Impact of soil moisture on interannual variability ofboreal summer precipitation
Shinjiro Kanae13, Yukiko Hirabayashi2, Tomohito Yamada3, Taikan Oki3
(1: Research Institute for Humanity and Nature, 2: Yamanashi University, 3: IIS, University of Tokyo)
Shinjiro Kanae, Research Institute for Humanity and Nature, 335 Takashima-cho, Kamigyo-ku, Kyoto 602-0878,
Japan.

e-mail: kanae@chikyu.ac.jp, kanae@iis.u-tokyo.ac.jp
Abstract:

A set of AGCM hindcast simulations for 1951-98 was carried out to investigate the influence of soil moisture on
interannual variability of boreal summer(June, July, August) precipitation. It was achieved by the comparison of two

simulations: a simulation forced with observed SST and a simulation forced with "realistic" land surface hydrologicalconditions, as well as observed SST, which were produced in an offline land surface simulation for the correspondingyears. The influence of the "realistic" land surface specification is evident in the successful hindcast of interannual
variability of boreal summer precipitation over certain regions of the world: Sahel, central Asia, the north of India, midto western part of North America, the middle of Australia and so on, identified as “semi-arid regions.” This implies thatpredictability of boreal summer precipitation appears over the “semi-arid regions” if adequate land surface hydrologicalconditions are provided. However, it is also recognized that it is mostly only above the “semi-arid regions” (and tropicaloceans) where the forcing from underlying surface has large variability that the interannual variability of precipitation isreproducible in AGCM simulations with the same land and ocean boundary conditions. There are insignificant
reproducibility of precipitation variability, and insignificant predictability accordingly, in many other parts of the world.
In some regions like the tropical Asian monsoon region, on the other hand, successful hindcast is not apparent despite
the existence of significant reproducibility. These issues should be further investigated for the improvement ofdynamical seasonal prediction of precipitation.
Keywords: soil moisture, AGCM, land surface-atmosphere interaction, predictability

1.Introduction
Because long-term, global-scale observation of soilhydrological conditions into AGCM simulations as aboundarycondition.

Evaluation

ofsimulated

moisture is not available so far, statistical analysis based

precipitation variability will provide us an opportunity toon observation has a limitation in clarifying the land

examine a potential land impact on seasonal precipitationinfluence on precipitation. Thus, AGCMs have been aprediction, from the viewpoint of representation ofnecessary tool for the investigation of such land impacts

interannual variability. The "realistic" land surface

on precipitation. In the previous studies dealing with thehydrological conditions for 48 years in this study,land impact on precipitation, soil moisture and snow

documented in Hirabayashi et al. (2004) (hereafter,were mostly calculated in the coupled land-atmosphereH2004) in detail, were developed in an offline global

system of an AGCM where their characteristics are notsimulation of a land surface model driven by observed ornecessarily consistent with the characteristics of the real

analyzed atmospheric forcing data. This study seems anland surface hydrological states of the globe. Here, as ananalogue to a study dealing with the impact of El Nino

extension of the previous studies, we newly demonstrateon precipitation that can be conducted by prescribing thethe impact of soil moisture on precipitation in terms ofobserved sea surface temperature (SST) fields for many

interannual variability. It was realized by incorporating ayears as a boundary condition in an AGCM. A full paper

long-term (48 years) dataset of "realistic" land surface(Kanae et al. 2004) is now under review.

2. Design of experiments
We carried out a set of numerical simulations with anAGCM for 1951-98 forced with different boundary
conditions. The global spectral AGCM used in this study
has been jointly developed by Center for Climate System
Research, the University of Tokyo and National Institutefor Environmental Studies, Japan. The employed model
resolution

ishorizontally

T42(correspondingtoapproximately 2.8 degree) with 20 vertical levels. The

Figure2: Correlation coefficient of precipitation

land surface part of the AGCM, MATSIRO, is a SiB2

between LND1 and observation

type land surface scheme. Observed monthly SST from1951 to 1998 was specified as a boundary condition in

We focus on seasonal mean precipitation amount ofJune, July and August (JJA). The correlation coefficient

the simulations.
The first simulation is the control experiment

between simulated JJA precipitation of CTRL and(hereafter, CTRL) in which only the observed SST wasobserved JJA precipitation of CRU calculated from theforced as a lower boundary condition. There was nointerannual variability for 1951-98 shows almost

specification of land surface hydrological conditions.

insignificant over land globally. Major exceptions are theThe coupled land-atmosphere system evolved freely ineast coast of Brazil, the Indonesia maritime continent,CTRL. Another simulation (hereafter, LND1) was forced

the west coast of Sahel and Central America where theirwith the “realistic” land surface hydrological conditions

interannual variability seems to be controled by tropicalfrom 1951 to 1998 as well as the SST field of theSST variability. On the other hand the correlation

corresponding month and year. The “realistic” land

between LND1 and CRU clearly shows the areas wheresurface hydrological conditions are the products of anthe “realistic” land surface hydrological conditions haveoffline land surface simulation (H2004) using MATSIRO,a positive impact in simulating interannual variability ofthe same land surface model attached to the AGCM. The

boreal summer precipitation. Significant impacts aredetail is described in H2004.

evident in the entire Sahel, central Asia, the north ofIndia, the middle of Australia, and the mid to westernpart of North America. It can be argued that a successful

3. Results: Regions wherehydrological conditions affectlandsurface

hindcast of precipitation variability would be realized
over these land areas if adequate land surfacehydrological conditions are provided. These areas
considerably

correspondtotheareasofstrongland-atmosphere coupling in AGCMs (Koster et al.
2004) despite the differences in AGCMs, experimental
design and the target time scale of precipitation. These
areas are considered to be mostly located in the zones
between dry and wet climates where soil moisture isenough dry to effectively control surface latent andsensible heat fluxes and the atmosphere is enough wet
favorable to precipitation (Koster et al. 2004). Thereby,
we call the areas “semi-arid regions” in the following.
Figure1: Correlation coefficient of precipitation between

Further validity of the name will be described later. ForCTRL and observation

the robustness of the above result, we should note aminor change in the significance level (for example, 5%to 1% or 5% to 10%) does not change the majorimpressions.

with the specification of realistic land conditions andNext, in order to show reproducibility, we carried out
the same AGCM simulation as LND1 only except for

SST fields.
Whenwelookback

again

attheusing a different initial condition of the atmospheresuccessfully-hindcasted areas, there also remains an(hereafter, LND2). The correlation coefficient between

important issue. It is indicated that precipitation

LND1

interannual

variability is well hindcasted mostly just above such

precipitation variability of such areas is significantly

semi-arid land areas (and tropical oceans). The

reproducible when land surface hydrological conditions

assumption that the influence of land is predominantly

as well as SST fields are specified, although an ensemble

local is no more an assumption. The current AGCMs

of more numbers of simulations is helpful for moreseem to be only capable of simulating predominantly

adequate expression of reproducibility. The distribution

local impact of land (and ocean) on precipitation whereof areas with reproducibility resembles that of areas withthe forcing from underlying surface is evident. We needsuccessful hindcast of precipitation variability); forto further investigate whether this characteristic ofexample, the “semi-arid regions,” the equatorial Pacific,

AGCM is appropriate for representing the climate

the equatorial Atlantic and so on. Predictability can bevariability of the earth, because it is crucial for therepresented as a good-correlation with observation

improvement of the predictability of other regions.

and

LND2

denotesthat

thelocated inside the areas with reproducibility. The areas

Currently, the phase 2 of GSWP has been underwith reproducibility, broader than the areas withprogress,

predictability,hydrological variables are calculated using a dozen ofcanbeconsideredaspotentiallypredictable areas.

and10-year(1986-95)

landsurfaceland surface models in a highly controlled manner. Eachmodel can be basically attached to each AGCM. Thus,

4. Discussion

utilizing the outputs of GSWP2 will enable us to realize

Predictability has been exhibited only in thea multi-AGCM analysis.

“semi-arid regions” where “water” in the land surface
regulates the surface fluxes. Is predictability hopeless

References

over other land areas outside the “semi-arid regions”?

Hirabayashi, Y., S. Kanae, T. Oki, 2004: A 100-year globalFor

theappearanceofpredictability,

certain

reproducibility should be secured firstly. Figure 4
indicates that, except for the “semi-arid regions,” evenretrospective estimation of terrestrial water cycle. J. Geophys.
Res., submitted.
Kanae, S., Y. Hirabayashi, T. Yamada, T. Oki: Influence ofreproducibility does not exist in most of mid to highland-surface

latitude even when both the land and ocean conditions

variability of precipitation in boreal summer, J.Climate,

are specified as boundary conditions. This seems not tosubmittedhydrologicconditions

oninter-annual

be unique to our study. It has been pointed out using theKoster, R.D., M.J. Suarez, M. Heiser, 2000: Variance andcurrent AGCMs that monthly to seasonal precipitation

predictability of precipitation at seasonal-to-interannual

variability in mid to high latitude, opposite to that in thetimescales. J. Hydrometeor., 1, 26-46.

tropics, is controlled mostly by chaotic atmospheric

Koster, R.D., P.A. Dirmeyer, Z. Guo, G. Bonan, E. Chan, P. Cox,

dynamics (e.g. Shukla 1998; Koster et al. 2000). While

C.T. Gordon, S. Kanae, E. Kowalczyk, D. Lawrence, P. Liu,

this chaotic nature of the atmosphere seems to inhibit

C.H. Lu, S. Malyshev, B. McAvaney, K. Mitchell, D. Mocko,

reproducibility, it has been speculated that the land

T. Oki, K. Oleson, A. Pitman, Y.C. Sud, C.M. Taylor, D.

surface hydrological conditions may strongly modulate

Verseghy, R. Vasic, Y. Xue, T. Yamada, 2004: Regions ofthe phase and amplitude of a major mode of climate

coupling between soil moisture and precipitation. Science,variability in northern latitude and that the interannual305, 1138-1140.

variability of precipitation at many locations in Monsoon

Shukla, J., 1998: Predictability in the midst of chaos: A

Asia is correlated, at least, with the equatorial Pacificscientific basis for climate forecasting. Science, 282,

SST variability. It suggests the existence of certain

728-731.

predictability of precipitation variability in these regions