Diagnostic Studies of ECHAM GCMs
by
Ji-yong Wang
Submitted to the Department of Earth, Atmospheric, and Planetary
Sciences in partial fulfillment of the requirements for the degree of
Master of Science in Meteorology
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
September, 1998
@ Massachusetts Institute of Technology, 1998. All Rights Reserved.
Au th or ...........................
...............
...................................................
Department of Earth, At ospheric and Planetary Sciences
August 3, 1998
Certified by ..................................
Peter H. Stone
Professor
DepartmepmLatthLAtmospheric, and Planetary Sciences
A ccepted by ...........................................................................................
Ronald Prinn
Department Head
Department of Earth, Atmospheric, and Planetary Sciences
MASSACHUSETTS INSTITUTE
FTECHNOLOGY
li9
Diagnostic Studies of ECHAM GCMs
by
Ji-yong Wang
Submitted to the Department of Earth, Atmospheric, and Planetary Sciences
on August 3, 1998, in partial fulfillment of the requirements for the degree of
Master of Science in Meteorology
Abstract
The two latest generations of MPI ECHAM AGCMs, ECHAM3 and ECHAM4, have been
performed the test run in relatively high resolution (T106) with the prescribed AMI
boundary conditions. There are major changes made in ECAHM4 T106 from ECHAM3
T106 in the radiation scheme, the treatment of radiation absorption by water vapor and
cloud water and the calculation methods of advection of water vapor and cloud water, etc.
It is shown that the simulation of annual mean heat balance at ocean surface has been
greatly improved in ECHAM4 T106 with respect to the GEBA observations.
While the annual mean state is important to the global climate system's heat and water
balance, the distribution of global heat and water divergence determines the energy and
water mass transport in the atmosphere and between the ocean and atmosphere. The diagnostic studies of ECHAM AGCMs' implied oceanic meridional heat transport and their
comparison with available observational data, and the break-down of model annual surface heat balance terms highlight the importance of model's treatment in radiation absorption of atmospheric water vapor, the cloud radiation forcing calculation and latent heat
contribution from hydrological balance requirement.
The ECHAM GCMs' response with the ocean-atmosphere boundary condition is also
investigated with MIT's ECHAM4 T42 datasets, which are obtained with the two different
boundary conditions. The ECHAM4 model is capable in simulating the annual mean
implied oceanic meridional heat transport with fare accuracy in T42 resolution, while the
interannual variation is clearly shown.
Thesis Supervisor: Peter H. Stone
Title: Professor
Contents
List of Tables ........................................................................................................
3
List of Figures ......................................................................................................
4
A cknow ledgm ent ...........................................................................................
9
1 ntroduct1 n ......................................................................................................
10
1.1 Background .................................................................................................
10
1.2 M otivation ....................................................................................................
13
1.3 Thesis Structure .........................................................................................
15
2 M odel D escription and D ata Sets ................................................
16
2.1 Model History and Description ...............................................................
16
2.2 Data Set .........................................................................................................
20
3 O ceanic H eat Transport C alculation .........................................
21
3.1 M ethodology ...............................................................................................
21
3.2 Correction Scheme ....................................................................................
24
3.3 Heat Fluxes at Ocean Surface .................................................................
25
3.4 M eridional Northward Oceanic Heat Transport ................................
3.4.1 Results ..................................................................................................
3.4.2 Discussion .............................................................................................
31
. . 32
36
3.5 Oceanic Heat Transport of Other Boundary Condition ...................
43
3.5.1 Ocean Surface Heat Fluxes ....................................................................
3.5.2 Oceanic Heat Transport .......................................................................
44
46
4 Surface H ydrological B alance ......................................................
48
4.1 Precipitable Water .....................................................................................
48
4.2 Precipitation ...............................................................................................
50
4.3 Evaporation ..................................................................................................
51
4.4 Band Mean E-P ..........................................................................................
52
4.5 River Runoff ...............................................................................................
56
4.6 Atlantic Ocean Freshwater Fluxes .......................................................
57
5 Sum m ary ............................................................................................................
59
6 R eference ...........................................................................................................
63
A p p e n d ix ................................................................................................................
67
Oceanic Heat Transport
by Ocean Surface Heat Flux Components ..............................................
67
List of Tables
Table 1 Information for ECHAM3 T42 and ECHAM4 T42 GCMs ....................
18
Table 2 Comparison of total oceanic heat transport calculation .........................
40
Table 3 a Comparison of various estimates for Atlantic
O cean heat transport ........................................................................
42
Table 3 b Comparison of various estimates for Pacific
Ocean heat transport ........................................................................
42
Table 4 a Band mean hydrological balance for Land ..........................................
53
Table 4 b Band mean hydrological balance for Ocean ........................................
54
Table 4 c Band mean hydrological balance for Global ......................................
55
Table 5 Comparison of River runoff calculations ................................................
102
Table 6 a Atlantic Freshwater Fluxes for ECHAM3 T106 ...................................
103
Table 6 b Atlantic Freshwater Fluxes for ECHAM4 T106 ...................................
104
Table 6 c Comparison of calculation of Atlantic freshwater fluxes ......................
105
OWMNIMMMUM
List of Figures
Figure 2.1 Comparison of simulations of the surface heat balance by
ECHAM GCMs together with various atmospheric GCMs
and GEBA Observation ......................................................................
19
Figure 3.1 Schematic of heat fluxes in the atmosphere-ocean system ..................
22
Figure 3.2 Annual mean net radiation flux at ocean surface for ECHAM3
and ECHAM 4 T106 ..........................................................................
69
Figure 3.3 Annual mean zonal mean net radiation flux at ocean surface
for ECHAM3 and ECHAM4 T106 ....................................................
70
Figure 3.4 Difference of net radiation flux at ocean surface between
ECHAM3 and ECHAM4 T106, for annual mean and zonal
mean respectively ...............................................................................
71
Figure 3.5 Annual mean absorbed short-wave radiation at ocean surface
for ECHAM3 and ECHAM4 T106 ...................................................
72
Figure 3.6 Annual mean zonal mean absorbed short-wave radiation flux
at ocean surface for ECHAM3 and ECHAM4 T106 .........................
73
Figure 3.7 Difference of absorbed short-wave radiation flux at ocean
surface between ECHAM3 and ECHAM4 T106, for annual
m ean and zonal mean ........................................................................
74
Figure 3.8 Annual mean downward longwave radiation flux at ocean
surface for ECHAM3 and ECHAM4 T106 ........................................
75
Figure 3.9 Annual mean zonal mean downward longwave radiation flux
at ocean surface for ECHAM3 and ECHAM4 T106 .........................
76
Figure 3.10 Difference of downward long-wave radiation flux at ocean
surface between ECHAM3 and ECHAM4 T106, for annual
mean and zonal mean ......................................................................
77
Figure 3.11 Annual mean global planetary albedo for ECHAM3 and
EC HAM4 T 106 .................................................................................
78
Figure 3.12 Annual mean zonal mean planetary albedo for ECHAM3 and
ECH A M 4 T 106 .................................................................................
79
Figure 3.13 Difference of global planetary albedo between ECHAM3 and
ECHAM4 T106, for annual mean and zonal mean ...........................
80
Figure 3.14 Annual mean total cloud cover over ocean for ECHAM3 and
EC H AM 4 T 106 .................................................................................
81
Figure 3.15 Annual mean zonal mean total cloud cover over ocean for
ECHAM 3 and ECHAM4 T106 ........................................................
82
Figure 3.16 Difference of total cloud cover over ocean between ECHAM3
and ECHAM4 T106, for annual mean and zonal mean .....................
83
Figure 3.17 ISCCP-C2 8 year mean total cloud amount observation .....................
84
Figure 3.18 Annual mean latent heat flux at ocean surface for ECHAM3
and ECH A M4 T 106 ..........................................................................
85
Figure 3.19 Annual mean zonal mean latent heat flux at ocean surface for
ECHAM 3 nd ECHAM4 T106 ..........................................................
86
Figure 3.20 Difference of latent heat flux at ocean surface between ECHAM3
and ECHAM4 T106, for annual mean and zonal mean .....................
87
Figure 3.21 Annual net heat flux at ocean surface for ECHAM3 and ECHAM4
T 106 model ........................................................................................
88
Figure 3.22 Annual mean zonal mean net heat flux at ocean surface for
ECHAM 3 and ECHAM4 T106 ........................................................
89
Figure 3.23 Difference of net heat flux at ocean surface between ECHAM3
and ECHAM4 T106, for annual mean and zonal mean ....................
90
Figure 3.24 Annual mean zonal mean meridional total oceanic heat transport
for ECHAM3 and ECHAM4 T106 .................................................
33
Figure 3.25 The schematic of ocean basins ..........................................................
35
Figure 3.26 Annual mean zonal mean meridional heat transport in
Atlantic Ocean and in Indo-Pacific Ocean for ECHAM3
and ECH A M4 T 106 ..........................................................................
35
Figure 3.27 The difference of implied total oceanic heat transport between
ECHAM 3 and ECHAM4 T106 ........................................................
37
Figure 3.28 Annual mean total northward atmospheric meridional heat
transport simulated by ECHAM3 and ECHAM4 T106 ....................
37
Figure 3.29 The "hybrid" total oceanic heat transport for ECHAM3 and
ECHAM 4 T106 models ...................................................................
39
Figure 3.30 25 year mean net radiation flux at ocean surface for ECHAM4
Gisst T42 model, both annual mean and zonal mean .......................
91
Figure 3.31 25 year mean absorbed shortwave radiation flux at ocean
surface for ECHAM4 Gisst T42, for both annual mean and
zonal mean ........................................................................................
92
Figure 3.32 25 year mean downward longwave radiation flux at ocean
surface for ECHAM4 Gisst T42, for both annual mean and
zonal mean ........................................................................................
93
Figure 3.33 25 year mean planetary albedo for ECHAM4 Gisst T42, for
both annual mean and zonal mean ....................................................
94
Figure 3.34 25 year mean total cloud cover for ECHAM4 Gisst T42, for
both annual mean and zonal mean ....................................................
95
Figure 3.35 25 year mean latent heat flux at ocean surface for ECHAM4
Gisst T42, for both annual mean and zonal mean .............................
96
Figure 3.36 25 year mean net heat flux at ocean surface for ECHAM4 Gisst
T42, for both annual mean and zonal mean ......................................
97
Figure 3.37 Comparison of simulation of the global mean shortwave
radiation flux absorbed at the surface (in (a)), and the
downward longwave radiation flux at surface (in (b)) among
AGCM s. Referring to Figure 2.1 .....................................................
98
Figure 3.38 Annual mean meridional northward total oceanic heat transport
for ECHAM4 Gisst T42 and ECHAM4 T106 .................................
99
Figure 3.39 25 year mean northward total atmospheric heat transport
for ECH AM 4 G isst T42 .....................................................................
99
Figure 3.40 Annual mean total heat transport at the top of the atmosphere
for ECHAM4 Gisst T42, T106 and ERBE observation ....................
100
Figure 3.41 Annual mean "Hybrid" oceanic heat transport for ECHAM4
Gisst T42 calculated from EABE observation ..................................
100
Figure 3.42 25 year mean meridional northward oceanic heat transport for
ocean basins, ECHAM 4 Gisst T42 ....................................................
101
Figure 4.1 Annual mean precipitable water vapor for ECHAM3 T106 .................
106
Figure 4.2 Annual mean precipitable water vapor for ECHAM4 T106 .................
106
Figure 4.3 Annual mean zonal mean precipitable water for both ECHAM3
and ECH AM 4 T 106 ..............................................................................
107
Figure 4.4 NVAP analysis of annual mean precipitable water, and zonal mean .... 107
Figure 4.5 Annual mean total precipitation for ECHAM3 T106 ...........................
108
Figure 4.6 Annual mean total precipitation for ECHAM4 T106 ...........................
108
Figure 4.7 Annual mean zonal mean total precipitation for both ECHAM3
and EC HA M4 T 106 .............................................................................
109
Figure 4.8 Annual mean total precipitation by Global Precipitation Climatology
Project (GPCP) .....................................................................................
109
Figure 4.9 Annual mean evaporation for ECHAM3 T106 .....................................
110
Figure 4.10 Annual mean evaporation for ECHA T106 ........................................
110
Figure 4.11 Annual mean global moisture flux for ECHAM3 T106 .....................
111
Figure 4.12 Annual mean global moisture flux for ECHAM4 T106 .....................
111
Figure 4.13 Annual mean zonal mean moisture flux for ECHAM3 T106
and ECH A M 4 T 106 .............................................................................
112
Figure 4.14 Annual mean moisture transport for ECHAM3 T106,
ECHAM4 T106 and for Baumgartner & Reichel ..................................
112
Figure A. 1 Difference of total heat transport in Atlantic Ocean and
Indo-Pacific Ocean for ECHAM3 and ECHAM4 T106 ........................
113
Figure A.2 Heat transport carried by ocean surface net shortwave radiation in
ECHAM3 and ECHAM4 T106 Atlantic, with their difference ............. 114
Figure A.3 Heat transport carried by ocean surface net shortwave radiation in
ECHAM3 and ECHAM4 T106 Indo-Pacific, with their difference ...... 115
Figure A.4 Heat transport carried by ocean surface downward longwave
radiation in ECHAM3 and ECHAM4 T106 Atlantic,
with their difference ..............................................................................
116
Figure A.5 Heat transport carried by ocean surface downward longwave
radiation in ECHAM3 and ECHAM4 T106 Indo-Pacific,
with their difference ..............................................................................
117
Figure A.6 Heat transport carried by ocean surface net latent heat flux in
ECHAM3 and ECHAM4 T106 Atlantic, with their difference ............
118
Figure A.7 Heat transport carried by ocean surface laten heat flux in ECHAM3
and ECHAM4 T106 Indo-Pacific, with their difference .......................
119
Acknowledgment
I would like to thank my advisor, Dr. Peter Stone, for suggesting this study, for all the time
and energy he has devoted. Special thanks to him for his patience.
Chapter 1
Introduction
1.1 Background
General Circulation Climate Models (GCMs) are believed to have the potential to simulate
the global scale dynamic and thermodynamic processes, and to calculate explicitly the
large-scale interactions such as exchanges of mass, momentum and energy across the
interfaces of the earth's climate subsystem. In view of the GCM's high capacity to simulate the climate under a great variety of boundary conditions, the model constitutes a powerful tool for climate research studies. Many of the major operational weather centers
around the world have been developing and using their GCMs in an attempt to explain the
causes of climate variability, to obtain a better understanding of the observed climate
changes, and to predict the possible climate state in the future.
The core of all numerical models is a set of equations expressing the general physical principles of conservation of mass, momentum and energy, and the chemical laws that govern
the composition of the components of the climate system. These three-dimensional equations are nonlinear and each variable in the equations is interrelated to the others. Any
change in one of these variables may induce variations in others and in turn generate a
feedback in the original variable. Normally, in passing from these governing equations to
the working climate model several steps are followed: (1) the discretization and choice of
resolution, (2) the parametrization of physics, (3) the integration in time, (4) the verification, (5) the model improvement. Each of these steps has its characteristic difficulties and
thus may introduce errors and uncertainties that compromise the model simulated climate
to a greater or lesser extent.
While the numerical methods and resolutions used in the GCMs to solve the equations are
relatively standard and well known, the same cannot be said for the parameterizations of a
wide range of physical processes which occur on scales too small to be resolved by the
models. These physical processes are referred to as subgrid-scale processes including, for
instance, the occurrence of convection, cloudiness and precipitation. The subgrid-scale
processes must be parameterized in terms of variables which are resolved by GCMs, and
in principle it is this parameterization which makes GCMs different from each other. It
may also account for some of the models' successes as well as some of their failures. For
example, the study of GCMs' performance and limitations by Stone and Risbey (1990)
"point[ed] out that the meridional transports of heat simulated by GCMs used in climate
change experiments differ[ed] from observational analysis and from other GCMs by as
much as a factor of two." In addition, they "demonstrate that GCM simulations of the large
scale transports of heat are sensitive to the (uncertain) subgrid scale parameterizations."
Besides, the earth's climate system is composed of the atmosphere, hydrosphere, cryosphere, land surface and biosphere and their interactions. These subsystems have very different response time scales and their interactions are not fully understood, but are known
to be complex and to produce feedbacks. So, the solutions of the climate governing equations involve a great deal of computation, starting from some initialized state and investigating the effects of changes in a particular component of the climate system. Boundary
conditions, for example the solar radiation, SSTs or vegetation distribution, are set from
observational data or other simulations. These data are rarely complete or of adequate
accuracy to specify completely the environmental conditions, so that there is inherent
uncertainty in the simulation results. On the other hand, all the uncertainties inherent in
GCMs leave room for model improvement.
With the complexity and difficulties in simulating the climate system mentioned above,
among many other reasons, together with the consideration of available computer
resources, historically many GCM communities start with a component GCM such as
Atmospheric GCM (AGCM) and Ocean GCM (OGCM) which may be coupled with other
components in the future to simulate the whole climate system.
The ability of current atmospheric models to simulate the observed climate varies with
scale and variable. Given the correct sea surface temperature (SST), most models simulate
the observed large-scale climate with skill, and give a useful indication of some of the
observed regional interannual climate variations or trends, as well as the characteristic
inabilities of a particular model. The simulation of clouds and their radiative effects
remains a major area of difficulty, and AGCMs generally are not expected to simulate the
local climate with fair accuracy (Risbey and Stone, 1996). The evaluation of the climate
simulation capability of component Ocean GCMs (OGCMs) shows that they realistically
portray the large-scale structure of the ocean gyres and the gross features of the thermohaline circulation. OGCM's major deficiencies are the representation of mixing processes,
the structure and strength of western boundary currents, the simulation of the meridional
heat transport, and the portrayal of convection and subduction.
AGCMs are developed by using specified lower boundary conditions (SSTs, sea-ice
extent, etc.), and OGCMs are integrated with specified surface wind stresses, and/or relaxation of surface temperature and salinity to climatological values. These models' behaviors are strongly constrained by the prescribed boundary conditions. Upon coupling, with
the removal of the constraint of boundary conditions, the coupled GCMs are known to
have a dramatic climate drift of its ocean-atmospheric system. Currently, most coupled
models use "flux adjustment" whereby the heat and fresh water fluxes (and possibly the
surface stresses) are modified before being imposed on the ocean by the addition of a "cor-
rection" or "adjustment.
It is expected that this conflict between the use of a fully physically based model and the
use of a non-physical flux adjustment will be reduced as models are further improved.
Some modelers even draw an encouraging analogy with the early development of numerical weather prediction when ad hoc corrections were used to improve forecasts. Later
improvements in model formulation and initialization techniques made such corrections
unnecessary.
The purpose of model improvement and ultimately the understanding and predictive ability of climate may be achieved by the systematic model intercomparison and evaluation
which can provide valuable information for model improvement with the in-depth diagnosis and interpretation of model results. (e.g., Atmospheric Model Intercomparison Project;
Gates, 1992).
1.2 Motivation
According to "Climate Change 1995, The Science of Climate Change" (IPCC, 1995), "the
major areas of uncertainty in climate models concern clouds and their radiative effects, the
hydrological balance over land surfaces and the heat flux at the ocean surface", and "the
comprehensive diagnosis and evaluation of both component and coupled models are
essential parts of model development, although the lack of observations and data sets is a
limiting factor."
The heat flux at the ocean surface can be used to calculate the mean state of the implied
meridional northward oceanic heat transport with the assumption that there is no heat
transport across the sea shore. The discrepancy in the simulation of the ocean surface heat
flux among the AGCMs would be exaggerated in the calculation of the oceanic heat transport. Further, the estimate of implied oceanic heat transport is also an important index of
an AGCM's simulation abilities.
Studies indicate that there are great discrepancies in the estimate of annual mean total oceanic heat transport by using various GCM models (this occurs even in the observational
estimates). Few studies have further investigated the roles taken by the three major ocean
basins: Atlantic Ocean, Pacific Ocean and Indian Ocean in contributing to the discrepancy.
We will carry out the calculation for the Atlantic Ocean and Indo-Pacific Ocean based on a
conceptual division of the total ocean, and seek a clearer picture of how the two major
ocean basins contribute to the total oceanic heat transport.
The surface energy budget is tightly related to the surface water budget, which is another
major area of uncertainty in climate models especially over land surfaces; thus we are also
motivated to carry out diagnostic studies of both ECHAM models's hydrological balance
and compare with available data such as surface moisture flux, precipitation and evaporation, etc.
Since the dataset we use is from two model runs, that is, the 10 year means for ECHAM3
T106 and ECHAM4 T106, the intercomparison between these two models and the comparison with other models and available observations will provide the useful information
for model improvement. Thus, it will give us a clue of model behavior requiring modification, and portray possible reasons for model failure.
Another calculation we carry out in this study is how much of the oceanic heat transport is
explained (or carried) by the surface heat flux components. We would like to investigate,
for example, how much heat would be transferred meridionally by the latent heat flux
component alone. We should expect these flux components to act differently in the total
oceanic heat transport, and in the ocean basins' heat transport as well.
1.3 Thesis Structure
This section provides a brief overview of the thesis structure and organization. The thesis
itself is divided into six chapters:
-
Chapter 1 introduces the background of the thesis work, the motivation of the study,
along with this thesis structure introduction.
-
Chapter 2 describes the model history, structure and the improvements implemented in
the two generations of models studied in the thesis. The description of datasets is
included.
-
The calculation for the model implied oceanic heat transport will be presented in
Chapter 3. There are five major sections -
the methodology, the correction schemes,
the annual mean of ocean surface heat fluxes, the model implied meridional oceanic
heat transport (for ocean total, and ocean basins), the oceanic heat transport with other
boundary conditions and horizontal resolution, and the oceanic heat transport carried
by the different heat flux components.
-
The model's simulation of surface hydrological balance and related diagnosis would
form Chapter 4. There are six sections in the chapter.
-
Chapter 5 will summarize the studies performed. Important conclusions will be drawn.
-
Chapter 6 is the major reference section.
-
Appendix includes the calculation of the meridional northward oceanic heat transport
carried by the different heat flux components, and the discussion on their roles in
transferring the heat for the energy balance of the climate system.
Chapter 2
Model Description and Data Sets
2.1 Model History and Description
The ECHAM atmospheric general circulation models are a series of models developed at
MPI. The earliest version, ECHAMO, evolved from the cycle 17 version (operational in
1985) of the numerical weather prediction model at the European Center for MediumRange Weather Forecasts (ECMWF). Since ECHAMO, the models developed at MPI are
the second generation ECHAMI and ECHAM2, the third generation ECHAM3 and the
fourth ECHAM4. The models we are using in this diagnostic study are ECHAM3 and
ECHAM4 T106 AGCMs. They have a horizontal spectral resolution with a 320 (longitude) x 160 (latitudes) Gaussian transform grid mesh and 19 vertical levels. The vertical
coordinate used in ECHAM3 and ECHAM4 is a hybrid
-p-coordinate system, with
smooth transition from G-coordinates at the surface to p-coordinates at the top of the
atmosphere.
The standard configuration of ECHAM3 and ECHAM4 GCMs are ECHAM3 T42 and
ECHAM4 T42 respectively, whose horizontal resolution corresponds to a 128 (longitude)
x 64(latitude) Gaussian transform grid. T106 are their test versions. The model formulation and parameterizations are identical for T42 and T106 versions for both ECHAM3 and
ECHAM4 except for two things: 1) the horizontal diffusion coefficients were made resolu-
tion dependent such that the slope of the spectral kinetic energy comes closer to observations; 2) a rain efficiency parameter in the cloud scheme was made resolution dependent,
which influences the cloud lifetime and thereby the associated planetary albedo, in order
to match the global annual mean top of atmosphere radiative fluxes with the Earth Radiation Budget Experiment (ERBE) satellite observations.
The formal documents for ECHAM3 T42 and ECHAM4 T42 have been published by
MPI. In table 1, we outline the model configurations. More details can be found in the
Max-Planck-Instutut fir Meteorologie Report No. 93 (Roeckner et al 1992) and Report
No. 218 (Roeckner et al 1996).
As the immediate preceder of ECHAM4, ECHAM3 T42 has been tested in many studies
(e.g. Gleckler et al, 1995; Gaffen et al., 1996). It is also the AMIP (Atmospheric Model
Intercomparison Project) (Gates, 1992) MPI baseline model. The model has generally performed outstandingly in the studies (Lau et al. 1996).
Most of the structure and parameterizations of ECHAM3 T42 have been carried forward
to ECHAM4 T42, while they differ most sharply in the treatment of transport and diffusion, of chemistry and radiation, and of the planetary boundary layer (PBL). The parameterization of convection, cloud formation and surface characteristics have also been
modified. The author would like to highlight here the three improvements made in
ECHAM4: 1) The advection of water vapor, cloud water and chemical species are calculated by the shape-preserving semi-Lagrangian transport scheme rather than a spectral
transform method; 2) the shortwave radiation is treated by the two-stream method of Fonquart and Bonnell (1980), the long-wave radiation by the method of Morcrette (1991); and
3) the calculations of shortwave absorption by atmospheric water vapor and longwave
absorption by the 7-12 micron water vapor continuum are improved (Giorgetta and Wild,
1995). Figure 2.1 shows the result of one test integration completed at ETH, which demonstrates the improvement in ECHAM4 T42's simulation of the surface radiation fluxes.
This improved treatment is significant for the calculations of model implied oceanic
meridional heat transport.
Table 1. Information for ECHAM3 T42 and ECHAM4 T42 GCMs.
ECHAM3 T42 L19
ECHAM4 T42 L19
Dynamics/Numerics
* Spectral (Baede, et al, 1979), but
* With revised formulation of the pressure gradient term (Simmons and
Chen, 1991)
The same as in ECHAM3, except that
* The horizontal advection of water
vapor and cloud water are treated by
shape-preserving semi-Lagrangian
transport (SLT) scheme (Williamson and Rasch, 1994)
* The vertical advection of positive
definite quantities is treated by SLT
scheme
Horizontal diffusion
* Enhanced scale selectivity (Laursen
and Eliasen, 1989),
* The diffusion coefficients were chosen such that the slope of the spectral kinetic energy is close to
observations.
* linear tenth-order horizontal diffusion is applied to all prognostic variables below about 150hpn,
* in the model stratosphere, the order
of the scheme is reduced incrementally to second-order at the upper
two model levels
Chemistry
* AMIP C02 concentration
* Trace constituents including methane, nitrous oxide, and 16 different
CFCs are added
Radiation
* Hense et al., 1982; and Rockel et al.,
1991.
* Shortwave radiation is treated by the
two-stream method of Fonquart and
Bonnell (1980),
* Longwave radiation by the method of
Morcrette (1991).
* In addition, further changes are made
in the treatment of other gaseous
absorbers, continuum absorption by
water vapor, and cloud-radiative
interactions.
Vertical diffusion
* Louis (1979), but
* with low wind correction according
to Miller et al. (1992).
* The Richardson number is revised to
include "moist" (cloud) effects.
* A higher-order closure scheme after
Brinkop and Roeckner (1995) to
compute the vertical diffusion of
momentum, heat, moisture, and
cloud water.
ECHAM3 T42 L19
ECHAM4 T42 L19
Convection
* Tiedtke (1989) for deep midleval and
shallow convection,
* Stratocumulus convection according
to Tiedtke et al. (1988)
The same as in ECHAM3
* With modifications after Nordeng
(1996)
* The closure assumption is also modified: cloud-base mass flux is linked
to convective instability instead of
moisture convergence.
Soil processes
* Five layer model for heat transfer,
* Refined bucket model for soil moisture;
* Vegetation effects included (Blondin,
1989)
* Sea ice temperature is calculated
from the net heat fluxes including
conductive heat transfer through ice
The same as in ECHAM3, except that
The heat capacity, thermal conductivity, and field capacity for soil moisture are prescribed according to
geographically
varying
values
derived from Ford and Agriculture
Organization (FAQ) type distributions (Patterson, 1990, and Zobler,
1986).
Surface LW down
Surface absorbed SW
182
350
185
180
342
171 173 175
340
F
337 335 334
162 164
326
160 I320
147
F
315
142
140
311
F
300 F
120 F
100'
U
91X
= 0
K~ >
X
Figure 2.1 Comparison of simulations of the surface heat balance by ECHAM GCMs
together with various atmospheric GCMs. On the left is the global mean annual mean
shortwave radiation absorbed at the surface, and on the right the global mean annual
mean longwave radiation incident on the surface. Observational values are based on
the Global Energy Balance Archive (Ohmura and Gilgen, 1993; Wild et al 1995).
2.2 Data Set
The data set used in this research is a part of the ten-year-mean output of the official
ECHAM3 and ECHAM4 T106 L19 model runs provided by Mr. Wild Martin at Swiss
Federal Institute of Technology, Zurich, Switzerland. This data set is mainly confined to
the model's simulation of surface energy budget and water budget. The main fields
included are:
*
Annual mean global distribution of surface latent heat flux.
-
Annual mean global distribution of surface sensible heat flux
Annual mean global distribution of surface short-wave radiation, including the upward
-
and downward partition.
-
Annual mean global distribution of surface long-wave radiation, including its upward
and downward partition.
-
Annual mean global distribution of total cloud cover.
-
Annual mean global distribution of surface albedo.
-
Annual mean global distribution of vertically integrated precipitable water vapor.
-
Annual mean global distribution of evaporation.
-
Annual mean global distribution of precipitation.
-
Annual mean global distribution of river runoff
Chapter 3
Oceanic Heat Transport Calculation
In this chapter, we will first introduce the methodology and correction schemes we used,
and present the simulated mean state of the total heat flux and the component fluxes at the
ocean surface. The model implied meridional northward oceanic heat transport for the
whole ocean and ocean basins will be calculated and compared with some other calculations and available observational study.
3.1 Methodology
The estimation of global mean annual mean oceanic meridional heat transport can be
obtained by several methods. Generally, the methods can be separated into three major
categories: residual method, direct estimate and surface heat-balance method. All these
methods have their associated advantages and disadvantages. The method used in this
research is the so-called "classical method", i.e., the surface heat-balance method, which
calculates the meridional northward oceanic heat transport from the heat balances at the
ocean surface.
A schematic of the atmosphere-ocean system is shown in Figure 3.1, with Fs as the net sea
surface, downward heat flux, which includes the net sea surface radiative flux Rs, net sea
surface latent heat flux LH and net sea surface sensible heat flux SH; and F0 , FA as the ver-
tically integrated divergent heat fluxes in the Ocean and in the atmosphere respectively; R
is the net radiation flux at the top of the atmosphere. The bracket in Figure 3.1 means the
zonal average. All quantities are the annual mean.
[R]
Top of atmosphere
Atmosphere
[A]
[F
[A]
Ocean
-*4
[Fol]
[FOJ
North
South
Figure 3.1 Schematic of heat fluxes in the atmosphere-ocean system. Northward heat fluxes are defined as positive, as are downward vertical fluxes.
The brackets are for the zonal mean.
The energy equation for the oceans can be written as:
S0 +VH*
F 0 = Rs+ LH + SH
(1)
where:
S,
heat storage in the oceans
VH-
horizontal divergence
In the annual mean, S, vanishes as long as no long-term temperature change is observed.
The equation then becomes:
VH
F
= R-VHeFA = F
= RS+LH+SH
(2)
If we take the zonal average of equation (2), we have
- -a[F ] coswp
a cos~paqp
= [Fs]
(3)
Since the northward oceanic heat transport at latitude (p is:
T,(p) = 21cacosp[Fj(p)]
(4)
where a is the earth's radius,
we have
1
a [T(p)] = [Fs]
2xta cosqp a(
(5)
= 2ta 2costp[Fs]
(6)
2
i.e.,
Integrating equation (6) starting from the north pole, the implied meridional oceanic heat
transport across a latitude circle (po is:
IC
p= -2 2 a2 [Fs]cos(pd(p
T90(0 = - 90
(7)
Equation (7) is the basic equation we will use to calculate the model implied oceanic
meridional heat transport. This method implicitly assumes that there is no heat flux across
the land-sea boundary.
3.2 Correction Scheme
Because of the existence of an imbalance in global mean surface heat flux over oceans, the
integration of equation (7) at the south pole will not result in zone transport, as it should
be. We need to apply the correction method to correct it.
As discussed in Carissimo et al., 1985, there are many correction methods based on different considerations. We will adopt here the same symbols as in Carissimo's work, where a
subscript NP indicates the integration was carried out starting at the north pole, and a subscript SP represents a calculation starting from the south pole. A superscript * means the
transport is uncorrected.
(1) Standard method, which applies a constant correction to all the original values.
TP
2
T0(y) - TONP
= T
ONP
*()
- - T
2
ONP
-- l(1
2
2cos gdp
- sing)
(8)
The calculation is uniformly applied to unit area at each latitudinal circle. So there is no
bias between the high latitudes and low latitudes. We will apply this correction scheme to
all the heat transport calculations in this thesis.
(2) the correction varies with latitude as in Saunders et at., 1983, which mainly affects the
low latitudes based on the consideration of reflected radiation associated with the diurnal
cycle of the albedo:
To(p) = TONP*(p )
cos (pd
J 22 2
f9
a
e
ONP
sin 2 e9
ONP*(
(9)
This scheme affects the tropical regions mostly since the mean surface albedo is highest
over that region.
(3) the correction weighted average of TONP* and Tos,*
T9()) =TONP
OSP
(10)
The effect of this correction schema depends on the distribution of the uncorrected heat
transport. In our case, the correction of flux is larger in the midlatitudes than in the tropics.
In this thesis, we use the standard method to correct the non-zero oceanic heat transport at
the south pole induced by the imbalance of heat flux at ocean surface.
3.3 Heat Fluxes at Ocean Surface
The surface heat fluxes across the surface, which is the boundary between the atmosphere
and ocean or the land, is an essential part of the climate system as are the fluxes at the top
of the atmosphere. The geographic distribution of the surface heat fluxes determines the
distribution of surface temperature, the amount of energy flux available to evaporate the
surface water and hence the intensity of the hydrological cycle. The understanding of the
heat balance at the surface is a necessary part of understanding climate and its dependence
on external constraints. Therefore, it is important for GCMs to simulate the surface heat
fluxes accurately when attempting to simulate the past, current and future climate.
The surface heat fluxes include the net radiative heat flux from the overlying atmosphere
into the surface, the latent heat flux and the sensible heat flux at the surface, as expressed
in the following equation:
+ LHsfC
FSfc + SHsfc
Fsfc
net
net
net =
-rad
(1
where the superscripts sfc means surface, subscript rad means radiative, SH is sensible
heat flux, LH the latent heat flux.
Since the net input flux of radiative energy to the surface is the sum of the net shortwave
and longwave heat fluxes, we re-write (11) as:
"
Fc Sfc1
FSfc = F fc -Ffc +F
1w
sw
sw
rad
(12)
where T means upward, I means downward; subscript sw means the shortwave, 1w means
the longwave. Here we define the downward heat flux as positive.
Several key physical properties of the ocean let it play the critical role in the climate system: it has a low albedo when unfrozen; it has a large heat capacity; it is fluid and it covers
most of the earth's surface area. Even overlain by the atmosphere, the ocean receives more
than half of the energy entering the climate system. It also gives much of the ocean
absorbed shortwave radiation to the atmosphere through evaporation, making the ocean
the primary source of water vapor and heat for the atmosphere.
To analyze the meridional northward oceanic heat transport calculated from the surface
heat-balance, we present here the model simulated annual mean heat fluxes at the ocean
surface in Figures 3.2 to 3.23, among which are figures for the annual mean, zonal mean
and the corresponding difference between ECHAM3 and ECHAM4, together with some
related fields. All these figures are positioned after Chapter 6.
Both ECHAM3 and ECHAM4 T106 AGCMs show that, in Figure 3.2 and Figure 3.3, the
net radiation flux is greatest over the tropical oceans. In that region the net radiation flux
2
exceeds 150 w/m 2 , and in ECHAM3 in central tropical Pacific Ocean, it exceeds 200w/m .
Along 60 N, there are minima showing in the global annual mean distribution and their
corresponding zonal means, which are associated with the annual mean location of InterTropical Convergence Zone (ITCZ).
The variation of the net radiation flux with latitude and surface conditions is systematic.
The low latitudes have the higher radiation surplus, and the high latitudes have the lower
one. In southern hemisphere, the radiation flux contours(eg., 100 w/m 2 and 50 w/m 2 ) are
aligned along latitude circles, while in the northern hemisphere the contours are tilted due
to surface effects.
The net radiation variation with the latitude can also be clearly seen in the zonal mean figures. Because of the tilting in the northern hemisphere, the change of zonal mean net radiation flux with latitude is slower in the northern hemisphere.
The difference of the net radiation flux at the ocean surface for annual mean and zonal
mean between ECHAM3 and ECHAM4 indicates the effect of the modification of the
radiation scheme from ECHAM3 to ECHAM4. In Figure 3.4(a) and Figure 3.4(b), the
most significant pattern is in the tropical Pacific Ocean. ECHAM4 has much less (more
0
than 50 w/m 2 ) net radiation flux input into the ocean than does ECHAM3 along 6 N in
Pacific Ocean, and in Atlantic Ocean it is about 25 w/m 2 less. For the zonal mean, the dif-
ference is more than 30 w/m 2 at 60N. On the other hand, in the zonal mean difference, the
ECHAM4 ocean has a larger net radiation flux surplus in the southern hemisphere midand high latitudes and northern hemisphere high latitudes. The required heat transport by
the ocean poleward from the tropics must be smaller in ECHAM4.
If we break down the net radiation flux into its shortwave and longwave components and
up- and downward directions, as shown in figures from Figure 3.5 to Figure 3.10, we can
2
see that the peak difference at 60 N with a value of -34 w/m 2 results from -46 w/m of
zonal mean absorbed shortwave radiation component flux, and 17 w/m 2 of zonal mean
downward longwave radiation flux component. To draw a conclusion on whether the air in
the ECHAM4 model absorbs more incoming shortwave radiation, we need to look at the
Top of the Atmosphere.
Figure 3.11 to Figure 3.13 present the ECHAM models' mean state (annual mean and
zonal mean, together with their difference) for planetary albedo. There is almost 1%
change of the global mean value, with 32.43% in ECHAM4 and 33.35% in ECHAM3.
Although the global mean value decreases in ECHAM4, the difference of the quantity for
annual mean and zonal mean shows that the decrease occurs mainly in high latitudes while
there is an increase in low latitudes in ECHAM4. The largest increase is over tropical
Pacific Ocean along 60N; the tropical Atlantic Ocean has a minor contribution.
Since both ECHAM3 and ECHAM4 models use the same solar constant, the difference in
the mean planetary albedo between the two models reflects the difference in the mean outgoing shortwave radiation at the top of the atmosphere. Therefore, over the tropical oceans
at the top of the atmosphere there would be less net incoming shortwave radiation while
there would be more net incoming shortwave radiation over other regions in ECHAM4.
Together with the Figures 3.5 to 3.10, we may infer that, due to the radiation scheme modification in the ECHAM4 T106 AGCM, the atmosphere over the oceans absorbs or reflects
much more incoming shortwave radiation in ECHAM4 than in ECHAM3. Consequently,
the underlying ocean in ECHAM4 receives less incoming shortwave radiation but more
downward longwave radiation from a cloudier atmosphere. The compensation makes the
changes in the net radiation flux less than in the absorbed shortwave radiation. From the
global mean point of view, ECHAM4 has a more realistic (i.e., closer to the observations,
see in Figure 2.1) radiation scheme.
The mean state of model simulated total cloud cover in Figure 3.14 to Figure 3.15,
together with their corresponding difference in Figure 3.16, also shows the effect of modification to the radiation scheme. We also include in Figure 3.17 the ISCCP-C2 8-year
mean total cloud amount observation which is the International Satellite Cloud Climatology Project (established in 1982 as part of the World Climate Research Programme
(WCRP)) Stage 2 analysis for the months July 1983 through June 1991. Compared against
the ISCCP data, ECHAM4 has simulated fairly accurately the total cloud cover distribution for the northern Atlantic Ocean, where the ECHAM3 apparently has too low total
cloud cover. The similar too low total cloud amount pattern can be found in ECHAM3
southern Atlantic Ocean. For the Pacific Ocean, the large area of low cloud amount in the
tropical Pacific Ocean is not shown in ECHAM4 but ECHAM3 has a good representation
for this region. But the overall pattern matching in the Indo-Pacific Ocean is much better
in ECHAM4 than in ECHAM3. The global mean total cloud cover is -50% in ECHAM3
and -59% in ECHAM4. The ECHAM3's total cloud cover is too low.
If comparing Figure 3.10(a) with Figure 3.16(a), the total cloud cover increase in the eastern Pacific and Atlantic Oceans in ECHAM4 corresponds to the increase of downward
longwave radiation over those regions.
The evaporative heat loss from the ocean surface (LH flux in Figure 3.18 to Figure 3.20) in
both models has the greatest values over the midlatitudes and the warm western boundary
currents. In the western region of the Pacific and Atlantic oceans, the latent heat loss may
exceed 200 w/m 2 which is much greater than the local net radiation heating to the ocean
surface. The latent heating cooling of the ocean surface is also large over the subtropical
oceans, which offsets most of the heating to the ocean surface by the net radiation.
Along the equator, there is a "tip" in the zonal mean latent heat flux (Figure 3.19) in both
models, showing a greater reduction (-40 w/m 2 ) of evaporation heat lose to the atmosphere. It results from lower evaporation over the relatively cold ocean water flowing from
the eastern ocean to the western ocean region (northern and southern equatorial currents).
The pattern of the difference of zonal mean latent heat flux at ocean surface between
ECHAM3 and ECHAM4 is very different from those of the radiation fluxes. It goes up
and down more frequently, and is mostly positive (more evaporative heat loss in
ECHAM4). The value is greater in the Northern Hemisphere, indicating a larger modification effect there, though it is hard to say the modification is really an improvement.
The sensible heat loss (figures not included in this thesis) from the ocean surface is small
in both models, except over the warm western boundary currents of the midlatitude
oceans. Over that region, when cold air from the continents flows over the warm ocean
2
surface (mainly during winter), a large sensible heat flux occurs. It may exceed 50 w/m in
the annual mean. But it is still much less important to the mean surface net heat flux than
is the latent heat flux. (The difference between the two models is small too!)
Adding the net radiation flux, the latent heat flux and sensible heat flux together, we obtain
the net heat flux at the ocean surface, as shown in Figure 3.21 to Figure 3.23. In both
ECHAM3 and ECHAM4 AGCMs, the net heat flux is large and negative over the western
boundary currents, where the ocean is supplying the energy through heat transport by the
ocean currents and then heating the atmosphere. In the equator and the eastern regions of
the ocean, the atmosphere is heating the oceans (with positive net heat flux at the ocean
surface) where upwelling brings the cold water from the deep ocean to the ocean surface.
We have seen that in these regions the evaporative cooling in reduced and the net radiation
is used to heat the ocean. Ultimately, the energy will be transferred to mid- and high latitudes and then lost back to the atmosphere. This heat transport from the tropical and eastern oceans to mid- and high latitudes plays a critical role in determining the energy cycle
of the climate system.
The difference of the net heat flux between the two ECHAM models (Figure 3.23) has
negative values within ±300 latitude, and positive values over other regions. Since the net
heat flux is positive in low latitudes and negative in mid- and high latitudes, the difference
indicates that, in tropical regions the ECHAM4 model's ocean receives less net heat flux
than does the ECHAM3's ocean; on the other hand, in mid- and high latitudes the
ECHAM4 ocean loses less net heat to the atmosphere than the ECHAM3 ocean. We also
notice the relatively large area of positive net heat flux around 500 S in ECHAM4, which
may induce an equatorward heat transport.
3.4 Meridional Northward Oceanic Heat Transport
The radiative energy budget of earth's climate system is characterized by strong input of
solar energy at low latitudes and a back radiation to space which is more uniformly distributed over the globe. On an annual mean basis, the net radiative energy budget of the climate system must be balanced, which implies the existence of a net poleward heat
transport by the atmosphere and oceans combined in both hemispheres. This combined
atmospheric and oceanic meridional heat transport can be estimated with high accuracy
from satellite radiation measurements of the net radiation budget at the top of the atmosphere. It is interesting to note that the apportionment of the meridional heat transport
between ocean and atmosphere has been a matter of some controversy in history: meteorologists have tended to ascribe a greater role in meridional heat transport to the ocean
than oceanographers (Bryden, 1993). Normally, meteorologists base their estimates of the
oceanic heat transport on residuals of the satellite derived radiation budget and the AGCM
15
output calculated atmospheric heat transport. The value is nearly 4 PW (lPW=10 watts)
in mid-latitudes. On the other hand, integrations of surface oceanic heat flux over ocean
derived from bulk formulas by Budyko (1974) and Talley (1984) have much lower values,
typically 1 to 2 PW. These uncertainties may come from the inaccurate simulations of the
surface heat fluxes by the AGCMs, lack of data over oceans, errors in the bulk formulae,
and instrumental radiation measurement uncertainty at the top of the atmosphere. With the
improvement of data availability and treatment of surface heat fluxes, recalculations are
needed and should contribute to a better understanding of the ocean's role in transferring
heat poleward and in reducing the equator-to-pole temperature gradient.
In this subsection, we will present the estimates of the global mean annual mean total oceanic meridional heat transports as a function of latitude based on ECHAM3 and ECHAM4
T106 ten year runs. The respective estimates for the major ocean basins: Atlantic ocean,
the Pacific Ocean and the Indian Ocean have been carried out. Since the Indonesian
throughflow has not been taken into account, the contributions from Pacific Ocean and
Indian Ocean in this research should be combined and then represented as Indo-Pacific
Ocean.
3.4.1 Results
The mean state of the meridional northward heat transport are calculated from the annual
mean net heat flux at the ocean surface using the surface heat- balance method, and corrected with the standard correction scheme. The results for the ocean total and ocean
basins are presented in Figure 3.24 and Figure 3.26. In the discussion section, some other
model simulation results and observations are included in Figures and Tables.
(1) Ocean Total
The ECHAM3 and ECHAM4 T106 implied annual mean meridional northward heat
transport for ocean total are shown in Figure 3.24. In both models, the annual mean ocean
transport shows a substantial asymmetry between the northern hemisphere and southern
hemisphere. This asymmetry must be related to the difference in physiography of the more
land-covered northern hemisphere and more ocean-covered southern hemisphere. In midlatitudes, the heating due to the convergence of the heat transport in northern hemisphere
is larger than that in southern hemisphere in both models. The highest heat transport
occurs at around 200 in both models. The variation in northern hemisphere is smaller than
in southern hemisphere, and we can see that, in southern hemisphere ECHAM4, even the
direction of oceanic heat transport changes to equatorward.
Total oceanic meridional heat transport
2.5 --
21.51D0.5U)0
0.5
fE
A
a
E
A
TECHAM3
-1.5P at
ECHAM4
-2-2.5
-80
-60
-40
-20
0
Latitude
20
40
I
60
80
Figure 3.24 Annual mean zonal mean meridional total oceanic heat transport
for ECHAM3 and ECHAM4 T 106.
The implied heat transport in northern hemisphere mid-latitude ocean is of the order of 2
PW. ECHAM3 and ECHAM4 show their consistency in the estimate over this region. The
value is much closer to the oceanographer's estimate (1-2 PW) than that of early meteorologists' (-4 PW).
(2) Ocean Basins
In order to calculate the heat transport for ocean basins, we need the proper boundaries to
"separate" the ocean basins from each other, and then apply the surface heat-balance
method to the ocean basin's annual mean zonal mean net heat flux. The calculations for
the Atlantic ocean and Indo-Pacific ocean may be questionable in the southern hemisphere
beyond the Cape of Good Hope (-350 S). There is a cross-basin heat transport which we
totally ignore in the calculation. Generally, the calculation method used for ocean basins is
only applicable to a basin which is wholly bounded by the land, or the western and eastern
boundary conditions are well known.
The conceptually separated ocean basins used in this calculation are shown in Figure 3.25.
We did not take into account the Indonesian throughflow, so the Pacific Ocean and Indian
Ocean should be considered as one, i.e., the Indo-Pacific Ocean. Because many other studies explicitly calculated the heat transport for the Pacific Ocean, we use the calculation
from the Indo-Pacific Ocean to represent the value in the northern hemisphere for the
Pacific Ocean since our calculation shows the heat transport in the northern hemisphere in
the Indian Ocean is small.
As we can see in Figure 3.26, the nature of the heat transport differs remarkably for Atlantic Ocean and Indo-Pacific Ocean. Northward heat transport is found at almost all latitudes
in the Atlantic Ocean in both models. The difference of heat transport between ECHAM3
and ECHAM4 is small compared with that for Indo-Pacific Ocean.
The variation of total oceanic heat transport between ECHAM3 and ECHAM4 is mostly
explained by their corresponding variation in the Indo-Pacific Ocean, especially in the
southern hemisphere. But the asymmetry of total oceanic heat transport between southern
Schematic of Ocean Basins
-20
-40
-60
-80
0
240
180
Longitude
120
60
300
360
Figure 3.25 The schematic of ocean basins.
Ocean basin meridional heat transport
-0.5
Atlantic, ECHAM3
Atlantic, ECHAM4
AtlndtPaic, ECHAM
... -.. Indo- P acific,.E C H A M 3
- ..........
-
-1.5
-.-.-.-.-.-.-.-.--.-.-.-....
-2
-Indo-Pacific,
-2.5
-80
-60
-40
-20
0
Latitude
20
40
ECHAM4
60
80
Figure 3.26 Annual mean zonal mean meridional heat transport in Atlantic Ocean
and in Indo-Pacific Ocean for ECHAM3 and ECHAM4 T106.
hemisphere and northern hemisphere mainly results from the contribution of Atlantic
Ocean. Generally, in the northern hemisphere, the poleward heat transport is larger in the
Atlantic Ocean than that in the Indo-Pacific Ocean, while in the southern hemisphere, the
heat transport by the Indo-Pacific Ocean dominates over the Atlantic Ocean.
3.4.2 Discussion
(1) Ocean Total
In the previous section we presented the characteristics of the ECHAM AGCMs' implied
mean state of oceanic meridional northward heat transport. The difference in this quantity
(as shown in Figure 3.27) is of great interest and important to us for the purpose of model
comparison and improvement.
In Figure 3.27, at almost every latitude in both southern and northern hemisphere,
ECHAM3 has the larger poleward heat transport than has ECHAM4, except in a small
area at the equator in the northern hemisphere. In other words, Figure 3.27 suggests that
the implied ocean in ECHAM4 plays a less important role in transferring heat poleward
than does the ECHAM3 implied ocean. Consequently, since the total heat transport
required for the balance of the earth climate system at the top of the atmosphere should be
the same for both ECHAM models, the ECHAM4 model's atmosphere will transfer more
heat poleward than will the ECHAM3's atmosphere. The calculation of atmospheric heat
transport for the models does not support this inference, as shown in Figure 3.28.
In Figure 3.28, the atmospheric heat transport simulated in ECHAM3 and ECHAM4 are
almost identical except a small difference (< 0.2 pw) spanning most areas in the southern
hemisphere. Further investigation shows that the model's atmosphere seems "transparent"
to the underlying ocean: the pattern of difference in oceanic heat transport between the
Difference of Total Oceanic Heat Transport
0.5-
0
-0.5ECHAM4 - ECHAM3
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure 3.27 The difference of implied total oceanic heat transport between
ECHAM3 and ECHAM4 T106.
Atmospheric annual total heat transport (PW)
Latitude
Figure 3.28 Annual mean total northward atmospheric meridional heat transport
simulated by ECHAM3 and ECHAM4 T106.
two models has been almost exactly copied to the top of the atmosphere, thus obtaining
the same pattern of difference in total heat transport at the top of the atmosphere.
Gleckler et al (1995) proposed that the implied oceanic heat transport was sensitive to the
radiative effects of clouds to explain the large discrepancy of this quantity among current
AGCMs. They computed a "hybrid" oceanic heat transport To which includes the cloud
radiative forcing with the formula:
TO=TA+O-TA~TO+ 6 TCRF
(13)
where:
TA+o = the atmosphere and ocean combined northward meridional heat
transport inferred from the observed 4 years net top-of-the-atmosphere radiation in the Earth Radiation Budget Experiment
(ERBE) (Barkstrom et al, 1990).
STCRF = the difference between the observed and simulated TA+O resulting
from the effects of clouds;
Their results show a remarkable improvement in the calculation of AGCMs' implied oceanic heat transport after the cloud radiative forcing correction.
In Figure 3.29 we calculate and present the same "hybrid" oceanic heat transport as in
Gleckler et al by using the same TA+o from ERBE observation. The improvement as compared with Figure 3.24 is also remarkable, the ECHAM3 and ECHAM4 T106 have
obtained a consistent (not necessarily correct) oceanic heat transport now! To verify the
reasonability of these "hybrid" oceanic heat transports, let's make the comparison against
other available observational data.
Total "Hybrid" Oceanic Heat Transport
-2.5'1
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure 3.29 The "hybrid" total oceanic heat transport for ECHAM3 and ECHAM4
T106 models.
In Table 2, we present the oceanic heat transport calculation for ECHAM3 and ECHAM4
before and after cloud radiative forcing correction, the results obtained by Macdonald and
Wunsch (1996), and by Trenberth and Solomon (1994). The Macdonald and Wunsch's
estimate was derived by integrating hydrographic velocity data over the rapid spatial variations that they show. Trenberth and Solomon used operational weather prediction analysis produced by the European Centre for Medium Range Weather Forecasts (ECMWF).
These two data sets are believed to be reliable.
Table 2. Comparison of total oceanic heat transport calculation (PW)
Latitude
hybrid
ECHAM3
hybrid
ECHAM4
ECHAM3
ECHAM4
Macdonald
Trenberth
500 N
1.0
0.8
0.8
0.55
0.55±0.3
0.57
24-250 N
2.0
2.1
2.0
1.42
1.6±0.3
2.0
100 N
1.0
1.1
1.8
1.5
1.5
100S
-1.7
-1.3
-1.1
-0.3
-0.7
300 S
-1.7
-1.6
-1.5
-0.4
-0.9±0.3
-0.8
In Table 2, the largest change occurs in ECAHM4 southern hemisphere low latitudes
(100S and 300 S here, as included in Table 2). Note please in Figure 3.24, that the oceanic
heat transport in the ECHAM4 southern hemisphere is small, and even equatorward
around 400S. It suggests that the oceanic heat transport in the ECHAM4 southern hemisphere without cloud forcing correction is unreliable. On the other hand, when comparing
the hybrid results with Macdonald's and Trenberth's, we may argue that this cloud radiative forcing correction which brings the consistency between ECAHM3 and ECHAM4 in
estimating the oceanic heat transport is even worse in this case for the ECHAM AGCMs.
The hybrid values at 100 S and 300 S in both ECHAM3 and ECHAM4 are nearly 1 PW
larger than those in Macdonald and Trenberth.
In Figure 3.29, the asymmetry of oceanic heat transport between northern hemisphere and
southern hemisphere disappears, and the distribution shows a nearly symmetric characteristic which is not appreciated due to the asymmetric physiography of northern and southern hemisphere.
So, our calculation suggests that, the cloud radiative forcing correction scheme can
explain only to some extent the discrepancy in estimating the oceanic heat transport, and
in some cases the correction scheme may result in a worse estimate. There must be other
factor or factors that may contribute and thus enhance the model's simulation ability.
(2) Ocean Basins
The intercomparison of ECHAM3 and ECHAM4's implied oceanic heat transport by
Atlantic Ocean and Indo-Pacific Ocean with some other studies were carried out and the
calculations can be found in Tables 3a and 3b.
There are great uncertainties in determining the role of different ocean basins in transferring heat meridionally, although both ECHAM models and some other studies show that
the Atlantic ocean transports heat northward in both southern and northern hemispheres
which is consistent with the conceptual "conveyor belt" flow. As we mentioned in the previous section, the calculations for the Atlantic ocean and Indo-Pacific ocean may be questionable in the southern hemisphere beyond the Cape of Good Hope (-350 S), because we
totally ignored the cross-basin heat transport in our calculation.
In the Atlantic Ocean, however, most of the estimates are in better agreement with each
other than in the Indo-Pacific Ocean. Virtually, all the listed estimates in Table 3a shows
the northward oceanic heat transport at all latitudes, with the most significant heat lose to
the atmosphere in the midlatitude North Atlantic. The ECHAM4 has the smaller transport
there than the ECHAM3, resulting from less input heat flux in the tropics and more heat
supply in midlatitudes. Although both ECHAM4's and ECHAM3's estimates are smaller
than other estimates except Hsiung's, ECHAM3 has the better representation than
ECHAM4 in the North Atlantic.
Table 3a: Comparison of various estimates for Atlantic Ocean heat transport (PW)
Latitude ECHAM3
60 0N
50 0N
40 0N
300N
24-25 0N
100N
0
100S
20 0S
300S
40 0S
0.21
0.55
0.71
0.97
1.03
0.87
0.62
0.30
0.07
-0.02
0.14
ECHAM4
Trenberth
(1994)
0.15
0.35
0.45
0.70
0.80
0.74
0.59
0.34
0.14
0.07
0.21
0.30
0.50
0.78
1.00
1.10
0.80
0.45
0.30
0.30
0.18
0.05
Macdonald
(1996)
Bryden
(1993)
0.65+0.3*
1.10+0.3
1.40+0.3
1.22
0.88+0.3
0.4+0.3
Hsiung
(1985)
0.24
0.45
0.63
0.95
0.96
0.80
0.54
0.23
0.15
0.04
0.09
Table 3b: Comparison of various estimates for Pacific Ocean** heat transport (PW)
Latitude
60 0N
500N
40 0N
300N
24-250 N
100N
0
100 S
20 0 S
300 S
40 0 S
ECHAM3
0.06
0.35
0.45
0.82
0.96
0.96
0.05
-1.34
-1.71
-1.43
-0.99
ECHAM4
0.03
0.20
0.22
0.53
0.62
0.74
0.22
-0.65
-0.81
-0.47
-0.18
Macdonald
(1996)
-0.1+0.3*
0.5+0.3
-1.3+0.3
Trenberth
(1994)
0
0.07
0.30
0.82
0.90
0.70
0.30
-1.0
-1.2
-1.0
-0.85
Bryden
(1993)
Hsiung
(1985)
0
0.55
0.76
0.32
-0.52
-0.24
0.01
Note:
*
The value is estimated at 47-48ON as in Table 2a in Macdonald
**
The results presented for ECHAM3 and ECHAM4 Pacific Ocean are for
Indo-Pacific Ocean
In the South Atlantic, ECHAM4 has a little larger northward heat transport than
ECHAM3, and it is closer to Trenberth's estimate. Macdonald's calculation is too large
comparing with all the others at 100 S, making its estimate questionable. There is a minimum at about 300S in both ECHAM3 and ECHAM4 which is absent in Trenberth's, indicating the heat loss to the atmosphere. The northward heat transport from south of 300S
also contributes to this heat lose.
The Pacific Ocean is the place where generally the oceanic heat transport estimates of different datasets and methods differ greatly with each other, especially in the South Pacific.
In the North Pacific, ECHAM3 has higher values than ECHAM4, and is higher than the
others in the Table 3(b). ECHAM4 has a better estimate than ECHAM3, when compared
with the other estimates. In the South Pacific, ECHAM4's estimate is too small while
ECHAM3's is too high. At 20 0 S, ECHAM4 only transfers half of the heat poleward as in
ECHAM3. Interestingly, Trenberth's estimates at 100, 200, and 300 S are just the mean of
the values in ECHAM3 and ECHAM4. Other studies suggest that ECHAM3 and
ECHAM4 set the upper and lower limit of model implied oceanic heat transport among
currently-in-use AGCMs respectively. These two ECHAM models may thus serve as the
bounds for guiding model heat flux related modifications.
3.5 Oceanic Heat Transport with other boundary Conditions
The calculations in the previous sections are based on the ECHAM3 and ECHAM4 T106
model runs with the monthly mean SSTs and sea ice extent as the surface boundary conditions, and with the mean solar constant as the external forcing at the top of the atmosphere.
The boundary conditions are the same for every model year. Thus analysis of the annual
mean state of the heat fluxes at the ocean surface and the model implied meridional oceanic heat transport will be smaller for the model years after the models' integrations converge.
The ability and reliability of an AGCM to simulate accurately the interannual variation of
the climate system are an important aspect for the validation of the model. One goal of a
climate model is to predict the future climate change reasonably accurately due to a natural or human-induced perturbation (such as the increase of Greenhouse gases), thus serving as a guide for humans to adjust their behavior in time and avert the worst
consequences of such a global climate change.
The climate modeling community at MIT has run the ECHAM4 model at T42 resolution
with the observed monthly mean SSTs and sea ice extent as the surface boundary condition. The total model run time is 25 years, including 7 El Nin6 events. We extract the same
datasets as the T106 models and perform the same analysis to study the model's behavior.
The model will be designated as "ECHAM4 Gisst" in this section. The analysis is for the
25 year mean.
3.5.1 Ocean Surface Heat Flux
The 25 year mean analysis of the heat flux components and their corresponding zonal
mean, together with the calculation of the 25 year mean planetary albedo and total cloud
cover, for ECHAM4 Gisst are carried out and presented in Figures 3.30 to 3.36 (following
Chapter 6). In Figure 3.37, we list the calculation of the global mean surface absorbed
shortwave radiation and downward longwave radiation for ECHAM4 Gisst and the comparison with ECHAM3, ECHAM4 T106 and GISS models as in Figure 2.1.
The simulation of these surface flux terms in ECHAM4 Gisst generally resemble the distribution in ECHAM4 T106, but there are some significant changes worth noticing.
The zonal mean net radiation flux (Figure 3.30) at the ocean surface is smaller (-40 w/m2 )
in the tropical oceans in ECHAM4 Gisst than that in ECHAM4 T106. Outside of the trop-
ics, the change is small. So the Gisst model tropical ocean would be less heated by the
radiation. If not compensated by other flux terms, the implied poleward heat transport
from the tropics will be smaller in the ECHAM4 Gisst model.
The mean distribution of planetary albedo has a small difference between ECHAM4 Gisst
and T106. The global mean is almost the same, reflecting the same radiation scheme
implemented in the ECHAM4 models. In the annual mean, the boundary condition at the
lower surface will have only a small effect on the distribution of planetary albedo.
The total cloud cover calculation (Figure 3.34) shows the same distribution as in
ECHAM4 T106 except over the warm pool region. ECHAM4 Gisst has a smaller area of
high annual total cloud cover ( 80%) in the western tropical Pacific than ECHAM4 T106
(Figure 3.14(b)). We may also notice the eastward expansion of the 70% contour in the
central tropical Pacific. It may result from the shift of strong convection from western
tropical Pacific to eastern tropical Pacific during El Nin5 events. So, including El Nin6
events in the boundary conditions can affect the distribution of the clouds. Careful comparison of Figure 3.34(a) with Figure 3.17, which is the ISCCP 8 year mean from satellite
data, suggests the close similarity between the two.
In Figure 3.35, there is a large area of strong latent heat loss from the ocean surface at
201S in the Pacific Ocean, making the zonal mean value there to be -40 w/m 2 . This
enhanced heat loss is not compensated by the heat supply to the tropics since we have
noticed the reduced radiation flux in the tropical Pacific (Figure 3.30). We then expect a
northward heat transport from the high latitude southern Pacific Ocean to provide the heat
loss from the ocean surface at 20 0 S. In the Atlantic, the latent heat loss is enhanced in the
north Atlantic near the Gulf region, so the northward heat transport from the south Atlantic may also increase. This situation is clearly shown in Figure 3.36, which is the annual
mean and zonal mean of the net heat flux at ocean surface in ECHAM4 Gisst.
The comparison of global mean annual mean shortwave radiation absorbed at the surface
as simulated by ECHAM4 Gisst and by some other AGCMs are presented in Figure
3.37(a), and the corresponding comparison for downward longwave radiation is included
in Figure 3.37(b). Comparing with ECHAM4 Gisst T42 with ECHAM4 T106, we may
conclude that the simulation of global mean annual mean absorbed shortwave radiation
and downward longwave radiation at the surface are not sensitive to the model's horizontal resolution and boundary conditions as they are using the same radiation schemes. The
difference in Figure 3.37 between ECHAM4 Gisst T42 and ECHAM4 T106 are small,
both are close to the GEBA observations.
3.5.2 Oceanic Heat Transport
(1) Ocean Total
The calculation of implied meridional northward oceanic heat transport in ECHAM4 Gisst
T42 is presented in Figure 3.38. The most striking pattern is in the southern hemisphere.
The equatorward heat transport at 400S has the same magnitude (-0.4 PW) as the poleward heat transport at around 200S. Notice our discussion in the previous section that the
calculation in the southern hemisphere beyond 350S is questionable. The 0.4 PW heat
transport at around 200S is too small. We can see the calculation from this ECHAM4 Gisst
T42 dataset in the southern hemisphere for the implied oceanic heat transport is worse
than in ECHAM4 T106. Nevertheless, the calculation in the northern hemisphere is better.
We also calculate the portion of northward heat transport by the atmosphere (in Figure
3.39) and the total heat transport required at the top of the atmosphere, together with the
total heat transport calculated from the ERBE satellite data (in Figure 3.40). Generally, the
total heat transport at the top of the atmosphere is smaller than the ERBE observations.
The largest deficit occurs at around 300 degree latitude in both hemispheres, with a magnitude of 0.8 PW. The "hybrid" oceanic heat transport for the ECHAM4 Gisst T42 is shown
in Figure 3.41. This calculation is not satisfactory in the northern hemisphere with a too
high northward heat transport. In southern hemisphere, the "hybrid" calculation seems
close to Macdonald's and Trenberth's.
(2) Ocean Basins
In Figures 3.42(a) and (b), the oceanic heat transport in the Atlantic Ocean and IndoPacific Ocean for ECHAM4 Gisst is presented. Compared against the estimates in Table
3.(a), the calculation for the Atlantic Ocean heat transport is improved. In both the southern and northern hemispheres, the ECHAM4 Gisst estimate is close to that in Trenberth's.
In the Indo-Pacific, the calculation in the southern hemisphere is not accurate with an
equatorward heat transport in mid- and high latitudes. The poleward transferred heat has
its peak (-0.65 PW) at 180S. It is too small. The magnitude of heat transport in the northern hemisphere is the right order, but it seems that the peak location has shifted equatorward to 80N.
Chapter 4
Surface Hydrological Balance
Besides the surface heat fluxes, another important surface process that is essential to the
climate system is the moisture fluxes at the surface. Through the processes of precipitation, evapotranspiration and condensation, surface moisture fluxes are highly related to the
redistribution of energy within the climate system.
This chapter presents the ECHAM3 and ECHAM4 T106 AGCM models' simulated
hydrological forcing terms, precipitation, evaporation, and river runoff. Also studied is the
vertically intergrated atmospheric water vapor (i.e. the precipitable water W). Because of
the importance of and our interest in atmospheric freshwater fluxes and their effect on the
thermohaline circulation, the calculation of simulated Atlantic freshwater fluxes is carried
out. These calculations are compared with the available observations or analyses.
4.1 Precipitable Water
Precipitable water W is the vertically integrated water content in the atmosphere:
W = fpeqdZ = 1fPSqdp
(14)
where q is the specific humidity, p is the density of air, Z is height, p is the pressure and p,
is the surface pressure, g is the gravitational acceleration. The hydrostatic principle has
been applied.
We show in Figure 4.1 and Figure 4.2 the global distribution of precipitable water simulated in ECHAM3 and ECHAM4 T106, and their corresponding zonal mean (Figure 4.3).
Also shown (Figure 4.4) is the recently compiled global precipitable water from the set of
global analyses of water vapor called NVAP (Randel et al, 1996).
Generally, both ECHAM3 and ECHAM4 T106 resemble the main pattern in the NVAP
analysis, and successfully simulated the global precipitable water climatology features:
the gradual decreasing of W from equator toward the south and north poles, the low values
of W over high terrain and the desert regions, and the maximum W over the tropical western Pacific. Nevertheless, both ECHAM T106 GCMs tend to overestimate the precipitable
water in the tropical convergence zones and in the south Pacific subtropical high. In
ECHAM3, the pattern in the south Pacific is more zonal instead of extending southeastward from the tropical Pacific warm pool as in ECHAM4 and the NVAP analysis. It is also
clearly shown in the zonal mean field that there is a second maximum located near the
equator in the southern hemisphere in ECHAM3. We can expect there is a wet bias to the
southern hemisphere (oceans) and tendency to overestimate the precipitation there. The
improvement in ECHAM4 may be attributed to the new treatment of horizontal advection
of water vapor and cloud water by the SLT scheme as highlighted in chapter 2.
The global mean precipitable water is 25.47mm in ECHAM3 and 24.7mm in ECHAM4.
The observed value is 25. 1mm which is computed using gridded analysis of radiosonde
observations during 1979-88 produced by A. Oort. ECHAM3 tends to overestimate the
global mean net precipitable water by 0.37mm (-1.5%). The percentage of reduction in
global mean W from ECHAM3 to ECHAM4 is 3.0%. The statistics may suggest that
while the SLT scheme improves the advection of water vapor and cloud water, it loses
water too. Please note that the SLR scheme is not inherently conservative. In ECHAM4,
the mass conservation is enforced at every time step through a variational adjustment of
the advected field which weights the amplitude of the adjustment in proportion to the
advection tendencies and the field itself.
4.2 Precipitation
Figures 4.5 to 4.8 show the annual mean distribution of total precipitation for ECHAM3
and ECHAM4 T106, observations of annual mean total precipitation from the Global Precipitation Climatology Project (GPCP) (Arkin and Xie, 1994; Xie and Arkin, 1996), and
their corresponding zonal mean distribution.
Generally, the two ECHAM T106 GCMs are capable of simulating the main precipitation
patterns. The ITCZ, the SPCZ, and the extratropical storm tracks are well defined, though
the ECHAM3 shows a persistent deficiency in simulating the SPCZ. The second maximum over midlatitudes is also clearly shown. Noteworthy are the very high values (~10
mm/d) simulated in both models over the equatorial regions in South America (Amazon
region), Africa, Indonesia, and in the central Pacific Ocean. The corresponding precipitation over these regions in GPCP are smaller. So, both ECHAM T106 models show the tendency to overestimate the precipitation compared with observations over these regions.
Furthermore, both ECHAM3 and ECHAM4 GCMs appear to overestimate the precipitation in the Pacific ITCZ and the maritime area. The effects of continents and mountain
ranges are clearly shown in the ECHAM GCMs: the precipitation over land tends to be
distribute along the coast line and large mountain ranges. (Risbey and Stone, 1996).
A striking difference in the distribution of precipitation between ECHAM3 and ECHAM4
occurs over the equatorial Pacific Ocean. ECHAM3 shows the zonal dipole feature over
that region, and the SPCZ in ECHAM3 is not well defined. The wet bias in the equatorial
south Pacific Ocean is clearly shown in the zonal mean figure (Figure 4.7). The zonal
mean precipitation in ECHAM3 shows a pronounced second maximum around 80 S, and a
dry belt along the equator. This feature is absent in ECHAM4 and in observations. As we
have discussed in the previous section, this wet bias in ECHAM3 may come from a deficiency in ECHAM3's water vapor advection scheme (the spectral transform scheme).
The global mean total precipitation in ECHAM3 T106 is 2.90 mm/day, and 2.76 mm/day
in ECHAM4 T106. According to Baumgartner and Reichel, the observed global mean
precipitation is 2.70 mm/day. If we refer to the global mean precipitable water discussed
in the previous subsection, the mean residual time of water vapor in the atmosphere is
shorter in ECHAM3 T106 (- 8.75 days) than that in ECHAM4 T106 (- 8.96 days). Generally, ECHAM4 T106 has a better simulation of the precipitation rate.
4.3 Evaporation
In Figure 4.9 and Figure 4.10 we present the global distribution of model simulated annual
mean evaporation. Because the evaporation rate depends on the incoming radiation, temperature, wind speed, humidity, stability of the atmosphere and the availability of water,
among others, it is influenced by local conditions. The evaporation rate may be of little use
in the large scale considerations. We show the figures here in an attempt to point out that,
due to the overestimated surface absorbed shortwave radiation in the tropics and subtropics in ECHAM3, the evaporation in ECHAM3 over these regions is larger than that in
ECHAM4.
Figures 4.9 and 4.10 also show that the oceans undergo greater evaporation than continents, the maximum values occurring in regions of relatively warm temperature.
4.4 Band Mean P-E
After considering the annual mean precipitation and evaporation fields separately, it is useful to compare them and to look at the annual mean net surface water flux P-E. Thus we
present in Table4 a-c, the annual mean band mean precipitation, evaporation, and P-E for
100 latitude bands over land, ocean, and the globe. Also included in the table are the quantities according to Baumgartner and Reichel. The hydrological indices evaporation ratio E/
P and the runoff ratio (P-E)/P are calculated and reported in the table.
The P-E values in the table show an excess of precipitation over evaporation at mid and
high latitudes as well as in the equatorial zone between 100S and 100N globally, while a
deficit of precipitation is found in the subtropics of each hemisphere between about
100and 40" latitude. Over land area, the models and observation all show an excess of precipitation over evaporation at each latitude band. On an annual mean basis, the excess
(deficit) of precipitation in each of the latitude bands must be compensated by the net
meridional divergence (convergence) of water in that band in the form of river outflow and
land runoff over land or flow of water in the oceans.
The runoff ratio (P-E)/P reflects the fraction of the precipitation that is involved in runoff.
The values of the evaporation ratio E/P show clearly the aridity of the subtropical oceans
with the ratios greater than 1, while this ratio is smaller than 1 over all land areas.
It is also interesting to see the different role taken by land and ocean, and northern hemisphere and southern hemisphere in the hydrological cycle. Generally, P-E is always positive over land globally, reflecting the land as a sink of water; the ocean as a whole acts as a
source of water as expected. Northern hemisphere has mean positive P-E values while the
southern hemisphere has negative P-E. Thus, we are led to the conclusion that a flow of
water in the liquid form must take place across the equator from the Northern hemisphere
into the southern hemisphere.
Table 4 a For Land (Unit: mm/year)
Latitude
P
E
D
80-90 0N
70-80 0N
60-70 0N
50-60 0N
40-50 0N
30-40 0N
20-30 0N
10-20 0N
0-10 0N
0-10 0S
10-20 0S
20-30 0S
30-400S
40-50 0S
50-60 0S
60-700S
70-80 0S
80-90 0S
E/P D/P
P
E
D
P
E/P D/P
E
D
218 72
422 136
704 304
680 429
469 363
445 332
510 317
774 547
1785 1065
19901116
1333 839
884 685
646 527
849 520
447 310
407 55
228 29
119 14
145
285
400
251
106
113
193
227
720
874
494
199
119
328
137
351
198
105
33
32
43
63
77
75
62
71
60
56
63
77
82
61
69
14
13
12
67
68
57
37
23
25
38
29
40
44
37
23
18
39
31
86
87
88
141
298
598
671
527
516
500
852
1542
1560
1095
769
647
975
523
352
188
74
38
94
290
437
415
398
343
613
959
1052
804
595
533
604
383
70
19
8
103 27
204 31
307 49
233 65
112 79
118 77
158 68
239 72
583 62
508 67
291 73
174 77
114 82
371 62
140 73
282 20
169 10
11
66
E/P D/P
(%) (%)
(%) (%)
(%) (%)
band
B &R
ECHAM4
ECHAM3
73
69
51
35
21
23
32
28
38
33
27
23
18
38
27
80
90
89
67
213
428
577
535
534
611
846
1724
1956
1184
564
660
1302
993
429
173
73
37
81
201
318
380
412
366
624
1080
1166
890
476
495
388
388
60
33
12
30
132
227
259
155
122
245
222
644
790
294
88
165
914
605
369
140
61
55
38
47
55
71
77
60
74
63
60
75
84
75
30
39
14
19
16
45
62
53
45
29
23
40
26
37
40
25
16
25
70
61
86
81
84
North Hemi.
698
435
263
62
38
681
452
229
66
34
678
435
243
64
56
South Hemi.
993
599
394
60
40
823
562
261
68
32
888
572
316 64
55
Global
846
517
329
61
39
752
507
245
67
33
746
480
266
64
55
Annual mean latitude-band mean precipitation (P), evaporation (E) and P-E (D), the
ratio E/P and ratio D/P for ECHAM3, ECHAM4 and observation.
Table 4 b: For Ocean (Unit: mm/year)
Latitude
P
E
D
80-90 0N
70-80ON
60-70 0N
50-60 0N
40-50 0N
30-40 0N
20-30 0N
10-20 0N
0-10 0N
0-10 0S
10-20 0S
20-300S
30-40 0S
40-50 0S
50-600S
60-700S
70-80 0S
80-900S
E/P D/P
P
E
D
352 64 288
527 219 308
1027 592 435
1209 807 402
1141 889 253
1017 1482 -464
981 1841 -860
1648 1838 -189
19981505 493
1418 1513 -95
1185 1815 -630
798 1696 -898
922 1336 -415
943 814 128
1026 578 448
902 299 604
530 122 408
70
158 88
18 82
42 58
58 42
67 33
78 22
146 -46
188 -88
111 -12
75 25
107 -7
153 -53
213-113
145 -45
86 14
56 44
33 67
23 77
56 44
E/P D/P
220
238 17
385 147 238
876 461 415
1119 669 450
1129 803 326
983 1351-368
814 1696 -881
14201738 -318
21161424 692
14281490 -61
10291723 -694
794 1637 -842
910 1314 -403
1007 825 182
1018 527 491
787 250 537
333
419 86
64
125 61
7 93
38 62
53 47
60 40
71 29
137 -37
208-108
122 -22
67 33
104 -4
167 -67
206-106
144 -44
82 18
52 48
32 68
20 80
48 52
North9Hemi.
1341 11382 -41 103 -3 1265 1275 -10 101 -1
South Hemi.
1035 1220 -185 118 -18
Global
P
E
D
1006 1180 -174 117 -17
1188 1301 -113 110 -10 j 1136 1228 -92 109
-9
E/P D/P
(%) (%)
(%) (%)
(%) (%)
band
B &R
ECHAM4
ECHAM3
43
195
694
1203
1258
931
715
1211
1943
1273
1090
841
906
1124
1001
562
388
-
35
146
455
622
920
1388
1557
1528
1303
1433
1684
1556
1274
877
555
244
104
8
49
239
581
338
-457
-842
-317
640
-160
-594
-715
-368
247
446
318
284
-
-
1160 1198 -38
19
81
75
25
66 34
52 48
27
73
149 -49
218-118
126 -26
67 33
113 -13
154 -55
185 -85
141 -40
78 22
55 45
43 57
27 73
-
-
103 3
996 1160 -164 116 16
1066 1176 -100 110 9
Annual mean latitude-band mean precipitation (P), evaporation (E) and P-E (D), the
ratio B/P and ratio D/P for BCHAM3, BCHAM4 and observation.
Table 4 c: For Globe (Unit: mm/year)
Latitude
P
E
D
80-90 0N
70-80 0N
60-70 0N
50-60 0N
40-50 0N
30-40 0N
20-30N
10-20 0N
0-10 0N
0-100S
10-20 0S
20-30 0S
30-40 0S
40-50 0S
50-600S
60-70 0S
70-80 0S
80-900S
E/P D/P
P
E
D
346 68
491 188
803 394
909 594
804 631
774 993
807 1272
1427 1493
19381404
1548 1426
12201601
819 1463
893 1238
939 805
1025 578
873 288
359 64
130 25
278
303
410
315
174
-219
-466
-67
534
122
-381
-644
-346
135
448
585
295
105
80
20
62
38
51
49
34
65
21
78
128 -28
158 -58
-5
105
28
72
8
92
131 -31
179 -79
139 -39
14
86
44
56
67
33
82
18
81
19
P
E/P D/P
E
D
232
354
683
865
827
784
698
1276
1980
1459
1045
790
881
1006
1018
769
286
85
9 91
22 211
125 229 35 65
343 339 50 50
62 38
538 327
614 213 74 26
121 -21
946 -161
1190-492 171 -71
113 -13
1439 -163
1317 663 67 33
4
96
1394 65
1521 -476 146 -46
1396-607 177 -77
1217-336 138 -38
19
817 188 81
48
528 490 52
68
242 526 32
84
44 241 16
81
19
69
16
1038 953
85
8
92
E/P D/P
(%
M%)
(%%)
(%M%)
band
B &R
ECHAM4
ECHAM3
46
200
507
843
874
761
675
111
1885
143
110
777
875
112
100
549
230
73
10
36
126 74
276 231
447 396
640 234
971 -210
1110-435
128 -167
1250 635
1371 64
1507-398
1305-528
118 -306
862 266
553 450
229 320
54 176
61
12
990
897
73
78
63
54
53
73
128
164
115
66
96
136
168
135
76
55
42
23
16
22
37
46
47
27
-28
-64
-15
34
4
-36
-68
-35
24
45
58
77
84
92
8
North Hemi.
1090 1012 78
93
7
South Hemi.
1031 1104 -73
107
-7
976 1064 -88
109 -9
975
1048 -73 107
7
3
100
0
1007 1009 -2
101
0
973
973
0
0
Global
1061 1058
100
Annual mean latitude-band mean precipitation (P), evaporation (E) and P-E (D), the
ratio E/P and ratio D/P for ECHAM3, ECHAM4 and observation.
4.5 River Runoff
ECHAM GCMs' runoff is calculated in a bucket type surface hydrology scheme as the
amount of water that exceeds the bucket depth in the surface water balance equation. It is a
more sophisticated scheme than the simple bucket scheme (e.g. Manabe, 1969) with a statistical relationship that considers the heterogeneities in a gridbox and their effects on the
soil saturation. In this section, a GISS river-runoff routine was used to calculate for
ECHAM3 and ECHAM4 T106 GCMs the corresponding annual mean river flow of the
world's major rivers, the land runoff not related to a river, and a comparison with others'
studies.
The GISS river model includes 44 major rivers, and runs at a 144 (longitude) x 90 (latitude) grid. The river runoff data in ECHAM3 and ECHAM4 were interpolated linearly to
the GISS grid. The comparisons of river runoff calculations are reported in Table 5 (see in
page 102), where both observations and model calculations are included. Perry et al
(1996) compiled an extended data set of river discharges including 981 rivers from
approximately 49 sources including B & R data listed here. We will use it here as a reliable observational data set. The GRDC (Global Runoff Data Centre) dataset (1994) is a
dataset of discharges for the 20 largest rivers based on observations at hydrological stations. The GFDL (Geophysical Fluid Dynamics Laboratory) data is the calculation based
on 25 years (1965-1989) observational data from GFDL Atmospheric Circulation Tape
Library by Cecelia Deluca (Master Thesis, MIT, 1996).
Our calculation of river runoff for ECHAM3 and ECHAM4 is presented in Table 5. When
compared with Perry et al. (1996), both ECHAM3 and ECHAM4 show some problems in
reasonably simulating the global river runoff. Among the 44 rivers whose name can be
identified in Perry et al. 1996, ECHAM3 overestimates the total runoff (=2.08 x 104 km3 /
yr) while ECHAM4 underestimates it (=1.51 x 104 km3 /yr). The Perry et al. (1996) compiled observational value for these rivers is 1.68 x 104km3/yr. This may also suggest that
there is water loss in ECHAM4 as we discussed in the precipitable water calculation. On
the other hand, the underestimation in ECHAM4 may not come from the model itself, but
rather come from the interpolation which smears out the gradient and reduces the peak
values (Risbey and Stone, 1996).
It is interesting to point out that, while many rivers (31) decrease their runoff from
ECHAM3 T106 to ECHAM4 T106, there are still 13 rivers which increase their runoff in
ECHAM4. These 13 rivers are all in the northern hemisphere, mainly in southern and
southwestern Eurasia.
4.6 Atlantic Freshwater Fluxes
Table 6a to 6c (in pages 103 -- 105) show the Atlantic freshwater budget due to its importance for forcing the oceanic circulation. Studies have suggested that oceans possess multiple equilibrium states with transients prompted by changes in oceanic freshwater budget
(Marotzke et al. 1991). The change of freshwater input to the North Atlantic Ocean may
induce abrupt climate change through the intensified or weakened thermohaline circulation (THC) (Manabe et al. 1995).
The calculation of annual mean freshwater flux into the Atlantic Ocean is divided into
three components: river runoff, land runoff which is the runoff from land not associated
with any river basin, and the precipitation minus evaporation (i.e., net water flux) over the
ocean surface. The results are listed in Tables 6a and 6b for ECHAM3 and ECHAM4
respectively. The comparison of the calculation from the ECHAM models with B & R
(1975), and GFDL (Cecelia's calculation) is presented in Table 6c.
Our conclusion is that, at latitudes north of 40 0 N, the calculations of ECHAM GCMs are
in fairly good agreement with other studies. Over other regions, the ECHAM models have
problems in reasonably simulating the freshwater flux.
Chapter 5
Summary
Diagnostic studies of the MPI ECHAM3 and ECHAM4 T106 AGCMs have been carried
out and the analyses have been presented. The models' simulation of surface heat balance
and surface hydrological forcing fields has been discussed. Their comparison with available observations has also been presented. For the purpose of assessing the model's behavior with the modifications from ECHAM3 to ECHAM4, some strengths and possible
weaknesses in ECHAM4 T106 have been suggested. The main results are summarized as
follows.
-
Surface absorbed shortwave radiative flux has been greatly improved in ECHAM4.
This improvement comes from the improved radiative scheme and shortwave absorption calculation method applied in ECHAM4. ECHAM3 tends to overestimate the surface absorbed shortwave radiation up to 22.5W/m 2 when globally averaged, which
stems from the model's overestimation of incident solar radiation at the surface.
-
The improved radiative scheme and improved longwave absorption by water vapor in
ECHAM4 T106 improve the model's calculation of surface longwave upward radiative flux. ECHAM3 T106 tends to underestimate the surface longwave downward radiation by about 15.2W/m 2 . From the view of the net surface radiative flux, ECHAM3's
overestimation of surface absorbed SW radiation is compensated by the underestimation of surface longwave downward radiation. This compensation feature is common
in GCMs.
-
The overestimation of surface absorbed SW radiation is further compensated by the
larger net surface latent heat (LH) flux in ECHAM3. The global mean imbalance of
the net surface heat flux is 2.1 W/m2 in ECHAM4 and 3.7W/m2 in ECHAM3.
ECHAM4 is "superior" to ECHAM3 in simulating the mean surface heat fluxes. A
possible reason for the ECHAM3's deficiency in surface radiative fluxes is the models'
underestimate of total cloud cover.
-
The analysis of the ECHAM3 and ECHAM4 T106 implied total oceanic heat transport
shows a larger heat transport in ECHAM3 than in ECHAM4 at almost every latitude.
The difference between the two models is greater in the southern oceans than in the
northern oceans. Comparison with other available analyses suggests that ECHAM3
T106 gives an upper bound while ECHAM4 T106 gives a lower bound for the calculation of the oceanic heat transport.
-
The "hybrid" oceanic heat transport proposed by Gleckler et al which includes the
effect of errors in cloud radiative forcing for both ECHAM3 and ECHAM4 T106
models does not provide a satisfactory explanation for the large errors in the estimates
of oceanic heat transport by AGCMs. Although this cloud radiative forcing correction
scheme reconciles the ECHAM3 and ECHAM4 T106 models, the corrected heat
transport still suffers from a relatively large discrepancy with the available analyses.
-
The role of the ocean basins in meridional heat transport are very different. Generally,
the Atlantic Ocean transports heat northward in both hemispheres. The Indo-Pacific
Ocean transfers more heat poleward from the tropics in the southern hemisphere
oceans than in the northern hemisphere oceans. The largest heat transport occurs at
around 200 degrees latitude in both ocean basins and in both hemisphere.
-
The analysis of the oceanic heat transport with the interannual variations in the lower
boundary conditions included and a lower horizontal resolution (T42) in ECHAM4
Gisst AGCM suggests that the total oceanic heat transport estimate for the northern
hemisphere oceans is better while that for southern hemisphere oceans is worse. The
ECHAM4 Gisst T42 oceanic heat transport estimate for the global ocean in the southern hemisphere lies outside the range set by ECHAM3 T106 and ECHAM4 T106.
This estimate is highly inaccurate. For ocean basins, the estimate for the Atlantic
Ocean is improved compared with the Trenberth analysis. For the Indo-Pacific, generally, the calculation and comparison suggest a worse result.
ECHAM3 T106 shows a profound dry zone in the equatorial Pacific Ocean and a wet
bias in the southern Pacific subtropics in its simulation of precipitable water and precipitation. These patterns are absent in ECHAM4. The advection scheme of water
vapor and cloud water has been changed from spectral transform scheme to the SLT
scheme in ECHAM4. The SLT scheme is not inherently conservative. We need to pay
attention to mass conservation in ECHAM4 T106.
-
The calculation of 10-degree latitude band mean P-E shows that, the land area is a sink
of water, and ocean is a source of water. The northern hemisphere, on an annual basis,
is a sink of water while the Southern hemisphere is a source. There is a net transport of
water in liquid form across the equator into the Southern hemisphere.
-
Both ECHAM3 and ECHAM4 T106 GCMs did not simulate the river runoff very well
when compared to observations. This may be due to the models' inaccurate simulation
of the precipitation distribution on the regional scale. In ECHAM4, most rivers reduce
their runoff values from those in ECHAM3, while 30% (13 out of 44) rivers increase
their runoff. The total runoff in ECHAM4 is smaller that in ECHAM3 and observations. ECHAM4 has the smaller departure from the observations.
-
The calculation of the Atlantic freshwater fluxes indicates that in both models the sim-
ulations are in fairly good agreement with the observations at the latitudes north of
40 0N, i.e., in the northern North Atlantic. Over the total Atlantic ocean basin, neither
ECHAM4 nor ECHAM3 gave a reasonable simulation of the freshwater budget.
Chapter 6
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Appendix
Oceanic Heat Transport by Surface Heat Flux Components
From equation (7) we have:
To(go)
=
2ta
-2
2 [Fs]cosepdp
(A.1)
fTo
The total oceanic heat transport T0(9 0) is calculated from the annual mean net heat flux
[Fs] at the ocean surface. If we break down the [Fs] in equation (A. 1):
(A.2)
[Fs] = [Rs] + [LH] + [SH]
and further break down the radiation flux [Rs] into:
[Rs] = [Fm] + [F
=
Fown-
]
Fs] + [Fdown -F
]
(A.3)
Substitute (A.2), (A.3) into (A. 1), we can calculate T0((P0 ) from all these flux components.
By carrying out the calculation, we would like to investigate, for example, how much heat
would be transferred meridionally by the latent heat flux component alone. We are also
interested in seeking their roles in the heat transport in ocean basins. The calculations are
presented in the figures from Figure A. 1 to Figure A.7 for the total heat flux, the net shortwave radiation, the downward longwave radiation, and latent heat flux. Our study suggests
that, the incoming shortwave radiation, the downward longwave radiation and latent heat
flux are the most important flux components that carry most of the total oceanic heat transport meridionally.
(a)
Annual Net Radiation Flux at Ocean Surface (W/mA2) ECHAM3
40
-5
100
100
-20
-0
-0
60
5 120
180
Longitude
240
300
360
Annual Net Radiation Flux at Ocean Surface (W/mf ECHAM4
(b)
80 -
-0
--
O
6040
-201510
010
-40-
100~
--
0 50
---50-
-60 -80
0
60
120
180
Longitude
240
300
3630
Figure 3.2 Annual mean net radiation flux at ocean surface for ECHAM3 and
ECHAM4 T 106.
Zonal Mean Annual Net Radiation Flux at Ocean Surface ECHAM3 T1 06
-50
-80
-60
-40
-20
0
Latitude
20
40
60
80
Zonal Mean Annual Net Radiation Flux at Ocean Surface ECHAM4 T1 06
-50
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure 3.3 Annual mean zonal mean net radiation flux at ocean surface for ECHAM3
and ECHAM4 T106.
(a)
Difference of Ocean Surface Net Radiation Flux ECHAM4-ECHAM3
80
60
40
20
-20
-40
-60
-80
60
(b)
120
180
Longitude
240
300
360
Diff. of Zonal Annual Net Radiation Flux at Ocean Surface (ECHAM4-ECHAM3)
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure 3.4 Difference of net radiation flux at ocean surface between ECHAM3
and ECHAM4 T106, for annual mean and zonal mean respectively.
Annual Absorbed Short-Wave flux at Ocean Surface (W/mA2) ECHAM3
-20
-40
100
100
100
-60
-80
Izu
S13u
Longitude
(b)
Annual Absorbed Short-Wave flux at Ocean Surface (W/mA2) ECHAM4
-20
-40
120
180
Longitude
Figure 3.5 Annual mean absorbed short-wave radiation at ocean surface for
ECHAM3 and ECHAM4 T106.
Zonal Mean Annual Absorbed Shortwave Flux at Ocean Surface ECHAM3 T1 06
250
-80
-60
-40
-20
0
Latitude
20
40
60
80
Zonal Mean Annual Absorbed Shortwave Flux at Ocean Surface ECHAM4 T106
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure 3.6 Annual mean zonal mean absorbed short-wave radiation flux at ocean
surface for ECHAM3 and ECHAM4 T106.
Difference of Ocean Surface Absorbed Short-Wave Flux ECHAM4-ECHAM3
(a)
-4
-4
0
60
180
120
240
300
360
Longitude
Diff. of Zonal Annual Absorbed Shortwave Flux at Ocean Surface (ECHAM4-ECHAM3)
(b) 10
0*
-1 0
. . . . . ... . . . .
-200
-30
-40
-50
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure 3.7 Difference of absorbed short-wave radiation flux at ocean surface
between ECHAM3 and ECHAM4 T106, for annual mean and zonal mean.
Annual Downward Long-Wave flux at Ocean Surface (W/mA2) ECHAM3
80
(a)
604
i TZU
Longitude
zou
300
Annual Downward Long-Wave flux at Ocean Surface (W/mA2) ECHAM4
80
(b)
60
tju
Longitude
Figure 3.8 Annual mean downward longwave radiation flux at ocean surface
for ECHAM3 and ECHAM4 T106.
Zonal Mean Annual Downward Longwave Flux at Ocean Surface ECHAM3 T1 06
E
250 -
-
-
100 - -.
150-
100
.
0
-80
-40
-60
-20
0
Latitude
20
40
60
80
Zonal Mean Annual Downward Longwave Flux at Ocean Surface ECHAM4 T106
450
(b)
--
400350 300 -CI
E
250 -
--
100
-
C
-
50-
50- -
0
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure 3.9 Annual mean zonal mean downward longwave radiation flux at ocean
surface for ECHAM3 and ECHAM4 T106.
(a)
Difference of Ocean Surface Downward Long-Wave Flux ECHAM4-ECHAM3
.I
I
10
Longitude
Diff. of Zonal Annual Downward Longwave Flux at Ocean Surface (ECHAM4-ECHAM3)
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure 3.10 Difference of downward long-wave radiation flux at ocean surface between
ECHAM3 and ECHAM4 T106, for annual mean and zonal mean.
Annual Mean Planetary Albedo (%)
(a)
ECHAM3 T106
80
60
40
20
a>
0
-
-20
-40
-60
-80
0
120
60
180
Longitude
Annual Mean Planetary Albedo (%)
(b)
240
300
360
Global mean: 33.35%
ECHAM4 T1 06
80
60
40
4O
3
*o.3
20
-20
-40
-60
03
0. 4
0.
.........
-80
1
eu
I
su
240
300
360
Longitude
Global mean: 32.43%
Figure 3.11 Annual mean planetary albedo for ECHAM3 and ECHAM4 T106.
0.8
0.7-
0.5
-
.
..
-.
--
0.3 --
0.2-
-80
I
I
-60
-40
I
-20
0
Latitude
20
I
1
40
60
80
Annual Zonal Mean Planetary Albedo (%) ECHAM4 T106
0.8-
(b)
0.6 --
0.4 --
0.3-
0.2-
-80
I
I
-60
-40
I
-20
0
Latitude
1
20
40
60
80
Figure 3.12 Annual mean zonal mean planetary albedo for ECHAM3 and ECHAM4.
Difference of Annual Mean Planetary Albedo (ECHAM4 - ECHAM3)
(a)
80-
60-*-
40
20
0.02
-20
o0
-
-40--
-t
-60
-0.05
-80
0ii 0
50
10
20
0
O
250
300
350
Longitude
Difference of Annual Zonal Mean Planetary Albedo (ECHAM4-ECHAM3)
(b)
0 .0 2 - -
-.-
-.- -.--.- --.-
--.- - - - --.- - --.-
-..
-..
. . . ..- .
.
-- .--.-.-.
-.-.. .. . .
--.- .
--.-. ..
-.-- - .
-0.14
-0.12
-80
~60
-40
-20
0
Latitude
20
40
60
80
Figure 3.13 Difference of planetary albedo between ECHAM3 and ECHAM4 T 106,
for annual mean and zonal mean.
Annual Mean Total Cloud Cover at Ocean Surface (%) ECHAM3
(a)
C>
-o
1zu
L oU
.5UU
Longitude
Annual Mean Total Cloud Cover at Ocean Surface (%) ECHAM4
(b)
804
604
120
180
Longitude
240
3o
Figure 3.14 Annual mean total cloud cover over ocean for ECHAM3 and
ECHAM4 T106.
360
Zonal Cloud Cover at Ocean Surface (%) ECHAM3
80
70
60
50
0
040
0
20
80
20
10
0
-80
-60
-40
-20
0
20
40
60
80
60
80
Latitude
Zonal Cloud Cover at Ocean Surface (%) ECHAM4
80
70
60
50
-
8-0
040
0
05
30
20
10
0
-80
-60
-40
-20
0
20
40
Latitude
Figure 3.15 Annual mean zonal mean total cloud cover over ocean for ECHAM3
and ECHAM4 T106.
Difference of Annual Total Cloud Cover at Ocean Surface ECHAM4-ECHAM3
(a)
80
60
40
20
0
-20
-40
-60
-80
60
120
180
240
300
Longitude
Difference of Zonal Ocean Surface Cloud Cover ECHAM4-ECHAM3
(b)
-80
-60
-40
-20
0
20
40
60
80
Latitude
Figure 3.16 Difference of total cloud cover over ocean between ECHAM3 and
ECHAM4 T106, for annual mean and zonal mean.
Figure 3.17 ISCCP-C2 8 year mean total cloud amount observation.
Annual Latent
(a)
60-
15
20
0D1
-20-
100
-100
9
-60
ADs
0
60
120
180
240
36 0
300
Longitude
Annual Latent Heat Flux at Ocean Surface (W/mA2) ECHAM4
(b)
~0
60
-50
-100
40
--
*
soo
-501
-50
-0
-80
0
-6
0
60
120
180
240
-
300
36
Longitude
Figure 3.18 Annual mean latent heat flux at ocean surface for ECHAM3 and
ECHAM4 T106.
Zonal Mean Annual Latent Heat Flux at Ocean Surface ECHAM3 T1 06
-80
-60
-40
-20
0
Latitude
20
40
60
80
Zonal Mean Annual Latent Heat Flux at Ocean Surface ECHAM4 T1 06
(b)
Cj
E
§E
L-i
r-1
-1
-1
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure 3.19 Annual mean zonal mean latent heat flux at ocean surface for ECHAM3
and ECHAM4 T106.
Difference of Ocean Surface latent Heat Flux ECHAM4-ECHAM3
(a)
60
m
0
*&
o
60
120
240
180
300
360
Longitude
160
Duff, of Zonal Annual Latent Heat Flux at Ocean Surface (ECHAM4-ECHAM3)
14
(b)
0 -
8
-0 --
4 -
--
8 -
12-
0
-8
- - -
- -- -- - - - -- -
-80
-60
-40
-
- -3-6-0---
-- -- --
-
.. . - -..
-20
-
--
. -.
0
Latitude
-
-o-g-
-....-.. -.-..
20
- -
0
0
40
-t--e
-.. -...-.
..-...-..
60
80
Figure 3.20 Difference of latent heat flux at ocean surface between ECHAM3 and
ECHAM4 T106, for annual mean and zonal mean.
Annual Net Heat Flux at Ocean Surface (W/mA2) ECHAM3
0
60
120
180
240
300
Longitude
Annual Net Heat Flux at Ocean Surface (W/mA2) ECHAM4
120
180
Longitude
Figure 3.21 Annual net heat flux at ocean surface for ECHAM3 and ECHAM4
T106 models.
Annual Net Heat Flux at Ocean Surface ECHAM3 T106
-80
-60
-40
-20
0
Latitude
20
40
60
80
Annual Net Heat Flux at Ocean Surface ECHAM4 T106
-80
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure 3.22 Annual mean zonal mean net heat flux at ocean surface for ECHAM3
and ECHAM4 T106.
Difference of Ocean Surface Heat Flux ECHAM4-ECHAM3
(a)
0
60
120
180
Longitude
240
300
360
Diff. of Zonal Annual Net Heat Flux at Ocean Surface (ECHAM4-ECHAM3)
(b)
-10
-20
-30
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure 3.23 Difference of net heat flux at ocean surface between ECHAM3 and
ECHAM4 T106, for annual mean and zonal mean.
25-year Mean Net Radiation Flux at Ocean Surface(W/mA2) ECHAM4 Gisst
-20
-40
-60
-80
60
120
180
Longitude
240
300
360
Zonal 25 Year Mean Net Radiation Flux at Ocean Surface ECHAM4 GISST
(b)
E
-50
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure 3.30 25 year mean net radiation flux at ocean surface for ECHAM4 Gisst
T42 model, both annual mean and zonal mean.
25-year Mean Absorbed Shortwave Radiation at Ocean Surface(W/mA2) ECHAM4 Gisst
(a)
I
I
I
I
I
60
120
180
Longitude
240
300
20
0
-20
-40
-60
-80
360
Zonal 25 Year Mean Absorbed Shortwave Flux at Ocean Surface ECHAM4 GISST
250
(b)
200
,150
1
LL 100
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure 3.31 25 year mean absorbed shortwave radiation flux at ocean surface for
ECHAM4 Gisst T42, both for annual mean and zonal mean.
25-year Mean Downward Longwave Radiation at Ocean Surface(W/mA2) ECHAM4 Gisst
(a)
80~
603035
40
0
20
40
0
-20
-40--60--
-80
60
0
120
240
180
Longitude
300
360
Zonal 25 Year Mean Downward Longwave Flux at Ocean Surface ECHAM4 GISST
450
(b)
400
350
-
-
-
-
-
-.-
300
~250
-
----
200
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure 3.32 25 year mean downward longwave radiation flux at ocean surface for
ECHAM4 Gisst T42, both for annual mean and zonal mean.
25 Year Mean Planetary Albedo (%) ECHAM4 Gisst
-20
-40
-60
-80
60
0
120
240
180
Longitude
300
360
Global mean: 32.3%
25 Year Mean Zonal Mean Planetary Albedo (%) ECHAM4 Gisst
0.8
(b)
0.7
0.6
0.5
0.4
0.3
0.2
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure 3.33 25 year mean planetary albedo for ECHAM4 Gisst T42, both for annual
mean and zonal mean.
25 Year Mean Global Total Cloud Cover (%) ECHAM4 Gisst
(a) 80
60
40
20
-o
0
-20
-40
-60
-80
50
0
100
150
200
250
Longitude
UU
35U
Global mean: 59.63
Zonal 25 Year Mean Total Cloud Cover ECHAM4 GISST
1
(b)
0.9
0.8
0.7
o-0.6
a>
30.5
60.4
0.3
0.2
0.1
n
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure 3.34 25 year mean total cloud cover for ECHAM4 Gisst T42, both for annual
mean and zonal mean.
25-year Mean Latent Heat Flux at Ocean Surface(W/mA2) ECHAM4 Gisst
(a)
8
6
4
2
CD
-0
-2
-4
-6
-8
180
Longitude
120
60
0
300
240
360
Zonal 25 Year Mean Latent Heat Flux at Ocean Surface ECHAM4 GISST
(b)
E
-1
-15(
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure 3.35 25 year mean latent heat flux at ocean surface for ECHAM4 Gisst T42,
both for annual mean and zonal mean.
25 Year Mean Global Net Heat Flux at Ocean Surface (w/mA2) ECHAM4 Gisst
80::
60
40
20
0-20
-40-60-80
0
60
120
180
240
Longitude
300
36C
Global mean: 0.14 W/mA2
Zonal 25 Year Mean Net Heat Flux at Ocean Surface ECHAM4 GISST
50
(b)
40
30
20
.
. . . . --. ...
.
.. --.
-----.....---...
--
. .....
.
. . .
--------.....
---. .-.....
--. .
. .
-.---.-....
.
.
.
10
. . . . ... . .
-... . . . ... . . . ... .--
. .. . . . . .
. . -... . .
. . ... . . . ... . . . . .. . . . .... .-.
..
. ..
..
. .. .. .
...-.--..
.
10
- - - -.
.. ..
-.. ..- .
-.--.--
-.
. ...
.. ..
--
-.
.---.- .-.-..- -
-.--
-20
-30
-40
-50
-80
-60
-40
-20
0
20
40
60
80
Latitude
Figure 3.36 25 year mean net heat flux at ocean surface for ECHAM4 Gisst T42,
both for annual mean and zonal mean.
Surface LW downward (w/m2 )
Surface Absorbed SW (w/m2)
2001O
1
1
owl
(a)
I
I
I
I
(b)
173
164
142
0
14
mr
149
0
I
0
-
|
0
Figure 3.37 Comparison of simulation of the global mean shortwave radiation flux
absorbed at the surface (in (a)), and the downward longwave radiation flux at surface
(in (b)) among AGCMs. Reference to Figure 2.1.
Annual Oceanic Meridional Heat Transport
1.5 -
i
0.5 0
0.
ECHAM4 Gisst
-1
-1.5
-
-
-
ECHAM4 T106
-
-80
-60
-40
-20
0
20
40
60
80
Latitude
Figure 3.38 Annual mean meridional northward total oceanic heat transport for
ECHAM4 Gisst T42 and ECHAM4 T106.
25 Year Mean Atmospheric Meridional Heat Transport
-80
-60
-40
-20
0
20
40
60
80
Latitude
Figure 3.39 25 year mean northward total atmospheric heat transport for ECHAM4
Gisst T42.
Annual Heat Transport at the Top of Atmosphere
-
/
ECHAM4 Gisst
-
ECHAM4 T106
-
-60
-40
-20
ERBE Observation
1
I
1
0
20
40
I
-80
-
|
60
Latitude
Figure 3.40 Annual mean total heat transport at the top of the atmosphere. Solid line
is the 25 year mean for ECHAM4 Gisst T42, dashed line is for ECHAM4 T106, and
dash-dotted line is for ERBE observation.
"Hybrid" Oceanic Heat Transport of ECHAM4 Gisst
-0.5
-1
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure 3.41 Annual mean "Hybrid" oceanic heat transport for ECHAM4 Gisst T42
calculated from EABE observation.
100
-
25 Year Mean Oceanic Meridional Heat Transport Atlantic
ECHAM4 Gisst
(a)
00Co)
C
-80
-60
-40
-20
0
Latitude
20
40
25 Year Mean Oceanic Meridional Heat Transport Indo-Pacific
60
80
ECHAM4 Gisst
(b)
0
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure 3.42 25 year mean meridional northward oceanic heat transport for ocean
basins: Atlantic Ocean and Indo-Pacific Ocean.
101
1
Table 5
River name
Amazon
Brahmaputra-Ganges
Zaire (Congo)
Danube
Yenisey
Fraser
Magdalena
Hsi Chiang (Xi)
Indus
Mackenzie
Lena
Mississippi
Niger
Ob
Lapelled (Parana)
Orinoco
Irrawaddy
St. Lawrence
Yellow
Yukon
Mekong
Columbia
Amur
Yangtze
Zambesi
Caspian Sea
Elbe
San Francisco
Rio Grande
Norton
Indigirka
Norwegian River
Kolyma
Loire
Murray
Nile
Colorado
Orange
Rhine (Rhein)
Severnaya Dvina
Tigris Euphrates
Volga
Godavari
Limpopo
Comparison of River runoff calculations (Unit: km 3/year)
ECHAM3 ECHAM4
8101
529
1497
81
571
207
529
239
50
282
531
344
546
456
1138
1268
253
187
95
310
633
322
147
1048
552
73
39
191
14
81
67
195
207
14
29
220
12
93
69
157
37
235
30
108
4006
582
1251
150
509
177
454
154
82
292
497
309
652
308
928
798
211
238
162
288
489
196
135
857
372
92
36
141
20
70
57
164
174
10
24
433
12
56
75
96
49
195
61
57
Perry et al
(St.Dev.)
6001
1058
1268
198
571
112
228
299
189
281
507
507
172
396
510
963
418
356
42
199
462
232
317
898
128
(400)
(109)
(46)
(4)
(31)
(7)
(14)
(59)
(35)
(33)
(12)
(65)
(18)
(17)
(47)
(119)
(15)
(46)
(5)
(17)
(58)
(14)
(16)
(111)
(65)
GRDC
(t
6000
1120
1330
4901
1000
1269
565
558
4839
445
-42
-10
323
102
519
172
-376
206
349
120
-287
455
249
1404
187
83
-198
290
314
135
-24
772
279
-216
-11
-827
-65
112
10
110
69
39
-310
49
70
-289
-14
68
-524
291
-103
-132
395
615
915
440
330
267
524
464
35
394
516
980
258
250
500
294
350
1100
307
789
105
515
560
96 (11)
51 (3)
93 (23)
57 (24)
39
70 (2)
106 (3)
255
102
GFDL
e.
B &R
(1058)
(295)
(636)
(55)
(119)
(27)
(109)
(116)
(128)
(82)
(109)
(271)
(213)
(218)
(714)
(326)
(112)
(58)
(78)
(49)
(214)
(73)
(182)
(186)
(231)
(72)
(17)
(185)
(55)
(92)
(27)
(48)
(45)
(23)
(154)
(452)
(72)
(164)
(33)
(45)
(165)
(108)
(181)
(85)
Table 6.a Atlantic Freshwater Fluxes (km3/year) (ECHAM3 T106)
River Runoff Land Runoff
Latitude
------------------------------------------------90
0.0
0.0
86
0.0
9.0
82
0.0
78
0.0
Total
89.4
89.4
279.7
288.7
40.0
452.3
492.3
65.5
670.2
735.7
2606.4
660.3
289.0
1657.1
74
Ocean P-E
85.7
941.3
399.8
70
455.8
66
156.6
175.7
610.2
62
276.1
216.4
608.3
942.5
1100.8
58
39.2
247.0
473.8
760.0
54
69.1
355.0
454.3
878.4
163.1
443.8
807.8
140.4
256.4
200.9
50
46
0.0
42
0-.0
84.2
396.8
-515.2
-431.0
4883.3
9609.1
----------------------------------->
1871.0
2854.8
40
-----------------------------------38
0.0
34
0.0
357.5
30
90.7
-1177.1
-1086.4
10.1
-1577.3
-1209.7
37.1
-1677.7
-1640.6
-1279.3
26
0.0
22
0.0
144.7
-1424.0
18
0.0
191.9
-1169.0
0.0
10
545.8
6
8101.4
2
-977.1
.
-635.6
328.3
1797.5
14
-853.9
-962.9
109.0
1490.2
918.0
959.4
1877.4
567.0
306.3
1419.1
289.8
-35.5
8355.7
-----------------------------------6095.2
-7393.4
2686.6
10802.0
40
to
0
------------------------------------
0.0
-6
-1625.5
-1955.3
329.8
270.0
-2231.8
-1770.6
-14
0.0
344.2
-2125.8
-1781.6
-18
0.0
186.1
-1950.0
-1763.9
-22
0.0
-1618.3
-1559.6
-1249.6
-1038.2
191.2
-10
92.9
-26
0.0
-30
1138.0
-34
0.0
-38
-40
1121.1
-848.1
471.6
1497.6
-2
to
118.5
132.1
-823.6
-955.7
94.3
-811.3
82.4
-253.4
421.0
-173.0
-8993.9
-14001.3
2087.7
2919.7
0
58.7
-----------------------------------339.6
404.8
745.9
877.3
910.8
932.4
9.4
857.5
866.9
0.0
0.0
873.8
873.8
-62
0.0
0.7
809.9
810.6
-66
0.0
0.0
653.0
653.0
-70
0.0
0.0
262.2
262.2
-74
0.0
0.0
-78
0.0
0.0
1.2
1.2
-82
0.0
0.0
0.0
0.0
-86
0.0
0.0
0.0
0.0
-42
0.0
-46
0.0
-50
0.0
-54
0.0
-58
65.2
131.4
21.6
93.3
93.3
------------------------------------90
to
-40
to
0.0
-40
90
16576.5
228.3
6645.3
103
5547.2
-16511.1
5775.5
6710.7
Table 6.b Atlantic Freshwater Fluxes (km3/year) (ECHAM4 T106)
Latitude
River Runoff
Land Runoff
Ocean P-E
Total
90
86
82
78
74
70
66
62
58
54
50
46
42
0.0
0.0
0.0
0.0
1529.3
307.8
96.5
234.7
36.4
74.5
247.7
0.0
0.0
0.0
7.9
38.9
47.5
229.0
92.5
184.0
205.6
284.4
394.6
137.1
139.0
57.6
63.7
208.2
352.0
497.2
487.5
351.6
554.9
633.1
522.9
443.9
421.7
256.7
-461.7
63.7
216.1
390.9
544.7
2245.8
751.9
835.4
1073.4
843.7
913.0
806.5
395.7
-404.1
> 40
2526.9
1818.1
4331.7
8676.7
38
34
30
26
22
18
14
10
6
2
0.0
0.0
329.0
0.0
0.0
0.0
1251.8
0.0
652.3
4006.4
46.5
46.1
15.5
46.1
104.0
151.6
531.7
987.5
408.6
152.6
-898.7
-1167.5
-1786.0
-2036.0
-1748.3
-1587.4
-1210.7
745.8
393.7
-141.2
-852.2
-1121.4
-1441.5
-1989.9
-1644.3
-1435.8
572.8
1733.3
1454.6
4017.8
0 to 40
6239.5
2490.2
-9436.3
-706.6
-2
-6
-10
-14
-18
-22
-26
-30
-34
-38
1251.4
0.0
141.1
0.0
0.0
0.0
55.8
0.0
928.4
0.0
97.6
91.5
143.7
222.9
189.4
48.2
155.5
141.4
62.0
89.2
-1088.0
-1721.3
-1889.7
-1802.7
-1660.3
-1397.1
-1054.7
-881.7
-758.9
-136.6
261.0
-1629.8
-1604.9
-1579.8
-1470.9
-1348.9
-843.4
-740.3
231.5
-47.4
2376.7
1241.4
-12391.0
-8772.9
-42
-46
-50
-54
-58
-62
-66
-70
-74
.-78
.- 82
-86
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
83.2
131.0
14.4
5.8
0.0
0.4
0.0
0.0
0.0
0.0
0.0
0.0
486.5
907.2
1044.6
922.9
828.0
667.3
499.0
196.1
64.8
0.8
0.0
0.0
569.7
1038.2
1059.0
928.7
828.0
667.7
499.0
196.1
64.8
0.8
0.0
0.0
-90 to -40
0.0
234.8
5617.2
5852.0
11143.1
10434.3
-17495.6
4081.8
-40 to 0
-40 to
90
104
Table 6 c: Comparison of calculation of Atlantic freshwater fluxes
> 40
0 to 40
Runoff P-E Total
Runoff
-40 to 0
P-E Total
Runoff
-40 to 90
P-E Total
Runoff P-E Total
ECHAM3
4726
4883
9609
13489 -7393
6096
5007 -14001
-8994
23222 -16511
6711
ECHAM4
4345
4332
8677
8730
-707
3618 -12391 -8773
21577 -17496
4082
B &R
4634
2152
6786
6771 -19911
-13140
7357 -23816 -16459
18762 -41575 -22813
GFDL
5411
4650 10061
5951
-2340
-9436
-8291
-6740 -13139
-19880
4623 -16780 -12157
Precipitable water vapor (ECHAM 3) (mm)
-0
O0-
-20 -
34020
-40- -02
-60
--
- - --
-- - - - - - -
01
-80
O
60
120
180
240
300
Longitude
Figure 4.1 Annual mean precipitable water vapor for ECHAM3 T106.
Contour interval 5 mm.
Precipitable water vapor (ECHAM 4) (mm)
Longitude
LOn
Figure 4.2 Annual mean precipitable water vapor for ECHAM4 T106.
106
Annual mean zonal mean pecipitable water (mm)
45
36
30
.
. .
...........
. . .
.
.
....
.........
...........
....
...
EC
...
EC
HA M 3:
--So
-60o
1
.. .
..
.
ECHAM
0
AM
-4
-20
4:
.
.
. . .
.
.
.
. . .
.
. .
.
1.26E+16-
0
20
40
so
s
Latitude
Figure
and
4.3
Annual
ECHAM4
mean
T
zonal
mean
precipitable water for
both
ECHAM3
106.
Precipitable water (NVAP)
Annual 1988-92 mean
(mm)
90N
60N
30N
0
30S
60S-
90S0
30E
60E
1 1 1 T
-I
90E
120E
150E
180
150W 120W
90W
60W
TT*
30W
r-r--1
-
0
P.....I....l
0
CONTOUR INTERVAL 5
Figure 4.4 NVAP analysis of annual mean precipitable water, with zonal mean.
107
30
so
Total Precipitation (mm/d) (ECHAM3)
-80
0
60
120
180
Longitude
240
300
360
Figure 4.5 Annual mean total precipitation for ECHAM3 T106. Contour
interval 2 mm/d.
Total Precipitation (mm/d) (ECHAM4)
180
Longitude
240
300
Figure 4.6 Annual mean total precipitation for ECHAM4 T106.
108
360
Annual mean zonal mean precipitation (mm/d)
8
ECHAM3
ECHAM4
6
5 -
-
4
..
10
--
-
- - -- -- -
.. . .
. . . . .
-
-80
---
-
-60
-
-
-
-
-
-
- ---
. . . . .
-----
-40
-20
----
---
--------
0
Latitude
20
---
40
-
-
60
-
-.
80
Figure 4.7 Annual mean zonal mean total precipitation for both ECHAM3
and ECHAM4 T106.
Precipitation (GPCP)
Annual 1979-95 mean
(mm-day1 )
90N
60N
30N
0
30S
60S.
90S0
30E
60E
90E
120E
150E
180
150W
120W
90W
60W
30W
0
a
CONTOUR INTERVAL 2
Figure 4.8 Annual mean total precipitation by Global Precipitation Climatology
Project (GPCP)
109
4
a
Annual mean evaporation (mm/d) (ECHAM3)
Uizu
i130
240
300
360
Longitude
Global mean evaporation: 2.91 mm/d
Figure 4.9 Annual mean evaporation for ECHAM3 T106. Contour interval
1 mm/d.
Annual mean evaporation (mm/d) (ECHAM4)
u
Longitude
1g
Global mean evaporation: 2.77 mm/d
Figure 4.10 Annual mean evaporation for ECHA T106.
110
Moisture Flux (g/mA2 s) (ECHAM3)
-20
-40
-60
-80
60
120
180
Longitude
240
300
360
Figure 4.11 Annual mean global moisture flux for ECHAM3 T106.
Moisture Flux (g/mA2 s) (ECHAM4)
0
60
120
180
Longitude
240
300
Figure 4.12 Annual mean global moisture flux for ECHAM4 T106.
360
Annual Mean Zonal Mean Moisture Flux
U) 40
-
E
C,,
230 M 20
10-
-80
-60
-40
-20
0
Latitude
20
40
80
60
Figure 4.13 Annual mean zonal mean moisture flux for ECHAM3 and ECHAM4
T106.
1
Annual Mean Moisture Transport
x 109
0.8
0.6
0.4
0.2
0
0~
-0.4
-0.6
-0.8
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure 4.14 Annual mean moisture transport for ECHAM3, ECHAM4 T106, and
for Baumgartner & Reichel. (Calculated from Table 4 c, page 55)
112
Diff. of Zonal Mean Annual Implied Oceanic Heat Transport (PW)
-80
-60
-40
0
-20
20
40
60
80
Latitude
Diff. of Zonal Mean Annual Implied Oceanic Heat Transport (PW)
-80
-60
-40
0
-20
20
40
60
80
Latitude
Figure A. 1 Difference of total heat transport in Atlantic Ocean and Indo-Pacific
Ocean for ECHAM3 and ECHAM4 T106.
113
Zonal Mean Annual Implied Oceanic Heat Transport (PW)
-80
-60
-40
-20
0
Latitude
20
40
60
80
Diff. of Zonal Mean Annual Implied Oceanic Heat Transport (PW)
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure A.2 Heat transport carried by ocean surface net shortwave radiation
in ECHAM3 and ECHAM4 T106 Atlantic, with the corresponding difference.
114
Zonal Mean Annual Implied Oceanic Heat Transport (PW)
-10-
-15-20-25-
-30-35-40 -
-45-50
-
-80
-60
-40
0
-20
20
40
60
80
Latitude
Diff. of Zonal Mean Annual Implied Oceanic Heat Transport (PW)
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
-0.5
-80
-60
-40
0
-20
20
40
60
80
Latitude
Figure A.3 Heat transport carried by ocean surface net shortwave radiation
in ECHAM3 and ECHAM4 T106 Indo-Pacific, with their difference.
115
Zonal Mean Annual Implied Oceanic Heat Transport (PW)
-80
-60
-40
-20
0
Latitude
20
40
60
80
Diff. of Zonal Mean Annual Implied Oceanic Heat Transport (PW)
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure A.4 Heat transport carried by ocean surface downward longwave
radiation in ECHAM3 and ECHAM4 T106 Atlantic, with their difference.
116
Zonal Mean Annual Implied Oceanic Heat Transport (PW)
0
-10-20E
-30 --
--ECHAM
4
For Indo-Pacific
-80-
downward long wave
-90 -100
-
-80
--
-60
-40
-20
0
Latitude
20
40
60
80
Diff. of Zonal Mean Annual Implied Oceanic Heat Transport (PW)
0
-0.5 --
-2-
(ECHAM4 - ECHAM3)
For Indo-Pacific
downward long wave
-3-
-3.51111
-80
-60
-40
0
-20
20
40
60
80
Latitude
Figure A.5 Heat transport carried by ocean surface downward longwave radiation
in ECHAM3 and ECHAM4 T106 Indo-Pacific, with their difference.
117
MINK&
Zonal Mean Annual Implied Oceanic Heat Transport (PW)
-80
-60
-40
0
Latitude
-20
20
40
60
80
Diff. of Zonal Mean Annual Implied Oceanic Heat Transport (PW)
-80
-60
-40
0
Latitude
-20
20
40
60
80
Figure A.6 Heat transport carried by ocean surface net latent heat flux in
ECHAM3 and ECHAM4 T106 Atlantic, with their difference.
118
Zonal Mean Annual Implied Oceanic Heat Transport (PW)
-80
-60
-40
-20
0
Latitude
20
40
60
80
Diff. of Zonal Mean Annual Implied Oceanic Heat Transport (PW)
-0.2
-0.4
-0.6
-0.8
-1
-1.2
-1.4
-1.6
-1.8
-2
-80
-60
-40
-20
0
Latitude
20
40
60
80
Figure A.7 Heat transport carried by ocean surface net laten heat flux in
ECHAM3 and ECHAM4 T106 Indo-Pacific, with their differnece.
119