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3094 


JOURNAL OF CLIMATE 


Volume 23 


Intraseasonal Variation of Winter Precipitation over the Western United States 
Simulated by 14 IPCC AR4 Coupled GCMs 

Jia-Lin Lin,* Toshiaki Shinoda," 1 " Taotao Qian,*’ # Weiqing Han,® Paul Roundy, & 

and Yangxing Zheng** 

* Department of Geography, The Ohio State University, Columbus, Ohio 
' Naval Research Laboratory, Stennis Space Center, Mississippi 
# Byrd Polar Research Center, The Ohio State University, Columbus, Ohio 
® Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Colorado 
& University at Albany, State University of New York, Albany, New York 
** NOAA/ESRL/CIRES Climate Diagnostics Center, Boulder, Colorado 


(Manuscript received 18 November 2008, in final form 16 June 2009) 

ABSTRACT 

This study evaluates the intraseasonal variation of winter precipitation over the western United States in 
14 coupled general circulation models (GCMs) participating in the Intergovernmental Panel on Climate 
Change (IPCC) Fourth Assessment Report (AR4). Eight years of each model's twentieth-century climate 
simulation are analyzed. The focus is on the two dominant intraseasonal modes for the western U.S. pre¬ 
cipitation: the 40-day mode and the 22-day mode. 

The results show that the models tend to overestimate the northern winter (November-April) seasonal 
mean precipitation over the western United States and Canada. The models also tend to produce overly 
strong intraseasonal variability in western U.S. wintertime precipitation, in spite of the overly weak tropical 
intraseasonal variability in most of the models. All models capture both the 40-day mode and the 22-day 
mode, usually with overly large variances. For the 40-day mode, models tend to reproduce its deep barotropic 
vertical structure and three-cell horizontal structure, but only 5 of the 14 models capture its northward 
propagation, and only 2 models simulate its teleconnection with the Madden-Julian oscillation in the tropical 
Pacific. For the 22-day mode, 8 of the 14 models reproduce its coherent northward propagation, and 9 models 
capture its teleconnection with precipitation in the tropical Pacific. 


1. Introduction 

The western United States normally receives the bulk 
of its precipitation during Northern Hemisphere (NH) 
winter from October to April, when the storm track 
across the North Pacific is active (e.g.. Mo and Nogues- 
Paegle 2005). Rainfall in the western United States 
during this season is significantly modulated on the in¬ 
traseasonal time scale (Mo and Higgins 1998a,b; Mo 
1999). For example, alternating wet and dry episodes 
with periods around 20 days are often observed at coastal 
stations in California (Mo 1999), and strong flooding in 
California is often associated with rainfall events on the 


Corresponding author address: Dr. Jia-Lin Lin, Dept, of Geog¬ 
raphy, The Ohio State University, 1105 Derby Hall, 154 North 
Oval Mall, Columbus, OH 43210. 

E-mail: lin.789@osu.edu 

DOI: 10.1175/2009JCLI2991.1 


submonthly time scale (e.g., Mo and Nogues-Paegle 
2005). 

Mo (1999) demonstrated that the intraseasonal vari¬ 
ability of western U.S. winter precipitation has two 
dominant modes: a mode with a period of about 36- 
40 days (hereafter the 40-day mode) and a mode with a 
period of about 20-25 days (hereafter the 22-day mode). 
Previous studies have found four mechanisms for generat¬ 
ing the intraseasonal variability of western U.S. winter 
precipitation (Fig. 1): 1) instability of the basic state (e.g., 
Simmons et al. 1983; Schubert 1986; Frederiksen 1986; 
Dole and Black 1990; Schubert et al. 1993), 2) orographic 
forcing (Marcus et al. 1994, 1996), 3) interactions with 
synoptic-scale eddies (Lau 1988; Held et al. 1989), and 
4) forcing of tropical convection (Mo and Higgins 1998a, 
1998b, Mo 1999). Of particular importance for extended- 
range weather forecasts is the tropical forcing mecha¬ 
nism. As shown by Mo (1999), the 40-day mode is related 
to the Madden-Julian oscillation (MJO) in the tropics, 


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4. TITLE AND SUBTITLE 

Intraseasonal Variation of Winter Precipitation over the Western United 
States Simulated by 14 IPCC AR4 Coupled GCMs 

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7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 

Naval Research Laboratory,Stennis Space Center,MS,39529 

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1 June 2010 


LIN ET AL. 


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Longitude (deg) 


Fig. 1. Schematic depiction of the suggested mechanisms for the intraseasonal variability of 
western U.S. winter precipitation. Contour is the northern winter (November-April) seasonal 
mean GPCP precipitation. The first contour is 1 mm day -1 , and the contour interval is 
2 mm day -1 . The black arrow schematically shows that these modes are propagated from the 
tropical Pacific. 


with enhanced convection propagating from the west¬ 
ern Pacific to the central Pacific. The spatial structure 
of precipitation anomaly excited by the propagation 
of convection exhibits a north-south three-sell pattern. 
Heavy precipitation in California is associated with dry 
conditions over Washington; British Columbia, Canada; 
and along the southeastern coast of Alaska and reduced 
precipitation over the subtropical eastern Pacific (Mo 
and Higgins 1998a). When enhanced convection moves 
to the central Pacific, the response in the Northern Hemi¬ 
sphere resembles the Pacific-North American (PNA) 
teleconnection pattern (Wallace and Gutzler 1981; 
Weickmann et al. 1985; Knutson and Weickmann 1987). 
The 22-day mode is also related to tropical convection 
with cloud bands propagating northward along the west 
coast of North America from the eastern Pacific through 
California to the Pacific Northwest. The spatial structure 
of this mode is similar to the traveling pattern described 
by Branstator (1987). 

These intraseasonal modes are responsible for alter¬ 
nating wet and dry episodes over the western United 
States. However, only a few previous studies have 
examined their simulations by the general circulation 
models (GCMs). In a pioneering study, Schubert et al. 
(1993) examined the simulations by an atmospheric 
GCM developed at the National Aeronautics and 
Space Administration (NASA) Goddard Laboratory for 


Atmospheres. They found that the GCM’s leading mode 
in the upper-tropospheric zonal wind is associated with 
fluctuations of the East Asian jet; this mode resembles 
the structure of the PNA pattern found in the observa¬ 
tions on these time scales. The GCM produces 60% of 
the total observed Pacific sector low-frequency zonal 
wind variance. About one-third of the missing vari¬ 
ability appears to be due to unrealistic simulations of 
the MJO. 

Recently, in preparation for the Intergovernmental 
Panel on Climate Change (IPCC) Fourth Assessment 
Report (AR4), more than a dozen international climate 
modeling centers conducted a comprehensive set of 
long-term simulations for both the twentieth century’s 
climate and different climate change scenarios in the 
21st century (Randall et al. 2007). Before conducting the 
extended simulations, many of the modeling centers 
applied an overhaul to their physical schemes to incor¬ 
porate the state-of-the-art research results. For example, 
almost all modeling centers have implemented prog¬ 
nostic cloud microphysics schemes to their models, 
some have added a moisture trigger to their deep con¬ 
vection schemes, and some now take into account con¬ 
vective momentum transport. Moreover, many modeling 
centers increased their models’ horizontal and vertical 
resolutions, and some conducted experiments with dif¬ 
ferent resolutions. 














downdrafts are Meso. CAPE means convective available potential energy. 


Volume 23 


3096 


JOURNAL OF CLIMATE 


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CSIRO Atmospheric Research CSIRO Mk3.0 (CSIRO) Spectral T63 X L18 4 mb Gregory and Rowntree (1990) Y/N/N Cloud-base 

buoyancy 









1 June 2010 


LIN ET AL. 


3097 


(a) OBS(GPCP) Mean Nov-Apr 



Longitude (deg) 


(b) GFDL2.0 Nov-Apr 



200 220 240 260 280 

Longitude (deg) 




Longitude (deg) 


Longitude (deg) 



Longitude (deg) Longitude (deg) 


Fig. 2. Northern winter (November-April) seasonal mean precipitation for (a) observation 
and (b)-(h) the 14IPCC AR4 models. The first contour is 2 mm day -1 , and the contour interval 
is 2 mm day -1 . 


The purpose of this study is to evaluate the intra- 
seasonal variation of winter precipitation over the west¬ 
ern United States in 14 IPCC AR4 coupled GCMs, with 
emphasis on the 40-day mode and the 22-day mode. The 
models and validation datasets used in this study are 
described in section 2. The diagnostic methods are de¬ 
scribed in section 3. Results are presented in section 4. 
A summary and discussion are given in section 5. 


2. Models and validation datasets 

This analysis is based on 8 yr of the Climate of the 
Twentieth Century (20C3M) simulations from 14 cou¬ 
pled GCMs. Table 1 shows the model names and acro¬ 
nyms, their horizontal and vertical resolutions, and brief 
descriptions of their deep convection schemes. For each 
model we used 8 yr of daily mean surface precipitation. 








































3098 


JOURNAL OF CLIMATE 


Volume 23 



(j) MIROC-hires Nov-Apr 



Longitude (deg) 
(k) MRI Nov-Apr 


220 240 260 

Longitude (deg) 

(I) CGCM Nov-Apr 


280 



(m) MPI Nov-Apr 



200 


220 240 260 

Longitude (deg) 


280 



Longitude (deg) 
(p) CSIRO Nov-Apr 



Longitude (deg) 


Longitude (deg) 


Fig. 2. ( Continued) 


Three-dimensional data are available for 7 of the 14 models, 
for which we analyzed upper air winds, temperature, and 
specific humidity. 

The model simulations were validated using the Global 
Precipitation Climatology Project (GPCP) Version 2 
Precipitation (Huffman et al. 2001). We used 8 yr (1997- 
2004) of daily data with a horizontal resolution of 
1° longitude X 1° latitude. Obtaining reliable precipita¬ 
tion estimates, especially over the open ocean area where 


surface observations are sparse, continues to be a big 
challenge for the research community and was the mo¬ 
tivation for the international GPCP project. The GPCP 
dataset is a merged analysis incorporating available pre¬ 
cipitation estimates from low-orbit-satellite microwave 
data, geosynchronous-orbit-satellite infrared data, and 
rain gauge observations. Gruber and Levizzani (2008) 
provided a detailed assessment of the GPCP dataset. 
The data quality varies significantly from region to region. 

















































1 June 2010 


LIN ET AL. 


3099 



Latitude (deg) 

Fig. 3. Meridional profile of northern winter (November-April) seasonal mean precipitation 
(mm day^ 1 ) averaged between 125° and 115°W for observation and 14 models. 


Fortunately, the region of interest for this study (the 
western United States and surrounding regions) is asso¬ 
ciated with relatively good data quality, although sub¬ 
stantial uncertainties still exist (see Fig. 2.2 of Gruber and 
Levizzani 2008). 

To evaluate the model-simulated atmospheric circu¬ 
lation, we also used 8 yr (1997-2004) of daily National 
Centers for Environmental Prediction (NCEP) rean¬ 
alysis data (Kalnay et al. 1996), for which we analyzed 
upper air winds, temperature, and specific humidity. 
There are possible errors associated with the reanalysis 
data coming from measurement errors, poor data cov¬ 
erage over certain geographical regions, and effects of 
assimilation models. Flowever, previous studies have 
shown that the errors could be significantly reduced by 
spatial averaging over many grid points and constructing a 
composite over many events (e.g., Carr and Bretherton 
2001; Lin et al. 2005, 2008). 

3. Methods 

Total intraseasonal (periods 10-90 days) anomalies 
were obtained by applying a 365-point 10-90-day Lanczos 
filter (Duchan 1979). Because the Lanczos filter is non¬ 
recursive, 182 days of data were lost at each end of the 
time series (364 days in total). The dominant intraseasonal 
modes are determined using wavelet spectra because they 
are active mainly during the southern summer. Wavelet 
spectrum is a powerful tool for analyzing multiscale, 


nonstationary processes. Its uniqueness is its ability to 
simultaneously localize the variability of the signal in 
both the frequency and time domains by using general¬ 
ized local base functions (wavelets) that can be stretched 
and translated with a flexible resolution in both frequency 
and time (e.g., Mak 1995; Torrence and Compo 1998). In 
other words, one can simultaneously determine both the 
dominant modes of variability and how those modes vary 
in time. We utilize the wavelet analysis program de¬ 
veloped by Torrence and Compo (1998) and use the 
Morlet wavelet as the mother wavelet. We have tested 
different mother wavelets (Paul or Derivative of Gaussian), 
and the results are similar. The 40-day mode, defined as 
precipitation variability in the period range of 30-60 days, 
was obtained by applying a 365-point 30-60-day Lanczos 
filter. Similarly, the 22-day mode is defined as precipita¬ 
tion variability in the period range of 20-30 days and was 
obtained by applying a 365-point 20-30-day Lanczos 
filter. We also tested the Murakami (1979) filter, and the 
results are similar. 

4. Results 

a. Northern winter (November-April) seasonal 
mean precipitation 

Previous observational studies indicate that the intra¬ 
seasonal variance of precipitation is highly correlated with 
time-mean precipitation (e.g., Wheeler and Kiladis 1999). 
Therefore, we first look at the horizontal distribution of 







3100 


JOURNAL OF CLIMATE 


Volume 23 


(a) OBS(GPCP) 10-90day Nov-Apr (b) GFDL2.0 Nov-Apr 



Longitude (deg) 


(e) PCM Nov-Apr 



Longitude (deg) 




200 220 240 260 280 


Longitude (deg) 



Longitude (deg) 


200 220 240 260 280 


Longitude (deg) 


Longitude (deg) 


Fig. 4. Horizontal distribution of the standard deviation of total intraseasonal (10-90 day) 
precipitation anomaly during northern winter (November-April) (a) observation and (b)-(h) 
the 14 IPCC AR4 models. The first contour is 2 mm day -1 , and the contour interval is 
1 mm day -1 . 


northern winter (November-April) seasonal mean pre¬ 
cipitation (Fig. 2). If we use the 2 mm day -1 contour to 
define the gross horizontal pattern of precipitation in 
observation, all 14 models capture reasonably this gross 
pattern. In particular, they all produce the NE-SW-tilted 
North Pacific storm track. Most of them also reproduce 
the peak along the west coast of the United States and 


Canada. The eastern Pacific ITCZ is also reasonably 
simulated by all models although with a large variation 
in precipitation magnitude. 

To conduct a more quantitative evaluation of the 
seasonal mean precipitation over the western United 
States, we plot in Fig. 3 the meridional profile averaged 
between 235° and 245°E. There is a wide spread among 




































1 June 2010 


LIN ET AL. 


3101 



(k) MRI Nov-Apr (l) CGCM Nov-Apr 



200 220 240 260 280 200 220 240 260 280 



200 220 240 260 280 


Longitude (deg) 


Longitude (deg) 
(p) CSlRO Nov-Apr 



Longitude (deg) 


Longitude (deg) 


Fig. 4. ( Continued ) 


the models. All but two models [the Meteorological 
Research Institute model (MRI-CGCM2.3.2, hereafter 
MRI) and the Goddard Institute for Space Studies 
ER model (GISS-ER)] overestimate the precipitation 
by more than 30%. The MRI model precipitation is in 
excellent agreement with observation. The precipita¬ 
tion peak is shifted slightly northward in one model 
(GISS-ER) but slightly southward in two others [the 
GISS Atmosphere-Ocean Model (GISS-AOM) and the 


Institute Pierre Simon Laplace Climate Model version 4 
(IPSL-CM4, hereafter IPSL)]. 

b. Total intraseasonal (10-90 day) variance 

Figure 4 shows the horizontal distribution of the total 
intraseasonal (10-90 day) variance of precipitation dur¬ 
ing northern winter (November-April). In observation 
(Fig. 4a), the horizontal distribution of total intrasea¬ 
sonal variance follows that of seasonal mean precipitation 






































3102 


JOURNAL OF CLIMATE 


Volume 23 



Latitude (deg) 

Fig. 5. Meridional profile of the total intraseasonal (10-90 day) variance (mm 2 day” 2 ) of 
precipitation anomaly averaged between 125° and 115°W. 


(Fig. 2), except that the variance over the North Pacific 
storm track is shifted slightly southward compared to the 
seasonal mean precipitation. The model variances show 
three characteristics. First, all models capture the ba¬ 
sic spatial pattern of the variance, including the slight 
southward shift compared to the seasonal mean pre¬ 
cipitation. Second, most models produce overly large 
variance along the west coast of the United States and 
Canada. Third, all models underestimate the variance 
over the North Pacific Ocean, in spite of the fact that they 
generally produce reasonable seasonal mean precipi¬ 
tation in that region (Fig. 2). This suggests an interesting 
land-sea contrast in the models’ ability to simulate ex- 
tratropical intraseasonal variability, with a better per¬ 
formance over land than over ocean. 

To provide a more quantitative evaluation of the model 
simulations, Fig. 5 shows the meridional profile of total 
intraseasonal (10-90 day) variance of precipitation during 
northern winter averaged between 125°E and 115°W. Over 
the western United States and Canada, all but two models 
(MRI and GISS-ER) produce a variance that is 2-7 times 
the observed variance, which is consistent with their overly 
large seasonal mean precipitation (Fig. 3). This is in sharp 
contrast with the models’ simulations of tropical intra¬ 
seasonal variability (Lin et al. 2006). Although the models 
generally produce reasonable seasonal mean tropical pre¬ 
cipitation, only a few of them could simulate reasonable 
tropical intraseasonal variability, suggesting that the trop¬ 
ical intraseasonal variability is generated by mechanisms 
different from the extratropical intraseasonal variability. 


c. The dominant intraseasonal modes 

Figure 6 shows the wavelet spectrum of precipitation 
averaged between 40°-45°N and 125°-115°W for obser¬ 
vation and the 14IPCC models. The Morlet wavelet was 
used as the mother wavelet. We have tested different 
mother wavelets (Paul or Derivative of Gaussian), and 
the results look similar. The observed spectrum (Fig. 6) 
demonstrates two dominant intraseasonal modes, a 30- 
60-day mode (the so-called 40-day mode) and a 15-30-day 
mode (the so-called 22-day mode). All models capture 
both modes, and the model variances are generally larger 
than the observed variances. The models also tend to 
produce more frequent active episodes. 

d. The 40-day mode 

Next we focus on the 40-day mode. Figure 7 shows the 
meridional profile of the 40-day mode variance averaged 
between 125° and 115°W. For both the observation and the 
models, the spatial distribution of the 40-day mode vari¬ 
ance looks quite similar to that of the total intraseasonal 
variance. All but one model (MRI) produce 2-9 times the 
observed 40-day mode variance over the western United 
States and Canada. The MRI model variance is in very 
good agreement with the observed variance. 

Figure 8 shows the lag-correlation of the 40-day 
mode precipitation anomaly averaged between 125° and 
115°W with respect to itself at 37.5°N, 240°E. Shading 
denotes the regions where lag-corrclation is above the 
95% confidence level. In observation (Fig. 8a), the 40-day 






1 June 2010 


LIN ET AL. 


3103 


ot ObslGPCPl 42N120W 


(b) GFDL2.0 



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Fig. 6. Wavelet spectrum of precipitation averaged between 40°-45°N, 125°-115°W. 


mode propagates northward from 10° to 50°N, which is 
consistent with the results of Mo (1999). Five of the 14 
models simulate coherent northward propagations [the 
Geophysical Fluid Dynamics Laboratory Climate Model 
version 2.0 (GFDL-CM2.0, hereafter GFDL2.0), the 
Community Climate System Model version 3 (CCSM3), 
MRI, the Canadian Centre for Climate Modeling 
and Analysis Coupled General Circulation Model 
(CGCM3.1-T47, hereafter CGCM), and IPSL], Two 
models produce standing oscillation [GFDL2.1 and the 


National Center for Atmospheric Research (NCAR) 
Parallel Climate Model (PCM)], while the other seven 
models simulate southward propagations [GISS-AOM, 
GISS-ER, the Model for Interdisciplinary Research 
on Climate-medres (MIROC-medres), MIROC-hires, 
the Max Planck Institute for Meteorology (ECHAM5/ 
MPI-OM, hereafter MPI), the Meteo-France/Centre 
National de Recherches Meteorologiques Climate 
Model version 3 (CNRM-CM3, hereafter CNRM), and 
the Commonwealth Scientific and Industrial Research 















































































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(0 Obs(GPCP) 42N120W 


(il MIROC-hires 



01 2345678 

Time (yeor) 

'k! MRI 


01 2345678 

Time (year) 

01 CGCM 


"D 

O 



01 2345678 

Time (year) 


01 2345678 

Time (year) 




Time (year) 


(o) CSIRO 


01 2345678 

Time (year) 



01 2345678 

Time (year) 


Fig. 6. ( Continued !) 


Organisation Mk 3.0 Climate System (CSIRO Mk3.0, 
hereafter CSIRO)]. 

Next we look at the vertical structures of the 40-day 
mode. Figure 9 shows the vertical structure of tempera¬ 
ture for observation (NCEP reanalysis) and seven models 
with three-dimensional data available. Note that for four 
models the 3D data is available only below 200 mb. In 
observation, the 40-day mode displays a two-layer struc¬ 
ture during the precipitating phase, with a cold core be¬ 
tween surface and 250 mb, and a warm core above 250 mb. 


Five of the seven models (GFDL2.0, GFDL2.1, CGCM, 
MPI, and CNRM) reproduce the two-layer structure. In 
GISS-AOM the two-layer structure is shifted to the later 
phase by about 7 days. MRI simulates a cold core be¬ 
tween 200 and 850 mb, and a warm core below 850 mb. 

Figure 10 shows the vertical structure of geopotential 
height. Consistent with the temperature structure, the 
observed geopotential height displays a deep barotropic 
structure, with negative anomaly extending from the sur¬ 
face to 100 mb during the precipitating phase (Fig. 10a). 
















































































1 June 2010 


LIN ET AL. 


3105 



Latitude (deg) 

Fig. 7. As in Fig. 5, but for the variance of the 40-day mode. 


All models reproduce the deep barotropic structure. 
However, in four models the correlation is low in the up¬ 
per troposphere (GFDL2.1, GISS-AOM, MRI, and MPI). 

Figure 11 shows the vertical structure of divergence. 
The observed divergence displays a two-layer structure 
during the precipitating phase, with convergence from 
the surface to 650 mb, and divergence above 650 mb 
(Fig. 11a). All but one model (MPI) reproduce fairly 
well the two-layer structure, although in GISS-AOM 
(Fig. lid) the convergence layer is too deep, extending 
from the surface to 450 mb. 

Next we look at the teleconnection pattern associated 
with the 40-day mode. Figure 12 shows the linear cor¬ 
relation of the 40-day-mode precipitation anomaly versus 
itself averaged between 35° and 40°N, 125° and 115°W. In 
observation (Fig. 12a), there is a three-cell pattern with 
positive precipitation anomaly over the western United 
States and negative anomalies over the eastern Pacific and 
the Pacific Northwest. At the same time, there is a dipole 
over the tropical Pacific with positive anomaly in the 
central Pacific and negative anomaly in the western Pacific. 
These are consistent with the results of Mo (1999, her 
Fig. 5c), who demonstrated that the dipole over the tropical 
Pacific is associated with the MJO. Most of the models 
simulate to some extent the three-cell pattern around the 
western United States. However, only two models (CCSM3 
and PCM) simulate the dipole over the tropical Pacific. 
Four other models (GISS-AOM, GISS-ER, MIROC- 
medres, and MIROC-hires) produce statistically significant 
positive anomaly in the central Pacific but no statistically 
significant negative anomaly in western Pacific. 


To summarize, the models tend to simulate overly large 
variance of the 40-day mode over the western United 
States and Canada. All models with three-dimensional 
data available reproduce the deep barotropic structure 
of the 40-day mode. All models reproduce to some extent 
the three-cell pattern of precipitation anomaly around the 
western United States, but only five models capture the 
northward propagation, and only two models simulate 
the teleconnection with the MJO in tropical Pacific. 

e. The 22-day mode 

Figure 13 shows the meridional profile of the 22-day 
mode variance averaged between 125° and 115°W. For 
both the observation and the models, the spatial distri¬ 
bution of the 22-day mode variance looks quite similar 
to that of the total intraseasonal variance and the 40-day 
mode. Eleven of the 14 models (GFDL2.0, GFDL2.1, 
CCSM3, PCM, GISS-AOM, MIROC-medres, MIROC- 
hires, CGCM, MPI, IPSL, and CSIRO) produce more 
than 2 times the observed 22-day mode variance over 
the western United States and Canada, while 3 models 
(MRI, CNRM, and GISS-ER) produce variances that 
are very close to the observed value. 

Figure 14 shows the lag-correlation of the 22-day- 
mode precipitation anomaly averaged between 125° and 
115°W with respect to itself at 37.5°N, 240°E. In obser¬ 
vation (Fig. 14a), the 22-day mode propagates north¬ 
ward from the equator to 45°N, which is consistent with 
the results of Mo (1999). Nine of the 14 models simulate 
coherent northward propagation (GFDL2.0, GFDL2.1, 









3106 


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(a) P Obs(GPCP) 40day Nov-Apr 



0 10 20 30 40 50 60 


Latitude (deg) 



(c) P GFDL2.1 Nov-Apr 

-20 

\ J .■ 



^ -10 
(S) 

■ °. S O', . 

o 

-o 

£r?m - 

Log ( 

> c 


^ / ™oc 

IU 

20 

V 



0 10 20 30 40 50 60 

Latitude (deg) 

(e) P PCM Nov-Apr 



0 10 20 30 40 50 60 


Latitude (deg) 



(b) P GFDL2.0 Nov-Apr 



(d) P CCSM3 Nov-Apr 



(f) P GISS-AOM Nov-Apr 



0 10 20 30 40 50 60 

Latitude (deg) 



0 10 20 30 40 50 60 0 10 20 30 40 50 60 

Latitude (deg) Latitude (deg) 

Fig. 8. Lag-correlation of the 40-day-mode precipitation anomaly averaged between 235° 
and 245°E with respect to itself at 37.5°N, 240°E for the 14 IPCC AR4 models. Shading denotes 
the regions where lag-correlation is above the 95% confidence level. 


CCSM3, GISS-ER, MIROC-hires, MRI, CGCM, MPI, 
and CSIRO). Three models produce standing oscillation 
(GFDL2.1, GISS-AOM, and CNRM), one model sim¬ 
ulates southward propagation (PCM), and two models 
display different propagation direction in different re¬ 
gions (MIROC-medres and IPSL). 

Figure 15 shows the teleconnection pattern of the 
22-day mode. In observation (Fig. 15a), there is a positive 


anomaly extending from western United States to 15°N, 
210°E, a positive anomaly around 15°S, 210°E, and a 
negative anomaly around 15°N, 130°E. Nine of the 
14 models reproduce a statistically significant positive 
anomaly around 15°N, 210°E (GFDL2.0, CCSM3, 
GISS-AOM, GISS-ER, MIROC-medres, MIROC-hires, 
IPSL, CNRM, and CSIRO), although in some models 
it is shifted slightly northward (e.g., GFDL2.0 and CCSM3). 







































1 June 2010 


LIN ET AL. 


3107 


(i) P Obs(GPCP) Nov-Apr 



(k) P MRI Nov—Apr 



0 10 20 30 40 50 60 

Lotitude (deg) 


(m) P MPI Nov—Apr 


A 

<m)\ 



' 'UUv: ; .y. 

•• ■••V 


(j) P MIROC —hires Nov—Apr 



0 10 20 30 40 50 60 

Latitude (deg) 


(I) P CGCM Nov-Apr 



0 10 20 30 40 50 60 

Latitude (deg) 


(a) P IPSL Nov-Apr 



• 

’ / ‘ o' / 

% 1 — 
\ P y ,‘ d 

- 0 l 


0 10 20 30 40 50 60 

Latitude (deg) 


(o) P CNRM Nov—Apr 



0 10 20 30 40 50 60 

Latitude (deg) 


0 10 20 30 40 50 60 

Latitude (deg) 


(p) P CSIRO Nov-Apr 



0 10 20 30 40 50 60 

Latitude (deg) 


Fig. 8. (Continued) 


Three models reproduce a statistically significant positive 
anomaly around 15°S, 210°E (MIROC-hires, CSIRO, 
and GFDL2.1), and only one model simulates a statisti¬ 
cally significant negative anomaly around 15°N, 130°E 
(CSIRO). 

5. Summary and discussion 

This study evaluates the intraseasonal variation of win¬ 
ter precipitation over the western United States in 14IPCC 


AR4 coupled GCMs. The results show that the models 
tend to overestimate the northern winter (November- 
April) seasonal mean precipitation over the western 
United States and Canada. The models also tend to 
produce overly strong intraseasonal variability in west¬ 
ern U.S. wintertime precipitation, in spite of the overly 
weak tropical intraseasonal variability in most of the 
models. All models capture both the 40-day mode and 
the 22-day mode, usually with overly large variances. 
For the 40-day mode, models tend to reproduce its deep 











































3108 


JOURNAL OF CLIMATE 


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(o) T Obs Nov-Apr (b) T GFDLO Nov-Apr 



Lag (days) Lag (days) 


(c) T GFDL1 Nov-Apr (d) T GISS-AOM Nov-Apr 



Log (doys) Log (days) 


(e) T MRI Nov-Apr 



Lag (doys) 



20 10 0 -10 -20 
Lag (days) 


(g) T MPI Nov-Apr 



(h) T CNRM Nov—Apr 

looi- 



20 10 0 -10 -20 
Log (days) 


Fig. 9. Lag-correlation of temperature averaged between 30°-°N, 125°-115°W vs the 40-day- 
mode precipitation anomaly at the same location for observation (NCEP reanalysis) and seven 
models. Shading denotes the area where correlation is above the 95% confidence level, with 
dark (light) shading for positive (negative) correlation. 


barotropic vertical structure and three-cell horizontal 
structure, but only 5 of the 14 models capture its north¬ 
ward propagation, and only 2 models simulate its tele¬ 
connection with the Madden-Julian oscillation in the 
tropical Pacific. For the 22-day mode, 8 of the 14 models 
reproduce its coherent northward propagation, and 
9 models capture its teleconnection with precipitation 
in the tropical Pacific. 


The above results have two implications on the dy¬ 
namics of intraseasonal variability of western U.S. win¬ 
ter precipitation. First, in spite of the lack of MJO and 
overly weak tropical intraseasonal variability in most 
of the models, they still produce overly strong intra¬ 
seasonal variability of western U.S. winter precipitation, 
suggesting that tropical forcing may be a secondary 
mechanism for generating this variability. This is consistent 




























1 June 2010 


LIN ET AL. 


3109 


(o) Z Obs Nov-Apr 



20 10 0 -10 -20 
Lag (days) 


(b) Z GFDLO Nov-Apr 



Lag (days) 


(c) Z GFDL1 Nov-Apr 



20 10 0 -10 -20 
Lag (days) 


(d) Z GISS-AOM Nov-Apr 



Log (days) 


(e) Z MRI Nov-Apr 



20 10 0 -10 -20 
Lag (days) 


(f) Z CGCM Nov-Apr 



Lag (days) 


(g) Z MPI Nov-Apr 

1001 



20 10 0 -10 -20 
Lag (days) 


(h) Z CNRM Nov-Apr 



Fig. 10. As in Fig. 9, but for geopotential height. 


with several previous studies (e.g., Lau 1981; Simmons 
et al. 1983; Karoly et al. 1989; Schubert and Park 1991; 
Schubert et al. 1993). 

Second, a new finding of this study is that several 
models could reproduce the northward propagation of 
the 40-day mode with the lack of MJO signals in those 
models. This suggests that the northward propagation of 
the 40-day mode may not be generated by the Rossby 
wave emanation from the tropical MJO. Theoretical and 
observational studies have suggested several different 
mechanisms for northward propagation of intraseasonal 


modes, including land surface heat flux (Webster 1983; 
Srinivasan et al. 1993), ocean surface sensible heat flux 
(Hsu et al. 2004), vertical-shear-induced boundary layer 
moisture convergence (Jiang et al. 2004), and moisture 
advection (Jiang et al. 2004). In future studies, analyses 
of heat, moisture, and vorticity budgets are needed to 
examine if these mechanisms contribute to the north¬ 
ward propagations in the models. 

Among the 14 coupled GCMs, the MRI model argu¬ 
ably produces the best overall intraseasonal variability of 
western U.S. winter precipitation. This is likely associated 




























































3110 


JOURNAL OF CLIMATE 


Volume 23 


X> 

E 


X) 

E 


a. 


a 


20 10 0 -10 -20 


Lag (doys) 
(c) Div GFDL1 Nov-Apr 



20 10 0 -10 -20 
Lag (doys) 


20 10 0 -10 -20 
Lag (days) 


20 10 0 -10 -20 
Log (days) 



100 

200 

300 

400 

500 

600 

700 

800 

900 

1000 



100 

200 

300 

400 

500 

600 

700 

800 

900 

1000 



100 

200 

300 

400 

500 

600 

700 

800 

900 

1000 




100 
200 
300 
_ 400 

f 500 
600 
700 
800 
900 
1000 

20 


10 0 -10 
Lag (days) 


-20 


100 

200 

300 

400 

500 

600 

700 

800 

900 

1000 

20 


10 0 -10 
Lag (days) 


-20 


(g) Div MPI Nov—Apr 



Lag (days) 


X) 

E 


Q. 



100 

200 

300 

400 

500 

600 

700 

800 

900 

1000 

20 


10 0 -10 
Lag (days) 


-20 


Fig. 11. As in Fig. 9, but for divergence. 


with its good simulation of the wintertime seasonal 
mean precipitation, because our results show that the 
models’ intraseasonal variability generally increases 
with the increase of seasonal mean precipitation. How¬ 
ever, one major caveat of this evaluation is the un¬ 
certainties associated with the precipitation observations, 
which are discussed briefly in section 2. Therefore 
we must be cautious when giving any ranking to the 
models’ simulations. Currently, NASA is planning its 
Global Precipitation Measurement (GPM) Mission to 
improve our measurements of precipitation over both 


the tropics and extratropics. We expect that the next 
generation of precipitation analysis will provide a more 
solid benchmark for evaluating the climate model 
simulations. 

Acknowledgments. Gary Russell kindly provided a 
detailed description of the GISS-AOM model. We ac¬ 
knowledge the international modeling groups for pro¬ 
viding their data for analysis, the Program for Climate 
Model Diagnosis and Intercomparison (PCMDI) for 
collecting and archiving the model data, the JSC/CLIVAR 
















































1 June 2010 


LIN ET AL. 


3111 


(a) Obs(GPCP) 40doy Nov-Apr 



(c) GFDL2.1 Nov-Apr 



0 60 120 180 240 300 360 

Longitude (deg) 



0 60 120 180 240 300 360 

Longitude (deg) 


(g) GISS-ER Nov-Apr 



(b) GFDL2.0 Nov-Apr 



0 60 120 180 240 300 360 

Longitude (deg) 


(d) CCSM3 Nov-Apr 



0 60 120 180 240 300 360 

Longitude (deg) 



0 60 120 180 240 300 360 

Longitude (deg) 


(h) MIROC-medres Nov—Apr 



0 60 120 180 240 300 360 

Longitude (deg) 


Fig. 12. Linear correlation of the 40-day-mode precipitation anomaly vs itself averaged 
between 35 o ^f0°N, 125°-115°W for the 14 models. Shading denotes the area where correla¬ 
tion is above the 95% confidence level, with dark (light) shading for positive (negative) 
correlation. 


Working Group on Coupled Modeling (WGCM) and 
their Coupled Model Intercomparison Project (CMIP) 
and Climate Simulation Panel for organizing the model 
data analysis activity, and the IPCC WG1 TSU for 
technical support. The IPCC Data Archive at Lawrence 
Livermore National Laboratory is supported by the 
Office of Science, U.S. Department of Energy. J. L. Lin 


was supported by the NASA MAP Program and NSF 
Grant ATM-0745872. T. Shinoda was supported by NSF 
Grants OCE-0453046 and ATM-0745897, the NOAA 
CPO/CVP program, and the 6.1 project Global Remote 
Littoral Forcing via Deep Water Pathways sponsored by 
the Office of Naval Research (ONR) under Program 
Element 601153N. 


























Lotitude (deg) Lotitude (deg) Latitude (deg) Latitude (deg) 


3112 


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(i) Obs(GPCP) 40day Nov-Apr 



(j) MIROC — hires Nov—Apr 


60 120 180 240 300 360 

Longitude (deg) 


(k) MRI Nov-Apr 



(I) CGCM Nov-Apr 



(m) MPI Nov-Apr 


(n) IPSL Nov-Apr 





CNRM Nov-Apr 


60 120 180 240 300 360 

Longitude (deg) 


0 60 120 180 240 300 360 

Longitude (deg) 


Fig. 12. ( Continued ) 






















Variance (mm/day) 


1 June 2010 


LIN ET AL. 


3113 


6 

5 

4 

3 

2 

1 

0 

20 30 40 50 60 

Latitude (deg) 

Fig. 13. As in Fig. 5, but for the variance of the 22-day mode. 


Obs(GPCP) 

- CCSM3 

- MlROC-medres 

- MPI 


- - PCM 

— - MIROC-hires 

- - IP5L 

GFDL2.0 

- GISS-AOM 

- MRl 

- CNRM 

GFDL2.1 

- -GISS-ER 

- - CGCM 

- -CSIRO 








Log (days) Lag (days) Lag (days) Lag (days) 


3114 


JOURNAL OF CLIMATE 


Volume 23 



0 10 20 30 40 50 60 



0 10 20 30 40 50 60 


Latitude (deg) 


Latitude (deg) 



-10 



Latitude (deg) 


Latitude (deg) 


-10 [ 
-5 
0 
5 
10 


(e) 



Latitude (deg) Latitude (deg) 

Fig. 14. Lag-correlation of the 22-day-mode precipitation anomaly averaged between 125° 
and 115°W with respect to itself at 37.5°N, 120°W for the 14 models. Shading denotes the 
regions where lag-correlation is above the 95% confidence level. 




































Lag (days) Lag (days) Log (days) Lag (days) 


1 June 2010 


LIN ET AL. 


3115 





0 10 20 30 40 50 60 

Latitude (deg) 


0 10 20 30 40 50 60 

Latitude (deg) 


P Obs(GPCP) Nov-Apr 


0 10 20 30 40 50 

Latitude (deg) 



0 10 20 30 40 50 60 

Latitude (deg) 


(j) P MIROC-hires Nov-Apr 



0 10 20 30 40 50 60 

Latitude (deg) 


(I) P CGCM Nov-Apr 



0 10 20 30 40 50 60 

Latitude (deg) 


(n) P IPSL Nov-Apr 



0 10 20 30 40 50 60 

Latitude (deg) 


(p) P CSIRO Nov-Apr 



0 10 20 30 40 50 60 

Latitude (deg) 


Fig. 14. ( Continued) 




















































Latitude (deg) Latitude (deg) Latitude (deg) Latitude (deg) 


3116 


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Volume 23 


(a) Obs(GPCP) 22day Nov-Apr 


(b) GFDL2.0 Nov-Apr 




(d) CCSM3 Nov-Apr 



60 120 180 240 300 360 

Longitude (deg) 


0 60 120 180 240 300 360 

Longitude (deg) 


GISS-AOM Nov-Apr 



0 60 120 180 240 300 360 

Longitude (deg) 



0 60 120 180 240 300 360 

Longitude (deg) 


(h) MIROC-medres Nov-Apr 



FIG. 15. As in Fig. 12, but for the 22-day mode. 





































Latitude (deg) Latitude (deg) Latitude (deg) Latitude (deg) 


1 June 2010 


LIN ET AL. 


3117 


(i) Obs(GPCP) 22doy Nov-Apr 



(j) MIROC-hires Nov-Apr 



0 60 120 180 240 300 360 

Longitude (deg) 

(k) MRI Nov—Apr 



0 60 120 180 240 300 360 

Longitude (deg) 


(I) CGCM Nov-Apr 



60 120 180 240 300 360 

Longitude (deg) 


(m) MPI Nov-Apr 


0 60 120 180 240 300 360 

Longitude (deg) 


(n) IPSL Nov-Apr 





0 60 120 180 240 300 360 

Longitude (deg) 


0 60 120 180 240 300 360 

Longitude (deg) 


Fig. 15. (Continued) 


























3118 


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