed.). 1998. 'Coupled climate modelling': abstracts of presentations at the tenth BMRC modelling
   workshop, 12-13 October 1998. BMRC Research Report No. 69, Bur. Met. Australia.
Jones, D.A. 1998. The prediction of Australian land surface temperatures using near global sea surface temperature
   patterns. BMRC Research Report No. 70, Bur. Met. Australia.
Tang, Y.M., Smith, N. and Greenslade, D. 1998. 'Comparison of model and observed surface winds', (report prepared for
   AMSA project). BMRC Research Report No. 71, Bur. Met. Australia.
Potts, R.J., Keenan, T. and May, P. 1999. Radar characteristics of storms in the Sydney area. BMRC Research Report No.
   72, Bur. Met. Australia.
Greenslade, D. 1999. The assimilation of ERS-2 altimeter data into the Australian wave model. BMRC Research Report
   No. 73, Bur. Met. Australia.
Wang, Y. 1999. A triply-nested movable mesh tropical cyclone model with explicit cloud microphysics B (TCM3).
   BMRC Research Report No. 74, Bur. Met. Australia.
Jasper, J.D. and Meighen, P.J. (eds) 1999. 'Parallel computing in meteorology and oceanography': abstracts of
   presentations at the eleventh annual BMRC modelling workshop, 9-11 November 1999. BMRC Research Report No.
   75, Bur. Met. Australia.
Mills, G.A. 2000. A synoptic/diagnostic study of the 1998 Sydney-Hobart yacht race storm - a warm cored extratropical
   cyclone. BMRC Research Report No. 76, Bur. Met. Australia.
Wang, G., Kleeman, R., Smith, N. and Tseitkin, F. 2000. Seasonal predictions with a coupled global ocean-atmosphere
   model. BMRC Research Report No. 77, Bur. Met. Australia.
Timbal, B. and McAvaney, B.J. 2000. A downscaling procedure for Australia. BMRC Research Report No. 78, Bur. Met.
   Australia.
Greenslade, D.J.M. 2000. Upgrades to the Bureau of Meteorology's ocean wave forecasting system. BMRC Research
   Report No. 79, Bur. Met. Australia.
Jasper, J.D. and Meighen, P.J. (eds) 2000. 'Model systematic errors': Extended abstracts of presentations at the twelfth
   annual BMRC modelling workshop (co-sponsored by WCRP/WGNE), 16-20 October 2000. BMRC Research Report
   No. 80, Bur. Met. Australia.
Viviand, J., Lirola, S., Timbal, B., Power, S. and Colman, R. 2000. Impact of soil moisture on climate variability and
   predictability. BMRC Research Report No. 81, Bur. Met. Australia
Mills, G.A. 2001. Impact of screen-level moisture observations in a regional data assimilation system. BMRC Research
   Report No. 82, Bur. Met. Australia.
Zhong A., Colman, R., Smith, N., Naughton, M., Rikus, L., Puri, K. and F. Tseitkin. 2001. Ten-year AMIP 1
   climatologies from versions of the BMRC Atmospheric model. BMRC Research Report No. 83, Bur. Met. Australia.
Jasper, J.D. and Meighen, P.J. (eds) 2001. 'Understanding the climate of Australia and the Indo-Pacific region': Extended
   abstracts of presentations at the thirteenth annual BMRC modelling workshop, 14-16 November 2001. BMRC
   Research Report No. 84, Bur. Met. Australia.
Keenan, T., Joe, P., Wilson, J., Collier, C., Golding, B., Burgess, D., May, P., Pierce, C., Bally, J., Crook, A., Seed, A.,
   Sills, D., Berry, L., Potts, R., Bell, I., Fox, N., Ebert, E., Eilts, M., O'Loughlin, K., Webb, R., Carbone, R., Browning,
   K., Roberts, R. and Mueller, C. 2002. World Weather Research Programme forecast demonstration project: overview
   and current status. BMRC Research Report No. 85, Bur. Met. Australia.
Chambers, L., Li, F. and Nicholls, N. 2002. Seasonal climate forecasts for south-west Western Australia. BMRC Research
   Report No. 86, Bur. Met. Australia.
Voldoire, A., Timbal, B. and Power, S. 2002. Statistical-dynamical seasonal forecasting. BMRC Research Report No. 87,
   Bur. Met. Australia.
Lemus-Deschamps, L., Colman, R., Fraser, J. and Zhong, A. 2002. The model and climatological data comparison system
   (MACCS). BMRC Research Report No. 88, Bur. Met. Australia.
Zhang, H., Henderson-Sellers, A., Irannejad, P., Sharmeen, S., Phillips, T. and McGuffie, K. 2002. Land-surface
   modelling and climate simulations: results over the Australian region from sixteen AMIP II models. BMRC Research
   Report No. 89, Bur. Met. Australia.
Hollis, A.J. and Meighen, P.J. (eds) 2002. 'Modelling and predicting extreme events'; Extended abstracts of presentations
   at the fourteenth annual BMRC modelling workshop, 11-13 November 2002. BMRC Research Report No. 90, Bur.
   Met. Australia.
Wu, Z-J., Colman, R., Power, S., Wang, X. and B. McAvaney. 2002. The El Niño Southern Oscillation response in the
   BMRC Coupled GCM. BMRC Research Report No. 91, Bur. Met. Australia.
Chambers, L.E. 2003. South Australian rainfall variability and trends. BMRC Research Report No. 92, Bur. Met.
   Australia.
Meighen, P.J. and Hollis, A.J. (eds) 2003. 'Current issues in the parameterization of convection’: Extended abstracts of
   presentations at the fifteenth annual BMRC modelling workshop, 13-16 October 2003. BMRC Research Report No.
   93, Bur. Met. Australia.
Sun, X., Manton, M.J. and Ebert, E.E. 2003. Regional rainfall estimation using double-kriging of raingauge and satellite
   observations. BMRC Research Report No. 94, Bur. Met. Australia.
Zhong, A., Alves, O., Schiller, A., Tseitkin, F. and Smith, N. 2004. Results from a preliminary version of the
   ACOM2/BAM coupled seasonal forecast model. BMRC Research Report No. 95, Bur. Met. Australia.

								
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