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NCAR/TN-429+STRNCAR TECHNICAL NOTE
September 1996
THE NCAR LAND SURFACE MODEL
(LSM VERSION 1.0) COUPLED To THE
NCAR COMMUNITY CLIMATE MODEL
Gordon B. Bonan
CLIMATE AND GLOBAL DYNAMICS DIVISION
NATIONAL CENTER FOR ATMOSPHERIC RESEARCHBOULDER, COLORADO
I
I I I I I III II I~~~~~~~~~~~~
Abstract
The NCAR land surface model (LSM version 1.0) was coupled to a modified version of
the NCAR Community Climate Model (CCM) - a greatly modified version of the CCM2
and precursor to the CCM3 (without the CCM3 convection scheme). LSM has interactive
hydrology and replaces the prescribed surface wetness and prescribed snow cover in the
CCM. It also replaces the land albedos and surface fluxes, using instead parameterizations
that include hydrological and ecological processes (e.g., soil water, phenology, stomatal
physiology, evaporation of intercepted water). Two ten-year simulations, one with LSMt
and one without, were compared to determine the impact of LSM on the simulated climate.
Major findings are:
* Without LSM, the atmospheric model has extensive regions of colder than observed
surface air temperatures. LSM improves the simulation of surface air temperature in
January, April, July, and October, except in northern North America and Asia in July
where the model introduces a cold bias.
* The interactive hydrology - in which the latent heat flux decreases as the soil dries
and the amount of water in the soil is a mass balance between precipitation input and
evapotranspiration and runoff losses - improves the seasonality of the surface fluxes, re-
sulting in reduced latent heat flux and increased sensible heat flux during periods of water
depletion, and eliminates unrealistic annual water budgets in which evaporation exceeds
precipitation.
* LSM has little impact on vertical profiles of temperature and zonal wind, but dries the
atmosphere up to 500 mb for many regions in July.
* LSM simulates land-atmosphere exchange of CO2 with clearly defined growing seasons
that depend on temperature and water availability.
* LSM allows the coupled land-atmosphere model to be used for paleoclimate applica-
tions, future climate change scenarios, studies of land use effects on climate, and other
experiments in which surface properties change greatly from extant conditions.
i
Table of Contents
1. Introduction . . .
2. Land Surface Model .... ...........
2.1 Model Background . ........... .
2.2 Model Summary .......... .....
3. Atmospheric Model ..................
4. Coupled Model Experiments ...... ........
4.1 Surface Air Temperature ...........
4.2 Regional Surface Climatology ..........
4.3 Regional Temperature, Humidity, and Wind Profiles
4.4 Annual Water Balance .. ... o ....
4.5 Annual CO 2 Fluxes ............
5 Summary and Conclusions ...............
6. References . . . . . . . . . . . . . . . . . . . .
ii
. . . . . . 10
...... 11. . 11
.,.... 13
.. . . . 24. . . . . 25
.... ~25
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41
. 1
List of Figures
1. Schematic diagram of model processes .. ............. 44
2. Surface fluxes simulated by the model .. .......... 46
3. Hydrologic processes simulated by the model .... . . .. o 48
4. Schematic diagram of soil physics ... .... 50
5. Schematic diagram of CO 2 fluxes ................... 52
6. Geographic distribution of vegetation types .... ... .. 54
7. Geographic distribution of lakes and wetlands ........ 60
8. Geographic distribution of sand and clay .. ............. 62
9. Geographic distribution of soil colors . . . . ...... . . 64
10. Disciplinary topics in land surface models .. ..... ..... 66
11. On-going research projects in land surface models . .......... 68
12. January surface air temperatures as compared to observations .... . 70
13. January surface air temperature differences between models ..... . 72
14. April surface air temperatures as compared to observations ..... 74
15. April surface air temperature differences between models ....... 76
16. July surface air temperatures as compared to observations ....... 78
17. July surface air temperature differences between models ........ 80
18. October surface air temperatures as compared to observations .... 82
19. October surface air temperature differences between models ..... 84
20. Geographic regions for regional analyses ............... 86
21. Monthly surface climatology for Alaska and North West Canada ... 88
22. Monthly surface climatology for Western United States ........ 90
23. Monthly surface climatology for Central United States ........ 92
24. Monthly surface climatology for Eastern United States ... .... . 94
25. Monthly surface climatology for Central America ............ 96
26. Monthly surface climatology for the Amazon Basin . . . ........ 98
27. Monthly surface climatology for Southern South America .... .... 100
28. Monthly surface climatology for Central Europe ..... ....... 102
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29. Monthly surface climatology for Northern Europe
Monthly surface climatology for West Siberia ......
Monthly surface climatology for East Siberia .......
Monthly surface climatology for the Sahara Desert ....
Monthly surface climatology for the Congo Basin ....
Monthly surface climatology for South Africa ......
Monthly surface climatology for the Tibetan Plateau . . .
Monthly surface climatology for India ......
Monthly surface climatology for Indochina .. .....
Monthly surface climatology for Indonesia ....
Monthly surface climatology for Australia ........
Monthly surface climatology for Greenland .......
Monthly surface climatology for Antarctica .......
January and July profiles for Alaska and North West Canada
January and July profiles for Western United States .
January and July profiles for Central United States . .
January and July profiles for Eastern United States ....
January and July profiles for Central America ...
January and July profiles for the Amazon Basin
January and July profiles for Southern South America
January and July profiles for Central Europe . . . ...
January and July profiles for Northern Europe ......
January and July profiles for West Siberia ....
January and July profiles for East Siberia ........
January and July profiles for the Sahara Desert
January and July profiles for the Congo Basin . . . .
January and July profiles for South Africa ........
January and July profiles for the Tibetan Plateau ....
~.......106
... .. .. 108
... . . . . 110
e o.. . . . . . 112
... . . . . 114
... . . . . 116
o o.. . . . . . 118
... . . . . 120
... . . . . 122
... . . . . 124
... .... 126
o o... ..... 128
e.. . . . . 130
.. . . . . . 132
... . . . . 134
.. . ... . 136
... . . . .o 138
... . . . . 140
... . . . . 142
....... 144
.... . . . 146
o .. . . . . . 148
*.. . . . . .o 150
... . . . . 152
o.. .... . 154
.. . .. . . 156
... . . . . 158
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. . . . . . 104
57. January and July profiles for India .... ............. 160
58. January and July profiles for Indochina ................ 162
59. January and July profiles for Indonesia ................ 164
60. January and July profiles for Australia .... ........... 166
61. January and July profiles for Greenland ............... 168
62. January and July profiles for Antarctica ............... 170
List of Tables
Surface types .................
Plant types ..................
Soil parameters .. .... .........
Soil colors . . . . . . . . . . . . .. . . . . .
Geographic regions ............. .
Annual water balances . . . ..........
Annual water balances for 6,000 years ago .....
Annual net primary production .........
Annual net primary production .........
Annual ratio of respiration-to-photosynthesis ..
Annual microbial respiration ...........
Annual net CO flux ..............
7
·.· · · ·. .· . .· . 8
.... .. . . . ..· 9
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1. Introduction
The NCAR LSM (version 1.0) is a land surface model developed to examine biogeophys-
ical and biogeochemical land-atmosphere interactions, especially the effects of land surfaces
on climate and atmospheric chemistry. It is the land surface parameterization for the NCAR
Community Climate Model (CCM) version 3 (Kiehl et al., 1996) and for the initial config-
uration of the NCAR Climate System Model that couples atmosphere, ocean, sea ice, and
land models. The purpose of this technical note is to provide an overview of this model
and to document the climatic effects of implementing the model in the NCAR CCM. Bonan
(1996a) provides a complete technical description of the model.
2. Land Surface Model
2.1 Model Background
Land-atmosphere interactions have traditionally been described in terms of four sub-
disciplines: biogeophysical fluxes, biogeochemical fluxes, hydrology, and ecosystem dynam-
ics. Because biogeophysical fluxes (e.g., latent heat, sensible heat) and biogeochemical fluxes
(e.g., CO2) depend on the hydrological and ecological states of the land, long-term simula-
tions of land-atmosphere interactions must include hydrological and ecological sub-models.
However, many ecological, hydrological, and atmospheric processes are so intertwined that
these cannot be considered separate disciplines. Successful modeling of net primary pro-
duction, carbon storage, and trace gas emissions requires an accurate model of the mi-
crometeorological and hydrological environments in addition to the traditional ecological
emphasis on vegetation and biogeochemical controls. Successful modeling of latent and sen-
sible heat fluxes requires an accurate description of the ecological state and biogeochemical
controls in addition to the traditional emphasis on the physical environment. More impor-
tantly, many ecological, hydrologic, biogeochemical, and atmospheric models parameterize
the same processes though with vastly different time-scales and complexity. This introduces
1
internal self-consistency problems wlhen coupling component models into a. comprehensive
land-atmosphere model (Bonall, 1993a, 1995a).
In the late-1980's, I became interested in combining the relevant biogeophysical, biogeo-
chemical, hydrologic, and ecosystem processes into a physically and biologically comprehen-
sive model of land-atmosphere interactions. The primary motivation for this initial work
was to study land-atmosphere exchange of CO2 in boreal forests. This early work, described
in the following paragraphs, directly lead to the development of the more generalized land
surface model (LSM version 1.0) at the spatial and temporal scales appropriate for coupling
to atmospheric numerical models.
The first model was a daily time step model of energy, water, and CO 2 exchange for
aspen, birch, balsam poplar, white spruce, and black spruce forests near Fairbanks, Alaska. It
combined photosynthesis and respiration, using a generic tree physiology, with surface energy
exchange and whole-tree carbon allocation. The model was quite successful at simulating
CO 2 uptake and loss during plant photosynthesis, plant respiration, and microbial respiration
as compared to annual net primary production and decomposition (Bonan, 1991a). The
model also simulated the hydrologic and thermal states of these forests fairly accurately
(Bonan, 1991b). Model applications showed:
(1) Year-to-year differences in atmospheric CO 2 concentration, air tempera-
ture, and precipitation can produce an increased seasonal amplitude in CO2
fluxes that is consistent with observations of the increased seasonal amplitude
of atmospheric CO 2 concentration (Bonan, 1992). The increased amplitude is
due to increased photosynthesis as atmospheric CO2 increases.
(2) Physiological properties of black spruce growing near treeline in subarctic
Quebec allow black spruce to grow over a wider range of air temperatures
(annual growing degree days) than is reflected in its geographic distribution
2
(Bonan and Sirois, 1992). In p)articular, the northern limit to black spruce is
not caused by the direct effects of cold growing season temperatures on tree
growth; growth is optimal, with respect to temperature, at the southern range
limit.
The model was substantially modified from this first version to study the environmen-
tal and physiological controls of forest production. The model was modified to include an
updated photosynthesis parameterization, with species-specific physiology, and dynamic ni-
trogen limitation to tree growth. The model was applied to the aspen, birch, balsam poplar,
white spruce, and black spruce forests near Fairbanks to examine the physiological controls
of the carbon balance of boreal forests (Bonan, 1993b). Model analyses suggest that differ-
ences in the carbon balance of these forests can be explained by key physiological parameters
that link photosynthesis, carbon allocation, nitrogen requirements, litter quality, and foliage
longevity. Simulations showed that coniferous and deciduous physiology maximize annual
tree production for coniferous and deciduous forests, respectively, giving a physiological basis
for the evolution of their different life history characteristics. Model applications showed:
(1) Net canopy CO2 assimilation is as sensitive to uncertainty in leaf area
index as it is to uncertainty in species composition (Bonan, 1993c).
(2) The model successfully reproduced the relationship between net primary
production and mean annual air temperature observed over large climatic gra-
dients, showing that it reflects, at least for boreal and temperate coniferous
forests growing on moist soils, the length of the growing season, nitrogen limi-
tation, and lower maintenance respiration rates in warm climates than in cold
climates (Bonan, 1993d). These analyses show that simple physiological as-
sumptions can result in reasonable predictions of net primary production over
a wide range of climates.
3
These background studies were sufficiently successful that I decided to abandon the boreal
forest model and make a more general land-atmosphere model that would have a diurnal
cycle and be applicable to all of the Earth's surface types. This would allow the model to
be coupled to an atmospheric general circulation model.
2.2 Model Summary
The land surface model (LSM version 1.0) is a one-dimensional model of energy, momen-
tum, water, and CO2 exchange between the atmosphere and land, accounting for ecological
differences among vegetation types, hydraulic and thermal differences among soil types, and
allowing for multiple surface types including lakes and wetlands within a grid cell. The
purposes of this model are to:
* provide surface albedos, upward longwave radiation, sensible heat flux, la-
tent heat flux, constituent fluxes (H 20, CO2), and momemtum fluxes to an
atmospheric model;
* simulate land surface climatology (surface temperatures, soil temperatures,
snow and soil water, etc.);
* explicitly link H 2 0 and CO2 fluxes through stomatal physiology; and
* simulate runoff for eventual transport to oceans by a river routing model.
The model has been used to study: land-atmosphere CO 2 exchange (Bonan, 1995b), showing
that simple physiological and ecological assumptions can result in reasonable simulation of
global land-atmosphere CO2 exchange; the effects of lakes and wetlands on climate (Bonan,
1995c), showing that in summer high inland water regions are 2 to 3 °C cooler, have increased
latent heat flux, and decreased sensible heat flux compared to a simulation without inland
water; and climate sensitivity to infiltration and surface runoff, showing that in an off-
line comparison with another land surface model most differences in sensible and latent
heat fluxes are related to the parameterization of infiltration capacity despite quite different
4
canopy energy exchange parameterizations (Bonan, 1994) and that when coupled to the CCM
a sub-grid parameterization of infiltration results in significantly less infiltration compared to
a grid-average parameterization, causing drier soils, decreased latent heat flux, and increased
surface air temperature (Bonan, 1996b).
In the model, the biogeophysical and biogeochemical fluxes depend on the ecological and
hydrological state of the land. These are updated using ecological and hydrological sub-
models (Figure 1). The four main components of the model are surface fluxes (Figure 2),
hydrology (Figure 3), soil physics (Figure 4), and CO2 fluxes (Figure 5). Major features of
the model are:
* prescribed time-varying leaf and stem areas;
* absorption, reflection, and transmittance of solar radiation, accounting for
the different optical properties of vegetation, soil, water, snow, and ice;
* absorption and emission of longwave radiation allowing for emissivities less
than one;
* sensible and latent heat fluxes, partitioning latent heat into canopy evapo-
ration, soil evaporation, and transpiration;
* turbulent transfer above and within plant canopies;
* vegetation and ground temperatures that balance the surface energy budget
(net radiation, sensible heat, latent heat, soil heat);
* stomatal physiology and CO 2 fluxes;
* interception, throughfall, and stemflow;
* snow hydrology;
* infiltration and runoff;
* temperatures for a six-layer soil column using a heat diffusion equation that
accounts for phase change;
5
* soil water for the same six-layer soil column using a one-dimensional conser-
vation equation that accounts for infiltration input, gravitational drainage at
the bottom of the column, evapotranspiration losses, and vertical water flow
based on head gradients; and
* temperatures for six-layer shallow and deep lakes accounting for eddy diffu-
sion and convective mixing.
Vegetation effects are included by using twelve plant types that differ in leaf and stem
areas, root profile, height, leaf dimension, optical properties, stomatal physiology, rough-
ness length, displacement height, and biomass. The model allows for 28 different vegetated
surfaces, each comprised of multiple plant types and bare ground so that, for example, a
mixed broadleaf deciduous and needleleaf evergreen forest consists of patches of broadleaf
deciduous trees, needleleaf evergreen trees, and bare ground. Lakes and wetland, if present,
form additional patches.
Soil effects are included by allowing thermal properties (heat capacity, thermal conductiv-
ity) and hydraulic properties (porosity, saturated hydraulic conductivity, saturated matrix
potential, slope of retention curve) to vary continuously depending on percent sand and
percent clay. Soils also differ in color, which affects soil albedos.
Consequently, each grid cell in the domain of interest is assigned a surface type, a fraction
covered by lakes, a fraction covered by wetlands, a soil texture (percent sand, percent silt,
percent clay), and a soil color. Table 1 lists the 28 surface types and the plant types and bare
ground (Table 2) that comprise each surface type. Figure 6 shows the global distribution of
these surface types on the CCM's T42 (approximately 2.8° by 2.8°) Gaussian grid. Figure
7 shows the fraction of each grid cell covered by lakes and wetlands. Figure 8 shows the soil
types as defined by the sand and clay content. The percent sand and percent clay are used
to define soil thermal and hydraulic properties, which are illustrated in Table 3 for sand,
6
loam, and clay soils. Soil albedos depend on waveband, whether the soil is wet or dry, and
soil color classes (Table 4). The 9th color is a special class for North Africa and the Arabian
Peninsula. The global distribution of these colors is shown in Figure 9.
Table 1. Plant types and fractional cover for each surface type
surface type plant cover plant cover plant cover
No Vegetation0 ocean - - - -1 land ice b 1.00 -2 desert b 1.00
Forest3 cool needleleaf evergreen tree net 0.75 b 0.254 cool needleleaf deciduous tree ndt 0.50 b 0.505 cool broadleaf deciduous tree bdt 0.75 b 0.256 cool mixed net and bdt net 0.37 bdt 0.37 b 0.267 warm needleleaf evergreen tree net 0.75 b 0.25 - -8 warm broadleaf deciduous tree bdt 0.75 b 0.259 warm mixed net and bdt net 0.37 bdt 0.37 b 0.2610 tropical broadleaf evergreen tree bet 0.95 b 0.05 - -11 tropical seasonal deciduous tree tst 0.75 b 0.25
Interrupted Woods12 savanna wg 0.70 tst 0.3013 evergreen forest tundra net 0.25 ag 0.25 b 0.5014 deciduous forest tundra ndt 0.25 ag 0.25 b 0.5015 cool forest crop c 0.40 bdt 0.30 net 0.3016 warm forest crop c 0.40 bdt 0.30 net 0.30
Non-Woods17 cool grassland cg 0.60 wg 0.20 b 0.2018 warm grassland wg 0.60 cg 0.20 b 0.2019 tundra ads 0.30 ag 0.30 b 0.4020 evergreen shrubland es 0.80 b 0.2021 deciduous shrubland ds 0.80 b 0.2022 semi-desert ds 0.10 b 0.9023 cool irrigated crop c 0.85 b 0.1524 cool crop c 0.85 b 0.1525 warm irrigated crop c 0.85 b 0.1526 warm crop c 0.85 b 0.15 -
Wetland27 forest wetland bet 0.80 b 0.2028 non-forest wetland b 1.00 -
7
Table 2. Plant types
Plant Type Acronym
needleleaf evergreen tree netneedleleaf deciduous tree ndtbroadleaf evergreen tree betbroadleaf deciduous tree bdttropical seasonal tree tstcool C3 grass cgevergreen shrub esdeciduous shrub dsarctic deciduous shrub adsarctic grass agcrop cwarm C 4 grass wgbare b
Table 3. Thermal and hydraulic properties for sand, loam, and clay soils
Texture
Parameter Sand Loam Clay
sand (%) 92 43 22clay (%) 3 18 58thermal conductivity, soil solids (W m - 1 K - 1) 8.61 7.06 4.54heat capacity, soil solids (MJ m- 3 K- 1) 2.14 2.20 2.31Clapp and Hornberger "b" 3.39 5.77 12.13saturated hydraulic conductivity (mm s-1) 0.0236 0.0042 0.0020saturated matrix potential (mm) -47 -207 -391saturated soil water (mm3 mm-3) 0.373 0.435 0.461optimal soil water (mm3 mm- 3 ) 0.034 0.138 0.281dry soil water (mm3 mm- 3) 0.028 0.122 0.266
8
Table 4. Dry and saturated soil albedos for visible (vis)and near-infrared (nir) wavebands
Dry Saturated
Color Class vis nir vis nir
1 = light 0.24 0.48 0.12 0.242 0.22 0.44 0.11 0.223 0.20 0.40 0.10 0.204 0.18 0.36 0.09 0.185 0.16 0.32 0.08 0.166 0.14 0.28 0.07 0.147 0.12 0.24 0.06 0.128 = dark 0.10 0.20 0.05 0.109 0.27 0.55 0.15 0.31
Land surface processes encompass elements of atmospheric science, ecology, hydrology,
and geology (Figure 10), and ongoing research projects run a gamut of topics (Figure 11).
The detail with which these topics can be parameterized poses an important challenge in
the development of a land surface model. The LSM model was designed for coupling to
atmospheric numerical models, although it can be run in a stand-alone mode on a spatial grid
that can range from one point to global if an atmospheric forcing is provided. Consequently,
there is a compromise between computational efficiency and the complexity with which the
necessary atmospheric, ecological, and hydrologic processes are parameterized. The model
is not meant to be a detailed micrometeorological model, but rather a simplified treatment
of surface fluxes that reproduces at minimal computational cost the essential characteristics
of land-atmosphere interactions important for climate simulations.
9
3. Atmospheric Model
TThe atmospheric model is a greatly modified version of the NCAR Community Climate
Model version 2 (CCM12), as described by Hack et al. (1993), and is a precursor to the CCM
version 3 (CCM3). The CCM2 is a spectral general circulation model configured at T42
resolution, with a horizontal grid of approximately 2.8° by 2.8°. The model has 18 vertical
levels with a top at 2.9 mb. The model has a 20 minute time step, but atmospheric solar
and longwave radiation are only updated every 60 minutes. The principal changes from the
CCM2 are related to the cloud, radiation, convection, and boundary layer parameterizations
and include: improved diagnosis of cloud optical properties (maritime versus continental
effective radius, liquid water path); incorporation of trace gases in the longwave radiation
(CH 4, N 20, CFC11, CFC12); incorporation of radiative properties of ice clouds; incorpora-
tion of background aerosol; modifications to the cloud fraction parameterization including a
new convective cloud scheme and modified layered cloud scheme; modified moist convection;
evaporation of stratiform precipitation; and diagnosis of boundary layer height in the non-
local atmospheric boundary layer scheme. Other changes include: dynamically determined
roughness length over ocean and a generalized gravity-wave drag parameterization. As in
the CCM2, the model uses prescribed climatologically varying sea surface temperatures, pre-
scribed surface wetness, prescribed snow cover, and prescribed surface albedos. The primary
difference from the CCM3 is that this model (internally referenced at NCAR as CCM2-
omegaO.5) uses the CCM2 convection scheme rather than the CCM3 parameterization.
LSM replaces the prescribed surface wetness, prescribed snow cover, and prescribed sur-
face albedos in CCM2. It also replaces the land surface fluxes in CCM2, using instead
flux parameterizations that include hydrological and ecological processes (e.g., soil water,
phenology, stomatal physiology, interception of water by plants).
In coupling to the atmospheric model, the land surface model provides to the atmospheric
10
model, at every time step, surface albedos (direct beam and diffuse for visible and near-
infrared wavebands), upward longwave radiation, sensible heat flux, latent heat flux, water
vapor flux, and surface stresses. The atmospheric model provides to the land model, at
every time step, incident solar radiation (direct beam and diffuse for visible and near-infrared
wavebands), incident longwave radiation, convective and large-scale precipitation, and lowest
model level temperature, wind, specific humidity, pressure, and height.
4. Coupled Model Experiments
Two 10.333 year experiments were performed with the atmospheric model, hereafter
refered to as CCM2+. The first was a standard simulation without the LSM land surface
model (hereafter referred to as the CCM2+ simulation). The other was with the LSM
model (hereafter referred to as the LSM simulation). Both simulations used prescribed
climatological sea surface temperatures and sea ice as in CCM2. Both began on September
1 of year zero. The first four months were discarded from the analyses to allow for spinup.
The only difference between the two simulations was the inclusion of the LSM model. All
model comparisons are based on the 10-year mean and standard error of the mean.
4.1 Surface Air Temperature
Figure 12 shows the January surface air temperature (2 m over land, 10 m over ocean)
simulated by CCM2+ as compared to the the Legates and Willmott (1990a) climatology.
Model biases, where they exist, tend to be colder than observations. The important temper-
ature biases over land are: (a) a greater than 4°C cold bias over Alaska, extending westwards
to the Pacific coast of northern Asia, (b) a greater than 4°C cold bias over eastern North
America, Greenland, and northern Europe, (c) a greater than 4°C warm bias over eastern
Siberia, (d) a 4°C to 6°C cold bias over the Sahara Desert and the Arabian Peninsula, (e)
a large cold bias centered on the Tibetan Plateau, (f) a 2°C to 4°0 cold bias over much
of Australia, and (g) a 20C to 40C cold bias over the Andes Mountains in South America.
11
LSM warms surface temperatures compared to the control simulation without LSM (Figure
13). This warming is on the order of 2°C to 4°C and is statistically significant, based on
a t-test, in tropical South America and Africa, Australia, India, Mongolia, eastern Europe,
and northern North America. This warming diminishes some of the cold biases in CCM2+
(Figure 12). The geographic extent of the CCM2+ cold biases is reduced in: (a) eastern
North America, (b) northern Europe, (c) Sahara Desert and Arabian Peninsula, (d) India,
and (e) Australia. However, the east Siberian warm bias is accentuated.
In April, CCM2+ has the same cold biases as in January (Figure 14). The important
difference is that most of northern and eastern Europe and Asia are uniformly too cold. As
in January, LSM warms surface air temperatures compared to the control simulation (Figure
15). This warming is greatest (in excess of 4°C ) in northern North America and Asia. This
warming reduces the geographic extent of the CCM2+ cold biases (Figure 14), especially in:
(a) North America, (b) northern and eastern Europe, (c) Asia, (d) North Africa, and (e)
Australia.
In July, CCM2+ has cold biases in: (a) North Africa and the Arabian Peninsula, (b)
Tibetan Plateau, (c) South Africa, (d) Australia, (e) the Andes Mountains, and (f) Greenland
(Figure 16). There is a smaller (2 0C ) cold bias in Alaska and a 2°C warm bias in central
North America. LSM significantly cools the surface in northern North America and Asia; it
warms the surface in Greenland, tropical South America, tropical Africa, India, and Australia
(Figure 17). With LSM, cold biases are reduced in: (a) the Andes Mountains, (b) South
Africa, (c) North Africa and the Arabian Peninsula, and (d) Australia (Figure 16). The
warm bias in Central North America is reduced. However, LSM accentuates the CCM2+
cold bias in Alaska and introduces a 20C cold bias in northern Europe and Siberia.
October CCM2+ temperature biases are, for the most part, similar to April with the
exception that the cold biases in North America, Europe, and Asia are riot as extensive
12
(Figure 18). LSM warms the surface by 2°C to 3°C in: (a) the United States, (b) the
Amazon Basin, (c) Greenland, (d) Africa, (e) central Asia, and (f) Australia (Figure 19).
This warming reduces the cold biases in: (a) the United States, (b) North Africa, (c) East
and South Africa, and (d) Australia.
In general, CCM2+ has extensive regions of colder than observed temperatures. Inclusion
of LSM warms the surface in January, April, and October, bringing the coupled model closer
to observations except in east Siberia in January, where the warming accentuates a CCM2+
warm bias. In July, LSM cools northern North America and Asia, but warms the tropics
and sub-tropics. These differences eliminate many CCM2+ biases, but accentuate a cold
bias in Alaska and introduce a cold bias in northern Europe and Siberia. Alaska, Greenland,
the Andes, North Africa, and the Tibetan Plateau are regions of recurring cold biases (all
four months) in both simulations. East Siberia has a warm bias in January and cold bias in
April and July.
4.2 Regional Surface Climatology
The impact of LSM on surface climatology and fluxes was further examined by dividing
the land into 21 geographic regions (Table 5, Figure 20). Monthly 2 m surface air temper-
ature, total precipitation, precipitation minus evaporation (P-E), net radiation, latent heat,
sensible heat, soil water, and snow were compared for the with LSM (LSM) and without
LSM (CCM2+) simulations. A t-test is used to infer statistical differences between the two
simulations.
13
Table 5. Regions of interest. Regions are the la.nd points in eachrectangle defined by southern and northern latitudes and
western and eastern longitudes
Latitudes Longitudes
Region South North West East Area (km 2) Cells
North AmericaAlaska and North West Canada 50Western United States 30Central United States 30Eastern United States 30Central and South AmericaCentral America 10Amazon Basin -10Southern South America -60Europe and RussiaCentral Europe 40Northern Europe 55West Siberia 50East Siberia 50AfricaSahara and Arabian Peninsula 10Congo Basin -10South Africa -35South East AsiaTibetan Plateau 30
10
10'-10
IndiaIndochinaIndonesiaAustraliaAustraliaPolar RegionsGreenlandAntarctica
70 -170 -100 579251450 -130 -110 208909450 -110 -90 411478550 -90 -70 3319689
25 -110 -80 16480320 -70 -50 2700228
-25 -80 -50 4020024
55 -10 40 535057070 5 60 353575170 60 90 371833170 90 140 6084540
30 -20 50 138968035 10 30 3768036
-10 10 40 5417506
40 80 100 167034630 70 90 337168930 90 120 453098210 90 150 2225278
-40 -10 110 160 7907074
126295646
182852
827977
126
1543960
21385123
91
60 90 -60 -20 2103902 81-90 -65 -180 180 12579541 750
14
Alaska andl North West Canada (Figure 21). Both the with and without LSM simulations
reasonably reproduce the annual cycle of 2 m surface air temperature compared to the
Legates and Willmott (1990a) climatology. The biggest difference between the two models
is in July, when LSM cools the surface. There is little difference in precipitation between the
two models. Both overestimate precipitation compared to the Legates and Willmott (1990b)
climatology. Both models have similar temporal patterns of P-E. The biggest difference is in
May, when LSM has a much lower P-E. Because precipitation is similar in both models, this
difference must be due to higher evaporation. Net radiation and latent heat are similar for
the two models. LSM causes higher sensible heating in the winter and less in the summer
compared to the without LSM simulation. Whereas CCM2+ uses prescribed time-invariant
soil water, LSM has interactive soil hydrology. Soil water recharges in late-spring as the
snow melts. The soil dries in the summer months when water input from precipitation is
small and water loss from evapotranspiration is high. CCM2+ also has a prescribed snow
cover. Both simulations agree in the timing and duration of snow cover and snow melt, but
LSM simulates much more snow than CCM2+. LSM simulates CO2 fluxes. The model has a
clearly defined growing season with peak rates of photosynthesis, plant respiration, microbial
respiration, and net CO 2 uptake in June and July. The model predicts a net release of CO 2
to the atmosphere from October through April.
Western United States (Figure 22). Both models simulate similar 2 m surface air temper-
atures. The biggest difference with the observations is in late-winter and spring, when the
models are colder than the observations. Both models simulate similar precipitation. For
the most part, the observations fall within the model inter-annual variability. LSM simulates
much less evapotranspiration during the summer, resulting in a higher P-E than CCM2+.
In other months, P-E is similar for the two models. Net radiation does not statistically differ
between the two models. However, LSM has much less latent heat and much more sensible
15
heat from MIay to September thai CCM12+. This appears to be related to the interactive soil
hydrology. The soil hydrology has a pronounced annual cycle of depletion during late-spring
and summer and recharge in fall and winter. The timing of snow melt and accumulation is
similar between the two models, but LSM has much more snow. Photosynthesis occurs year-
round, with maximum CO 2 uptake in June. Maximum C0 2 loss from plant and microbial
respiration occurs one month later. This region is a net sink of CO 2 during April, May, and
June and a net source in the rest of the year.
Central United States (Figure 23). Temperature, precipitation, and P-E do not statistically
differ between the two models. The seasonality of 2 m air temperaure is reasonably simulated
compared to observations except that both models have a warm bias in July. Precipitation is
also reasonably well simulated except that the observations show a peak in June whereas the
models have a peak in July. Net radiation, latent heat, and sensible heat are, for the most
part, similar between the two models. LSM has more latent heat flux in snowy months than
CCM2+. Soil water shows the standard draw-down during summer and recharge during fall
and winter. Both models argree on the timing of snow melt and accumulation, but LSM has
more snow. LSM has a clearly defined growing season with peak rates of photosynthesis,
plant respiration, microbial respiration, and net CO2 uptake in May, June, and July. The
model predicts a net release of CO2 to the atmosphere from August through April.
Eastern United States (Figure 24). Temperature, precipitation, and P-E do not statistically
differ between the two models. Both models are colder than observations in winter and
warmer than observations in July. Both models have more seasonality to precipitation than
is seen in the observations. Net radiation does not statistically differ between the models,
but LSM has less latent heat and more sensible heat in the summer. Soil water shows
the standard draw-down during late-spring and summer and recharge during fall, winter,
and early-spring. Both models argree on the timing of snow melt and accumulation, but
16
LSM has much more Snow. LSM has a clearly defined growing season with peak rates of
photosynthesis, plant respiration, microbial respiration, and net CO2 uptake in May, June,
and July. The model predicts a net release of CO2 to the atmosphere from September
through April.
Central America (Figure 25). Temperature, precipitation, and P-E do not, for the most part,
statistically differ between the two models. Any differences between the models (e.g., April
and May temperatures, July precipitation) are small compared to the differences with the
observations. Both models underestimate 2 m air temperature and overestimate precipitation
throughout the year. June, July, August, and September precipitation are especially poorly
simulated. Net radiation does not statistically differ between the two models. LSM has
much less latent heat and much more sensible heat from February to May than CCM2+.
This is a period of relatively low soil water, so the difference between the two simulations
may be related to interactive versus prescribed hydrology. The soil water shows draw-down
from January through April and recharge beginning in May associated with the high rates
of precipitation. Maximum rates of photosynthesis, plant respiration, and net CO 2 uptake
occur during the rainy season.
Amazon Basin (Figure 26). The major difference between the two models is in their simula-
tion of the dry season from May through September. During this period, CCM2+ simulates
colder than observed surface air temperatures. Moreover, the observations show a slight
warming during this period, but CCM2+ simulates a large cooling. In contrast, LSM re-
produces the observed warming, but overshoots the warming by about 2°C . Both models
underestimate precipitation except for the period of September through December. CCM2+
has a better simulation of precipitation prior to the dry season; LSM provides a better sim-
ulation after the dry season. P-E statistically differs between the two models from June
through October, when CCM2+ has a much higher latent heat flux than LSM. LSM has
17
decreased latent heat flux and increased sensible lheat flux during tlhe dry season. This is
clearly associated with the draw-down of soil water. Plant photosynthesis decreases during
the dry season, causing this period to be a net source of CO2 to the atmosphere.
Southern South America (Figure 27). Surface air temperature does not statistically differ
between the two models. Both models are colder than the observations at all times of the
year.. Precipitation is similar to the observations in both models. CCM2+ has much lower
P-E than LSMI during the period November through February because of its higher latent
heat flux. LSM shows that this is a period of low soil water, and perhaps the fixed hydrology
of CCM2+ does not respond to the appropriate hydrologic cycle.
Central Europe (Figure 28). The 2 m surface air temperature does not statistically differ
between the two models; both are consistent with the observations. Precipitation differs
between the two models. LSM is wetter than the observations in spring and drier than the
observations in the summer. In contrast, CCM2+ is consistent with observations in spring
and has a smaller dry bias in the summer. LSM has less latent heat flux and more sensible
heat flux than CCM2+ from June to September. This is a period of low soil water, which
suggests the fixed hydrology of CCM2+ does not respond to the appropriate hydrologic
cycle. LSM has a clearly defined growing season with peak rates of photosynthesis, plant
respiration, microbial respiration, and net CO 2 uptake in May, June, and July.
Northern Europe (Figure 29). LSM is warmer than CCM2+ in April and May and is cooler
than CCM2+ in July. The warming results in better agreement with the observations, but
the cooling results in a cold bias. Precipitation is, for the most part, not statistically different
between the two models. The biggest difference is in May when LSM is wetter than CCM2+.
Net radiation is similar between the two models. Latent heat is also similar between the two
models; the primary difference is in May when LSM has a much higher flux than CCM2+.
18
LSMI has higher sensible heat flux in spring than CCM2+. LSM has a clearly defined groNwing
season with peak rates of photosynthesis, plant respiration, microbial respiration, and net
CO2 uptake in June and July.
West Siberia (Figure 30). Temperature, precipitation, and P-E do not, for the most part,
differ statistically between the two models. For both models, temperature and precipitation
are similar to the observations. Net radiation is similar for both models. LSM has more latent
heat in May and June and much less sensible heat in June and July. The models disagree
in snow hydrology. CCM2+ melts snow much earlier than does LSM, which accumulates
snow until March and does not begin snow melt until April. As in other regions, LSM has
much more snow than CCM2+. LSM has a clearly defined growing season with peak rates
of photosynthesis, plant respiration, microbial respiration, and net CO2 uptake in June and
July.
East Siberia (Figure 31). Both models overestimate, by a similar amount, 2 m surface air
temperature in January. In July, LSM is colder than CCM2+. In other months, tempera-
tures are similar and consistent with the observations. Precipitation and P-E do not differ
statistically between the two models. Both models overestimate precipitation compared to
the observations. Net radiation is similar for both models. LSM has slightly less latent heat
in July and much more sensible heat in spring. The models disagree, similar to the West
Siberia region, in snow hydrology. CCM2+ melts snow much earlier than does LSM, which
accumulates snow until March and does not begin snow melt until April. As in other regions,
LSM has much more snow than CCM2+. LSM has a clearly defined growing season with
peak rates of photosynthesis, plant respiration, microbial respiration, and net CO2 uptake
in June and July.
Sahara and Arabian Peninsula (Figure 32). Both models have a cold surface temperature bias
at all times of the year. This bias originates because of the use of high soil albedos, needed
19
to match the high clear sky albedos observed in the Earth Radiation Budget Experiment.
LSM is statistically warmer than CCMI2+, bringing the simulated temperatures closer to
observations. CCM2+ has a fairly good simulation of precipitation. LSM underestimates
precipitation in July and August. LSM has significantly less latent heat flux than CCM2+.
As a result, P-E is generally higher than that of CCM2+ except in July and August when
LSM underestimates precipitation. LSM has less net radiation from April through August
than CCM2+. Sensible heat flux is similar. CO 2 fluxes from this sparsely vegetated region
are very small.
Congo Basin (Figure 33). Differences between the two models in their simulation of the
Congo region are similar to those of the Amazon Basin region. CCM2+ has a relatively con-
stant latent heat flux throughout the year and a marked decrease in surface air temperature
during the dry season. In contrast, LSM simulates a decrease in latent heat flux during the
dry season, and although surface air temperature decreases during this period the decrease
is not as large as in CCM2+. Both models underestimate 2 m surface air temperature, but
LSM is generally within 1"C of the observations whereas CCM2+ is much colder. Both mod-
els overestimate precipitation in February and March, but the less precipitation simulated
by LSM is more consistent with observations than CCM2+. In the dry season, CCM2+ is
too dry; LSM is similar to the observations. In November, CCM2+ is somewhat wetter than
the observations. Overall, LSM simulates a more realistic annual cycle for precipitation than
does CCM2+. LSM has less net radiation from May through October than CCM2+, which
is associated with the warmer surface temperature. LSM also has much more sensible heat
than CCM2+. Photosynthetic CO2 uptake decreases during the dry season, causing this
region to be a small source of CO2 to the atmosphere. In the rainy season, the region is a
sink of CO2.
South Africa (Figure 34). LSM is statistically warmer than CCM2+ from May to October,
20
with a simulation that is closer to the observations. Precipitation does not statistically differ
between the two models. The simulations are fairly consistent with the observations except
in October, November, and December when both models are too wet. LSM has less latent
heat flux than CCM2+ from April through October. This decrease in latent heat flux is
associated with a depletion of soil water. LSM has less net radiation, associated with its
warmer surface, and more sensible heat than CCM2+.
Tibetan Plateau (Figure 35). Both models greatly underestimate 2 m surface air temperature
throughout the year. Differences between the models are much smaller than this cold bias.
Both models also overestimate precipitation - a problem associated with orographic locking
of precipitation that was also common to CCM2. LSM provides a worse simulation of
precipitation than CCM2+. LSM has lower P-E than CCM2+ because of much higher
latent heat flux. Net radiation differs between the two models throughout the year and LSM
has much less sensible heat. CCM2+ has no snow cover in this region. LSM simulates a
deep snow pack that begins to melt in April.
India (Figure 36). LSM simulates a warmer surface than CCM2+, resulting in better agree-
ment with the observations. LSM is somewhat drier than CCM2+; both models greatly
underestimate the July and August monsoon. LSM has much less latent heat flux than
CCM2+, resulting in a higher P-E. Indeed, P-E is negative for much of the year in CCM2+,
and the annual P-E is also negative. This is clearly an artifact of the fixed hydrology scheme
in CCM2+. LSM has less net radiation, associated with its warmer surface, and much more
sensible heat than CCM2+.
Indochina (Figure 37). The 2 m surface air temperature does not statistically differ between
the two models. Both are colder than the observations throughout the year. Precipitation
differs statistically only in July and August, when LSM is wetter than CCM2+. This wet
21
bias accentuiates the wet bias in CCMI2+. For the remainder of the year, both models
are in close agreement with the observations. LSMI has reduced net radiation and reduced
latent heat flux compared to CCM2+. The biggest difference in sensible heat flux occurs in
October through May, when LSM has much more sensible heat than CCM2+. Soil water
recharges during the rainy season. This is also the period of maximum CO2 uptake during
photosynthesis and maximum net CO 2 uptake.
Indonesia (Figure 38). Although the 2 m surface air temperature differs between the two
models, both are within 1.5°C of the observations throughout the year. Precipitation does
not, for the most part, statistically differ between the two models. Both are consistent with
observations except during the dry season when both underestimate precipitation. P-E and
net radiation do not, for the most part, differ between the two models. LSM has much less
latent heat and much more sensible heat than CCM2+. These fluxes are especially different
during the dry season, when the draw-down of soil water in LSM reduces the latent heat
flux and increases the sensible heat flux.
Australia (Figure 39). LSM provides a better simulation of 2 m surface air temperature than
CCM2+. Both models are colder than observations, primarily in the Australian winter, but
LSM is warmer than CCM2+, bringing the simulated temperatures closer to observations.
Precipitation does not statistically differ between the two models, and both are in close
agreement with the observations. LSM has a much larger P-E than CCM2+ due to much
smaller latent heat flux, especially in the Australian spring, summer, and fall. In fact,
CCM2+ has a negative P-E throughout the year; this is clearly an unrealistic artifact of the
fixed hydrology scheme. Net radiation does not statistically differ between the two models.
LSM has higher sensible heat flux than CCM2+.
Greenland (Figure 40). LSM provides a better simulation of 2 m surface air temperature
than CCM2+. Both models are colder than observations, but LSM is warmer than CCM2+,
22
bringing the simulated temperatures closer to observations. Precipitation and P-E do not
statistically differ between the two models. Both models provide a fairly reasonable simu-
lation of precipitation. LSM has much more net radiation, latent heat, and sensible heat
during the summer than CCM2+. LSM accumulates much more snow than CCM2+. Most
of this snow completely melts during the summer, except for a small region.
Antarctica (Figure 41). The two models do not statistically differ in their simulation of 2
m surface air temperature, precipitation, or P-E. Both models are colder and drier than
the observations. LSM has less net radiation, latent heat, and sensible heat flux during the
Antarctic summer. LSM has much more snow than CCM2+ and has a secular increase in
snow depth. Snow depth increases with time in LSM because there is little sublimation or
melt relative to snow fall. Hence, more snow accumulates than is lost. The model does not
allow for this snow to turn to ice as it is compacted from above.
The annual cycles of temperature, precipitation, soil water, snow, surface energy fluxes,
and CO2 fluxes are large, natural climate signals that provide an important and necessary
validation of the model. Overall, both models do a good job at reproducing the observed
seasonal changes in temperature, precipitation, and surface fluxes, although there are large
biases for certain regions at certain times of the year. Inclusion of LSM in the CCM both
improves and deteriorates the quality of the simulation for certain regions at certain times
of the year.
In general, differences between the two models often seem to be related to hydrology.
CCM2+ uses a fixed, time-invariate surface wetness in its latent heat flux. The latent heat
flux does not respond to precipitation and may not be in balance with precipitation. This is
certainly true for India and Australia, where the fixed hydrology scheme of CCM2+ results
in an annual negative P-E.
23
In contrast. LSMIs latent heat flux depends, for the most part, on the amount of water
in the soil - decreasing as the soil becomes drier. The amount of water in the soil responds
to water inputs from precipitation and snow melt, water loss from evapotranspiration, and
varies depending on soil physical properties that determine how much water infiltrates into
the soil, how much water the soil can hold, and how much water drains from the soil. In
temperate and cold climates, LSM simulates a depletion of soil water beginning in late-spring
and continuing through summer, with recharge in fall, winter, and early spring. Spring snow
melt is an important source of water to recharge the dry soils. In the tropics, soils recharge
during the rainy season. In general, LSM has decreased latent heat flux and increased
sensible heat flux during periods of soil water depletion compared to CCM2+. This is
especially important in the tropics. However, West Siberia and the Tibetan Plateau, where
LSM has higher latent heat and less sensible heat, are exceptions.
LSM also simulates a clearly defined growing season. In temperate grasslands, forests,
and crops and also in boreal forests and arctic tundra, peak rates of photosynthesis, plant
respiration, microbial respiration, and net CO2 uptake occur during the summer. These
areas are small sources of CO 2 during the rest of the year. In the tropics, maximum rates of
photosynthesis, plant respiration, and and net CO2 uptake occur during the rainy season.
4.3 Regional Temperature, Humidity, and Wind Profiles
Vertical profiles of temperature, specific humidity, and zonal wind are examined in Jan-
uary and July for the 21 regions (Table 5) used in the previous sections (Figures 42 to 62).
There are minimal differences between the simulations with (LSM) and without (CCM2+)
LSM in January. Southern South America (Figure 48), Indochina (Figure 58), and Indonesia
(Figure 59) are drier near the surface with LSM than without. In the Amazon, LSM shifts
the direction of the near-surface winds from westerly to easterly (Figure 47).
The biggest effects of implementing LSM occur in July, when 10 of the 21 regions are
24
drier with LSM than without: Alaska and North West Canada (Figure 42), Western United
States (Figure 43), Amazon Basin (Figure 47), Central Europe (Figure 49), East Siberia
(Figure 52), Sahara and Arabian Peninsula (Figure 53), Congo Basin (Figure 54), South
Africa (Figure 55), India (Figure 57), and Indonesia (Figure 59). This drying extends from
the surface (1000 mb) to 800 to 700 mb, and in some regions up to 500 mb. Only the Tibetan
Plateau (Figure 56) and Greenland (Figure 61) are wetter with LSM than without. Surface
temperature differences extend upwards from the surface only for Alaska and North West
Canada (Figure 42), Amazon Basin (Figure 47), East Siberia (Figure 52), and India (Figure
57). The cooling in East Siberia is particularly noteworthy because this is the only region
where temperature changes throughout the atmospheric profile. In the other regions, the
temperature changes are limited to the near-surface atmosphere. Zonal winds do not, for the
most part, statistically differ between the two models. The biggest difference is in Central
America (Figure 46), where LSM increases:the strength of the easterlies up to 500 mb. In
the Congo Basin (Figure 54), LSM reduces the strength of the near surface easterlies.
Overall, LSM has little impact on the vertical profiles of temperature and zonal wind for
the 21 regions examined. Differences in surface temperatures do not extend upwards into
the atmosphere except in four regions. Even in these regions, temperature differences are
limited to the near-surface atmosphere (except East Siberia). LSM has a much larger impact
on specific humidity. In July, 10 of the 21 regions are drier with LSM than without. This
drying extends up to 500 mb. LSM has little impact on January specific humidity.
4.4 Annual Water Balance
The annual water balance is evaluated in terms of precipitation and runoff. For the simu-
lation without LSM (CCM2+), runoff is precipitation minus evaporation. For the simulation
with LSM (LSM), runoff is surface runoff plus subsurface drainage. Annual precipitation and
the ratio of runoff-to-precipitation are compared for 13 river basins and all land (Table 6).
25
Table 6. Annual precipitation and runoff for selected river basins.
Observed precipitation is from Legates and Willmott (1990b).
Area Precipitation (mm day- 1) Runoff-to-Precipitation
Basin (km 2) Observed LSM CCM2+ LSM CCM2+
Amazon 5510292 6.19 4.09 3.86 0.257 -0.057
Congo 3060214 4.55 4.85 4.84 0.367 0.225
Orinoco 1065672 6.32 4.52 5.20 0.323 0.392
Yenisey 2235598 1.23 1.74 1.74 0.437 0.431
Mississippi 2411034 2.03 1.69 1.77 0.077 0.175
Lena 2162146 1.08 1.39 1.48 0.496 0.453
Ganges 1028659 3.38 0.88 1.75 0.182 -0.737
Ob 2304330 1.41 1.61 1.52 0.261 0.395
Amur 1620308 1.45 1.80 1.85 0.350 0.405
Mackenzie 2002815 0.99 1.55 1.48 0.426 0.405
Yukon 781709 1.23 2.23 2.33 0.740 0.768
Zambezi 1703743 2.73 2.73 2.95 0.264 0.031
Kolyma 586484 1.00 1.42 1.50 0.718 0.667
Global Land 148853184 2.24 2.25 2.41 0.453 0.253
26
Annual precipitation is similar for both models for most river basins. The biggest dif-
ferences are the Orinoco, where LSM is 0.7 mml day-l drier than CCM2+, and the Ganges,
where LSM is 0.9 mm day- 1 drier than CCM2+. Averaged over all land, LSM is 0.2 mm
day - 1 drier than CCM2+, which is closer to the observations. Annual precipitation for some
river basins is particularly well simulated (e.g., Zambezi). Others (e.g., Yukon, Ganges,
Amazon) are poorly simulated.
For the 13 river basins, there is a statistically significant positive correlation between
observed and simulated precipitation for both models
LSM: SIM = 0.91 + 0.56 x OBS, F 24.1, p < 0.0005, r 2 = 0.687
CCM2+ : SIM = 0.93 + 0.60 x OBS, F = 40.2, p < 0.0005, r2 = 0.785
The slope and intercept of these two regression equations do not differ, but there is better
agreement with the observations for CCM2+ (78% of variance "explained" versus only 69%
for LSM). The intercept of both relationships is significantly greater than zero (p < 0.05),
and the slopes are significantly less than one (p < 0.05). This means that both models
overestimate low precipitation values and underestimate high precipitation values.
Overall, there is a significant and positive correlation between the runoff-to-precipitation
ratios simulated by CCM2+ and LSM
LSM = 0.278 + 0.361 x CCM2+, F = 11.4, p < 0.01, r2 = 0.509
However, CCM2+ clearly simulates unrealistic runoff for the Amazon and Ganges rivers,
where annual P-E is negative. LSM simulates much less runoff than CCM2+ for the Mis-
sissippi River, but much more for the Zambezi River. Runoff-to-precipitation ratios are
27
similar for the other nine basins. Globally, however, LSM Ihas almost twice as much runoff
as CCM2+.
The differences in simulated runoff between the two models are most likely related to
the fixed hydrology scheme in which evaporation is not constrained by available water. The
regional analyses presented in section 4.2 also suggest the unconstrained evaporation in
CCM2+'s hydrology scheme - in which soil water is not conserved - can produce unrealistic
hydrologic cycles in the simulation of the modern, climate.
Inadequate hydrologic cycles caused by the fixed hydrology scheme are. also seen in pa-
leoclimate simulations. Table 7 shows differences in annual P-E between simulations of the
modern climate and 6,000 years ago. Simulations for 6,000 years ago differ from the mod-
ern simulations only in the use of orbital parameters for 6,000 years ago (J.E. Kutzbach,
personal communication). All other aspects of the simulation (e.g., vegetation types, sea
surface temperatures) do not differ. The two models agree in their simulation of the climate
6,000 years ago only for Eastern United States, West Siberia, and East Siberia. In two of
the other five regions (Alaska and North West Canada, Central United States), CCM2+
simulates wetter conditions 6,000 years ago whereas LSM simulates either no change or drier
conditions. In Western United States and Central Europe, CCM2+ simulates drier condi-
tions whereas LSM simulates wetter conditions. CCM2+ simulates no change in Northern
Europe whereas LSM simulates a drier climate. Observations are available for only 5 of the
regions. In two of these (Eastern United States, West Siberia), the models agree in their
simulated climate change. In Western United States, LSM's simulation of a wetter climate
disagrees with the observations whereas CCM2+'s simulation of a drier climate agrees with
the observations. However, in Central United States and Central Europe, LSM agrees with
the observations whereas CCM2+ does not. Of the five regions with observations, LSM
improves the simulation for two and makes it worse for one.
28
Table 7. Differences ill annual P-E between simulations of the modern climate and 6,000
years before present. A "+" indicates that conditions 6,000 years ago were wetter
than the modern climate. A "-" indicates drier conditions. A "o" indicates no
change. Observations are from J.E. IKutzbach (personal communication).
Region Observations LSM CCM2+
Alaska and North West Canada o +
Western United States- +
Central United States +
Eastern United States
Northern Europe o
Central Europe + +
West Siberia +-
East Siberia
29
4.5 Annual CO2 Fluxes
Annual C02 fluxes simulated by LSMI are tested using the same procedures as in Bonan
(1995b).
The net primary production (NPP) simulated by LSM is proportional to the difference
between CO2 uptake during photosynthesis and CO2 loss during maintenance and growth
respiration. Comparisons between simulated and observed NPP are, therefore, a means to
test the plant CO 2 fluxes in LSM.
Table 8 compares the annual NPP simulated by LSM with Lieth's "observations". These
observations are obtained by applying Lieth's regression equations between NPP and mean
annual air temperature and annual precipitation to Legates and Willmott's (1990a, 1990b)
temperature and precipitation climatology (see Bonan (1995b) for more details). This cli-
matology was mapped from its original 0.5° resolution to the same 2.8° surface grid used by
LSM when coupled to the CCM. This allows for direct comparison of the two NPP values
on the same surface grid. Lieth's equations show the grid cells classified as needleleaf ev-
ergreen tree, broadleaf deciduous tree, savanna, grassland, and tundra are more productive
than simulated by LSM. Overall, however, there is fairly good agreement between the two
estimates of NPP.
Lieth's regression equations are just one estimate of NPP. Potter et al. (1993) and Melillo
et al. (1993) have developed global models of terrestrial NPP. When LSM is compared to
these three estimates, the annual NPP simulated by LSM generally falls within the range
of estimates (Table 9). The four models generally agree on NPP for needleleaf evergreen
forests and tropical broadleaf evergreen forests. Potter et al. simulated much lower NPP
for broadleaf deciduous forest and grassland than the other three models. Melillo et al.
simulated lower NPP for savanna and higher NPP for mixed forests than the other three
models. Lieth has much higher NPP for tundra than the other three models.
30
Table 8. Annual net primary production in units of dry matter.
Net Primary Production
(g m-2 yr- 1)
Vegetation Type Cells Area (106 km2 ) LSM Lieth
Glacier 841 14.8 0
Desert 80 5.2 0
Forest
Needleleaf evergreen tree 146 7.9 516 705
Needleleaf deciduous tree 54 2.8 352 373
Broadleaf deciduous tree 25 2.0 1129 1567
Mixed needle- and broadleaf tree 56 3.3 893 811
Tropical broadleaf evergreen tree 159 15.2 2311 2251
Interrupted Woods
Savanna 138 13.0 1214 1592
Evergreen forest tundra 66 3.2 382 470
Deciduous forest tundra 88 3.6 315 285
Mixed forest and crop 128 9.1 837
Non-Woods
Grassland 315 25.2 698 895
Tundra 225 9.4 257 425
Shrubland 91 8.0 219
Semi-desert 177 14.6 11
Crop 160 11.8 1038
31
Table 9. Annual net primary production il units of dry matter.
Net Primary Production
(g m - 2 yr-1)
Vegetation Type LSM Lieth Potter et al. Melillo et al.
Needleleaf evergreen forest 424 522 409 489
Broadleaf deciduous forest 1129 1567 630 1240
Mixed needle- and broadleaf forest 893 811 632 1338
Tropical broadleaf evergreen forest 2311 2251 2054 2196
Savanna 1214 1592 1118 752
Grassland 698 895 360 533
Tundra 257 425 160 240
Shrubland 219 - 368 296
Crop 1038 - 576
32
The ratio of respiration-to-photosynthesis is another means to test the plant CO 2 fluxes
simulated by LSM. McGuire et al. (1992) and Lloyd and Farquhar (1994) provide estimates
of this ratio for different vegetation types. For the most part, these estimates are similar
except for needleleaf evergreen forest and tundra, where McGuire et al. (1992) have much
higher ratios than Lloyd and Farquhar (1994). The ratios simulated by LSM are generally
within or close to the estimated range.
Table 10. Annual ratio of respiration-to-photosynthesis
R/P
Vegetation Type LSM McGuire et al. Lloyd and Farquhar
Needleleaf evergreen forest 0.68 0.60 to 0.76 0.38 to 0.45
Broadleaf deciduous forest 0.42 0.54 0.50
Mixed needle- and broadleaf forest 0.59 0.61 0.50
Tropical broadleaf evergreen forest 0.70 0.88 0.75
Savanna 0.67 0.60 0.55
Forest tundra 0.50 0.63
Grassland 0.56 0.48 to 0.56 0.40
Tundra 0.48 0.73 0.35
Shrubland 0.42 0.53
Crop 0.38 - 0.45
33
Annual microbial respiration is tested with comparisons to Raich and Schlesinger's (1992)
observations. There is good agreement with these limited observations. LSM's values fall
within one standard deviation of the observations except for semi-desert vegetation,- where
LSMI has much less microbial respiration compared to the observations.
Table 11. Annual microbial respiration
LSM Raich and Schlesinger
Vegetation Type Mean Vegetation Type Mean ± sd
(g CO 2 m-2 yr-) (g CO 2 m-2 yr- 1)
Needleleaf forest 571 Boreal forest 826 i 318
Broadleaf deciduous and 1087 Temperate forest 1661 ± 705
mixed forest
Broadleaf evergreen forest 3079 Tropical moist forest 3234 ± 463
Savanna 1922 Savanna 1614 ± 408
Grassland 1073 Grassland 1134 ± 600
Tundra 96 Tundra 154 ± 51
Shrubland 1962 Shrubland 1830 ± 814
Semi-desert 215 Desert scrub 575 ± 169
Crop 1569 Cropland 1396 ± 1047
34
The ultimate validation of the simulated CO2 fluxes is the fluxes themselves (Table
12). Unfortunately, there are no observations at the biome-scale (i.e., spatially averaged for
each vegetation type) with which to validate the simulated fluxes. Instead, one has to rely
on intuitive guesses of whether the simulated fluxes are "reasonable". Forest vegetation,
including forest tundra, are a sink of 10 x 1012 g CO2 yr- 1. This is approximately equally
distributed between tropical broadleaf evergreen forests and extratropical (temperate and
boreal) forests. This CO 2 uptake is nearly offset by large CO 2 loss from shrublands, which
occurs because of high microbial respiration (Table 11) and extremely low NPP (Tables 8,
9). This large loss of CO2 from shrublands and the loss from forest crop vegetation do
not intuitively seem "correct" and result in the biosphere being a net source of CO2 to the
atmosphere.
35
Table 12. Annual net CO2 flux
Vegetation Type Cells Area Total
(106 km2 ) (1012 g CO2 yr- 1)
No Vegetation
Glacier 841 14.8 0.00
Desert 80 5.2 0.00
Forest
Needleleaf evergreen tree 146 7.9 -0.79
Needleleaf deciduous tree 54 2.8 -0.39
Broadleaf deciduous tree 25 2.0 -0.09
Mixed needle- and broadleaf tree 56 3.3 -1.54
Tropical broadleaf evergreen tree 159 15.2 -5.47
Interrupted Woods
Savanna 138 13.0 0.44
Evergreen forest tundra 66 3.2 -0.91
Deciduous forest tundra 88 3.6 -0.82
Mixed forest and crop 128 9.1 4.67
Non-Woods
Grassland 315 25.2 -0.07
Tundra 225 9.4 -2.03
Shrubland 91 8.0 9.32
Semi-desert 177 14.6 2.08
Crop 160 11.8 -0.28
36
Total 2749 148.8 4.11
5. Summary and Conclusions
The NCAR land surface model (LSM version 1.0) was coupled to a modified version of the
NCAR Community Climate Model (CCM2+). LSM has interactive hydrology and replaces
the prescribed surface wetness and prescribed snow cover in the CCM. It also replaces the
land albedos and surface fluxes, using instead parameterizations that include hydrological
and ecological processes (e.g., soil water, phenology, stomatal physiology, evaporation of
intercepted water). These changes allow the coupled land-atmosphere model to be used for
paleoclimate applications, future climate change scenarios, and studies of land use effects on
climate - experiments in which the surface properties change greatly from extant conditions.
LSM is the land surface component for the CCM3 and the initial configuration of the NCAR
Climate System Model.
It would be very nice to make a broad statement that inclusion of LSM in the CCM
improves the climate simulation for all locations at all times of the year. The reality, however,
is that in certain regions and at certain times of the year LSM produces a better climate
simulation than the standard fixed hydrology surface flux parameterization; it also produces
a worse simulation.
One of the major differences due to LSM is in the simulation of surface air temperature.
Without LSM, CCM2+ has extensive regions of colder than observed temperatures. Inclu-
sion of LSM warms the surface in January, April, and October, bringing the coupled model
closer to observations except in east Siberia in January, where the warming accentuates a
CCM2+ warm bias. In July, LSM cools northern North America and Asia, but warms the
tropics and sub-tropics. These differences eliminate many CCM2+ biases, but accentuate a
cold bias in Alaska and introduce a cold bias in northern Europe and Siberia.
Both versions of the model (with and without LSM) do a good job of reproducing the
observed seasonal changes in temperature, precipitation, and surface fluxes, although there
37
are large biases for certain regions at certain times of the year. Il general, differences between
the two models seem to be related to hydrology. LSIMs latent heat flux depends, for the most
part, on the amount of water in the soil - decreasing as the soil becomes drier. The amount
of water in the soil responds to water inputs from precipitation and snow melt, water loss
from evapotranspiration, and varies depending on soil physical properties that determine
how much water infiltrates into the soil, how much water the soil can hold, and how much
water drains from the soil. In temperate and cold climates, LSM simulates a depletion of soil
water beginning in late-spring and continuing through summer, with recharge in fall, winter,
and early spring. Spring snow melt is an important source of water to recharge the dry soils.
In the tropics, soils recharge during the rainy season. In general, LSM has decreased latent
heat flux and increased sensible heat flux during periods of soil water depletion compared to
CCM2+. This is especially important in improving the simulation of the tropics.
In addition to differing in seasonality, the two versions of the model differ greatly in their
annual water budgets. Globally, LSM has almost twice as much annual runoff as CCM2+.
Regionally, in India, Australia, and the Amazon Basin, CCM2+ simulates an annual water
budget in which evaporation exceeds precipitation - there is negative runoff. LSM reduces
evaporation so that there is a positive water balance. These differences are most likely related
to CCM2+'s fixed hydrology scheme, in which evaporation is not constrained by available
water.
LSM has little impact on the vertical profiles of temperature and zonal wind. Differences
in surface temperatures do not extend upwards into the atmosphere except in four regions.
Even in these regions, temperature differences are limited to the near-surface atmosphere
(except East Siberia). LSM has a much larger impact on specific humidity. In July, 10 of
the 21 regions examined are drier with LSM than without. This drying extends up to 500
mb. LSM has little impact on January specific humidity.
38
Finally, LSM\ simulates land-atmoshere CO2 exchange during photosynthesis, plant res-
piration, and microbial respiration. Comparisons of these simulated fluxes with observations
show that LSMA reasonably reproduces the annual fluxes averaged over vegetation types.
Comparisons with other models show that LSM produces as good a simulation, and in many
cases a better simulation, than the other models. LSM also simulates a clearly defined
growing season. In temperate grasslands, forests, and crops, in boreal forests, and in arctic
tundra, peak rates of photosynthesis, plant respiration, microbial respiration, and net CO2
uptake occur during the summer. These areas are small sources of CO2 during the rest of
the year. In the tropics, maximum rates of photosynthesis, plant respiration, and and net
CO2 uptake occur during the rainy season.
In summary:
* LSM improves, for the most part, the simulation of surface air temperature,
except in northern North America and Asia in July where the model introduces
a cold bias.
* The interactive hydrology - in which the latent heat flux decreases as the soil
dries and the amount of water in the soil is a mass balance between precipita-
tion input and evapotranspiration and runoff losses - improves the seasonality
of the surface fluxes, resulting in reduced latent heat flux and increased sen-
sible heat flux during periods of water depletion and eliminating unrealistic
annual water budgets in which evaporation exceeds precipitation.
* LSM has little impact on vertical profiles of temperature and zonal wind,
but dries the atmosphere up to 500 mb for many regions in July.
* LSM simulates land-atmosphere exchange of CO z with clearly defined grow-
ing seasons that depend of temperature and water availability.
39
* LSM allows the coupled land-atmosphere model to be used for paleoclimate
applications, future climate change scenarios, studies of land use effects on
climate, and other experiments in which surface properties change greatly
from extant conditions.
40
6. References
Bonan, G.B. 1991a. Atmosphere-biosphere exchange of carbon dioxide in boreal forests.
Journal of Geophysical Research 96:7301-7312.
Bonan, G.B. 1991b. A biophysical surface energy budget analysis of soil temperature in the
boreal forests of interior Alaska. Water Resources Research 27:767-781.
Bonan, G.B. 1992. Comparison of atmospheric carbon dioxide concentration and metabolic
activity in boreal forest ecosystems. Tellus 44B:173-185.
Bonan, G.B. 1993a. Do biophysics and physiology matter in ecosystem models? Climatic
Change 24:281-285.
Bonan, G.B. 1993b. Physiological controls of the carbon balance of boreal forest ecosystems.
Canadian Journal of Forest Research 23:1453-1471.
Bonan, G.B. 1993c. Importance of leaf area index and forest type when estimating photo-
synthesis in boreal forests. Remote Sensing of Environment 43:303-314.
Bonan, G.B. 1993d. Physiological derivation of the observed relationship between net pri-
mary production and mean annual air temperature. Tellus 45B:397-408.
Bonan, G.B. 1994. Comparison of two land surface process models using prescribed forcings.
Journal of Geophysical Research 99:25803-25818.
Bonan, G.B. 1995a. Land-atmosphere interactions for climate system models: coupling
biophysical, biogeochemical, and ecosystem dynamical processes. Remote Sensing of
Environment 51:57-73.
Bonan, G.B. 1995b. Land-atmosphere CO 2 exchange simulated by a land surface process
model coupled to an atmospheric general circulation model. Journal of Geophysical
Research 100:2817-2831.
Bonan, G.B. 1995c. Sensitivity of a GCM simulation to inclusion of inland water surfaces.
41
Journal of Climate 8:2691-2704.
Bonan, G.B. 1996a. A land surface model (LSM version 1.0) for ecological, hydrological,
and atmospheric studies: technical description and user's guide. NCAR Technical Note
NCAR/TN-417+STR. National Center for Atmospheric Research, Boulder, Colorado.
150 pp.
Bonan, G.B. 1996b. Sensitivity of a GCM simulation to subgrid infiltration and surface
runoff. Climate Dynamics 12:279-285.
Bonan, G.B, and Sirois, L. 1992. Air temperature, tree growth, and the northern and
southern range limits to Picea mariana. Journal of Vegetation Science 3:495-506.
Hack, J.J., Boville, B.A., Briegleb, B.P., Kiehl, J.T., Rasch, P.J., and Williamson, D.L. 1993.
Description of the NCAR Community Climate Model (CCM2). NCAR Technical Note
NCAR/TN-382+STR. National Center for Atmospheric Research, Boulder, Colorado,
108 pp.
Kiehl, J.T., Hack, J.J., Bonan, G.B., Boville, B.A., Briegleb, B.P., Williamson, D.L., and
Rasch, P.J. 1996. Description of the NCAR Community Climate Model (CCM3). NCAR
Technical Note NCAR/TN-420+STR. National Center for Atmospheric Research, Boul-
der, Colorado.
Legates, D.R. and Willmott, C.J. 1990a. Mean seasonal and spatial variability in global
surface air temperature. Theoretical and Applied Climatology 41:11-21.
Legates, D.R. and Willmott, C.J. 1990b. Mean seasonal and spatial variability in gauge-
corrected, global precipitation. International Journal of Climatology 10:111-127.
Lloyd, J., and Farquhar, G.D. 1994. 13C discrimination during CO2 assimilation by the
terrestrial biosphere. Oecologia 99:201-215.
McGuire, A.D., Melillo, J.M., Joyce, L.A., Kicklighter, D.W., Grace, A.L., Moore, B. III,
42
and Vorosmarty, C.J. 1992. Interactions between carbon and nitrogen dynamics in es-
timating net primary productivity for potential vegetation in North America. Global
Biogeochemical Cycles 6:101-124.
Melillo, J.M., McGuire, A.D., Kicklighter, D.W., Moore, B. III, Vorosmarty, C.J., and
Schloss, A.L. 1993. Global climate change and terrestrial net primary production. Na-
ture 363:234-240.
Potter, C.S., Randerson, J.T., Field, C.B., Matson, P.A., Vitousek, P.M., Mooney, H.A.,
and Klooster, S.A. 1993. Terrestrial ecosystem production: a process model based on
global satellite and surface data. Global Biogeochemical Cycles 7:811-841.
Raich, J.W., and Schlesinger, W.H. 1992. The global carbon dioxide flux in soil respiration
and its relationship to vegetation and climate. Tellus 44B:81-99.
43
Figure 1. Schematic diagram of the biophysical, biogeochemical, hydrologic, and ecosystem
processes simulated by the model. Lateral inflow of water, hydrologic transport, vegetation
dynamics, soil processes, and fluxes of methane, non-methane hydrocarbons, and nitrous
oxides are not currently simulated by the model.
44
stomatal ph* momentum* soil heat/snm
temperature
p ., I -W Ill MP�..P' MH '1,� R, Ri� �V , , "'fi I . la -.. �1," 1�0�1-,1�� -Y.,
eratures
o:~,'i;,~ ·'. '4. % :t y p e ., ..,: .
type .soilP.,.4S iO1
SAI
logy :-.: X
.1
*radiatii
one
dyI-oIv
*m
*gl
Figure 2. Surface fluxes simulated by the model. The model simulates the surface energy
budget of the vegetation and the ground. The surface energy budget of the vegetation
depends on the solar and longwave radiation absorbed by the vegetation, the sensible heat
flux from the vegetation, and the latent heat flux, which is partitioned into evaporation of
intercepted water and transpiration from sunlit and shaded leaves. The energy budget at
the ground depends on solar and longwave radiation absorbed by the ground, the ground
sensible and latent heat fluxes, and the soil heat flux. The radiative fluxes determine the
net radiative heating of the atmosphere. The sensible heat fluxes determine atmospheric
temperatures. The latent heat fluxes determine atmospheric water vapor. The momentum
flux determines atmospheric winds.
46
Surface Fluxes
reflected radiation*, , · le . *
4T, q, u, v, Qrad
incident radiation
emitted radlatlon
fible heat
Rn = H + X]
absorbed radiation
reflected radiation
emitted radiation
Rn-H H + E + G
momentum flux
vegetation evaporation
-- ^W- transpiration* sunlit leaves* shaded leaves
evaporation
insmitted radih
litted radiatioi
~* ground sensible heatsoil heat flux
Figure 3. Hydrologic processes used to simulate canopy, snow, and soil water pools.
48
evaporation
canovp water
throughfallstemflow
evaporation
infi
_ ~~melt
ion
Hydrology
transpiration
surface runoff-------- ^Do
drainage t
i:
03 ---- -1 -------0-4-- --- A---- ---
06 1__
I!~
- - - - - - - - - - - - - -
~~~11~~~~~1~~~~1~~obb~~~~~~~L~1l~~~~~l~~pp
- - - - - - - - - -
a , v, I
Figure 4. The soil column is divided into six layers 10, 20, 40, 80, 160, and 320 cm thick. The
fine resolution near the surface is needed to simulate the diurnal dynamics of temperature
and moisture and to reasonably simulate permafrost depth in high-latitudes. Each layer has
a temperature, thermal conductivity, and heat capacity, and a volumetric water content,
hydraulic conductivity, and matrix potential.
50
Azl=O.10 m
Az2=0.20 m
Az3=0.40 m
AZ4=0.80 m
Az 5=1.60 m
Az6=3.20 m
---- Tkc, ---- , k,-- -------
------- T k 2 2ck------- 2 2 2 -----
------ T3 k3 C3 ------ O 3 k3 /3 .---------
------- T4 k4 4 ------- 4 k 4 4 -----------
--- T k 5 c 5 ------- 05 k5 5
--- T 6 k- Tk 6 c --- 06 k 6 v6 ---
--- z 1=0.05 m-z =0.20 m
z3=0.50 m
-- 4=1.10 m
Z-5=2.30 m
-- z.6=4.70m
Soil Physics
I - ---
I -�
Figure 5. The model simulates CO 2 uptake during photosynthesis, which varies with environ-
mental factors such as photosynthetic photon flux density, temperature, water, atmospheric
CO2 concentration, and foliage nitrogen. CO2 loss during respiration varies among foliage,
stem, and root biomass and depends on temperature.
52
ASSIMILATION
6
0 0 500 1000PPFD
(umol/m 2/s)
0.5
CO 2--- 3---- - bO
a
-10 2
temperatu
temperature (o C)
-2 -1 0 0 50(foliage water potential CO co
(MPa) 2 (I
0 50 100nitrogen (mmol/g)
CO 2
0-10 25 60
temperature (o C)
0.3
-r̂n
boO4
-10 25temperature ( C)
CO2
6
0
6
0
CiO
0
0.01
CO 2
060
RESPIRATION
C/
bl
:3
)
Figure 6a. Distribution of glaciers and deserts (top) and semidesert, tundra, and shrub
vegetation (bottom) on the T42 grid used by the model.
54
Glacier and Desert
150W 120W 90W 60W 30W
Semidesert, Tundra, and Shrub
120W 90W 60W 30W 0
D
30E 60E 90E 120E
Figure 6b. Distribution of savanna and grassland vegetation (top) and crop and forest crop
vegetation (bottom) on the T42 grid used by the model.
56
Savanna, Cool Grassland, and Warm Grassland
Crop and Forest Crop
150W 120W 90W 60W 30W 0
Figure 6c. Distribution of needleleaf forest vegetation (top) and broadleaf and mixed
broadleaf-needleleaf vegetation (bottom) on the T42 grid used by the model.
58
Needleleaf Evergreen and Needleleaf Deciduous Forest
Broadleaf Decidous, Mixed, and Broadleaf Evergreen Forest
Figure 7. Percent of each T42 grid cell covered by lakes and wetlands.
60
Percent Lake
5 10 20 40
Percent Wetland
5 10 20 40
Figure 8. Sand and clay contents for soils on the T42 grid.
62
Percent Sand
1 a I I II, I , a I, I iI I I -I I II I i I I Ia I
0 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180
30 70
Percent Clay-.. . .a. . . . . . .I A __l~ I II I I I a I a I I I .- a
-a -- 1 a
V P "
I I I I I I I I I I I I I -I I I I I I I I I I I I I I I I I I I I I
90N -
60N -
30N -
0-
30S -
60S -
90S -
1I
SON -
O6N-
30N-
ON -
30S-
i.0t
180 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180
10 40
wvui - i | X _ _ i-.1 -
,
I
I'
I
-r-
9 I I I I I I I I I I I i I I I' I I I a I I I I-
'pE6
IanBT
.
r
Figure 9. Soil colors on the T42 grid. Light colors indicate light soils; dark colors indicate
dark soils.
64
Soil Color
I I I I I I I I I I - I I - I I I I I I I I I I I I I I I I
U- _
180 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180
QnK _
60N -
30N -
J0 -
30S -
oU5 -
90S- I. I I i I I I I a I I I IaI I a1 I' I I I I IaI I I I I I a I I
i .
la�
a
I
ft
CfLl%
w .4r
Figure 10. Disciplinary topics represented in land surface models.
66
I 8
vy o d
- .-
0 *LA'
-I
* 0.b)c
-: ' . "'. * ' : :* ::
D~ :
F:w Erz "S
' * :'""" *1
Figure 11. On-going research projects in the development of land surface models.
68
hphenology j.
matal phyiStosynthesi:robial proc
uction
* vegetation maps· soil mapspoint validation: FIFESremote-sensing: NDVI
id:heterc
solar radiation:on allowing for emiss
t b..at into canopy
layer s
il, andnospl
heat I
.:A14 '.'A
'Y ",1�1' Ol.. .1 1W'I e,11..21,
.a , -" � -
Figure 12. January surface air temperature difference as compared to the Legates and
Willmott climatology for CCM2+ (top) and CCM2+ with LSM (bottom). Widely spaced
hatches and stipples indicate regions with a temperature biases more than 20C cold and
warm, respectively. Denser hatches and stipples indicated biases in excess of 40C .
70
January Surface Air Temperature Differencelegates and willmott (°K)
I ' I I 0 -12 I I30E 60E 90E 120E 150E
CONTOUR FROM -24 TO 12 BY CONTOUR INTERVAL 2
ccm2+ with Ism legates and willmott90N
60N
30N
0
30S
60S
90S1
60 I 90E 120I 15 I60E 90E 120E 150E
CONTOUR FROM -22 TO 14 BY CONTOUR INTERVAL 2
ccm2 +90N
60N
30N
0
30S
60S
90S
(°K)
-2
Figure 13. January surface air temperature difference between the simulations with and
without LSM. Contours are -2, -1, 1, 2, 3, 4, 5, and 6°C . Stipples show regions where the
difference is statistically significant at the 95% level based on a t-test.
72
January Surface Air Temperature Differenceccm2+ with Ism ccm2+ (OK)
CONTOUR FROM -2 TO 6
90N
60N
30N
0
30S
60S
90S)--- -----
Figure 14. As in Figure 12, but for April.
74
April Surface Air Temperature Differencelegates and willmott
180 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E
CONTOUR FROM -26 TO 18 BY CONTOUR INTERVAL 2
ccm2+ with Ism legates and willmott (OK)
180 150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180
CONTOUR FROM -22 TO 18 BY CONTOUR INTERVAL 2
ccm2+90N -
60N -
30N -
0-
30S -
60S-
(°K)·
90S-180
90N
60N
30N
0
30S
60S
90S
, -'' U :
�.,
I
Figure 15. As in Figure 13, but for April.
76
April Surface Air Temperature Differenceccm2+ with Ism ccm2+ (°K)
180 150W 120W 90 W 60W 3 0W 30E 60E 90E 120E 150E 180
CONTOUR FROM -3 TO 11
Figure 16. As in Figure 12, but for July.
78
July Surface Air Temperature Differencelegates and willmott
150W 120W 90W 60W 30W 0 30E 60E
CONTOUR FROM -28 TO 18 BY CONTOUR INTERVAL 2
ccm2+ with Ism
90E 120E 150E
legates and willmott (OK)I I I I I I I I I I I I I I I I I I I I
150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E
CONTOUR FROM -28 TO 18 BY CONTOUR INTERVAL 2
ccm2+90N
60N
30N
0
30S
60S
(°K)
90S180 180
qnkl _
60N
30N
30S
30S
60S
90S180 180
I I I I I I I I I I I I I I -· I I I I · _· · . I · · · · · · I -· · i-
-
I , , I I I I
-
Figure 17. As in Figure 13, but for July.
80
July Surface Air Temperature Differenceccm2+ with Ism ccm2+ (°K)
CONTOUR FROM -4 TO 7
90N
60N
30N
0
30S
60S
90S
Figure 18. As in Figure 12, but for October.
82
October Surface Air Temperature Differencelegates and willmott
CONTOUR FROM -26 TO 16 BY CONTOUR INTERVAL 2
ccm2+ with Ism legates and willmott
150W 120W 90W 60W 30W 0 30E 60E 90E 120E 150E 180
CONTOUR FROM -24 TO 16 BY CONTOUR INTERVAL 2
ccm2+ (°K)
(°K)90N
60N
30N
0
30S
60S
90S180
I I -I I -I I - I - I I
Z, ,~~~~~~~e,--
0....--(d ....·~·
i
Figure 19. As in Figure 13, but for October.
84
October Surface Air Temperature Differenceccm2+ with Ism ccm2+ (°K)
60W
CONTOUR FROM -3 TO 5
90N
60N
30N
0
30S
60S
90S)
Figure 20. Geographic extent of each region defined in Table 5.
86
I I I I a I I I - I I II I I I I I I I I I L III I II
90S -
0 30E 60E 90E 120E 150E 180
90N -
60N -
30N -
0-
30S -
60S -
-I It -
;
I I I i
<s-.I_ 4
i-_
180 15OW 12OW 9ow 6OW 3OW
Figure 21. Monthly averaged temperature, total precipitation, precipitation minus evapora-
tion, net radiation, latent heat, sensible heat, soil water, snow and CO2 fluxes for the region
of Alaska and North West Canada. Data are time-averages of the spatial-averages for the 10
year simulations. Vertical bars indicate the 95% confidence intervals for the simulations with
and without LSM based on the models' interannual variability. For reference, data for CCM2
are also presented. Observed temperature and precipitation are from Legates and Willmott
(1990a, 1990b). CO2 fluxes are: photosynthesis (solid line), plant respiration (dotted line),
microbial respiration (dashed line), and net flux = respiration - photosynthesis (solid line
with error bars).
88
20
_o
a,
-20
_u
-30
200O
150E
100C0
Q.
50
.
0
o
._ 20
Oz
0
-50
0.40
;- 0.38EE
E 0.36E
.- 0.340
n) 0.32
0.30
4
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I
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Figure 22. As in Figure 21, but for Western United States.
90
western_usI I I I I I I 1 I I I30
J M M J S NMonth
E
-
0
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5
04
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100
80
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5
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J M M J S NMonth
...... observations___ ccm2___ ccm2+
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I'
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Figure 23. As in Figure 21, but for Central United States.
92
central_us30 I . . I i . .
I I I I . I I I I I I . .
J M M J S NMonth
I I I I I I I/ \
I I I I I I I I I I I
J M M J S NMonth
II I I I I I I I I
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0
J M M J S NMonth
1 11 I I 11 I I I
I I I ! I I I
J M M J S NMonth
, J M M J S NMonth
J M M J S NMonth
...... observations___ ccm2__- ccm2+
ccm2+ with Ism
20
10
0
S,
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200
150
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0V" 0.20
0.18
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Figure 24. As in Figure 21, but for Eastern United States.
94
30
20
10
0
-10J MM J S N
Month
250
. 200
E
' 150c0.9_
-o 10001
z 50
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MonthJ M M J S N
Month
10
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5
0
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Month
...... observations-_- ccm2
- ccm2+ccm2+ with Ism
C)o
).3
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I I I I I I I I I7 I r-1
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Figure 25. As in Figure 21, but for Central America.
96
I , I II I I I I I I
J M M J S NMonth
30
t
E 20c0.-00.
t0LQ.
0
central_americaI I -I I I I .I I I I
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J M M J S NMonth
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Month
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/
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100
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x
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-5
I I I I I I I I I
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J M M J S NMonth
J M M J S NMonth
..... observationsccm2
_--- ccm2+.ccm2+ with Ism
. 30
u- 25a,
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10
I 1I I I i I I I I I I )
I I
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Figure 26. As in Figure 21, but for the Amazon Basin.
98
\\\ ,/I/ I
brI I I, I - I I * - I I i
J M M J S NMonth
0O
Eco
m
a.
01--
amazonbasin
J M M J S NMonth
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J M M J S NMonth
I I I. I I I I I j I I I
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1.0
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J M M J S NMonth
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c
J M M J S NMonth
J M. M J S NMonth
...... observations___ ccm2___ ccm2+
ccm2+ with Ism
30
' 28
,.
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- 24
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22
20
200
_ 180
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v 160coo0 140o
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[ I I I I I I I I I 1 1
i I I I I I I1 I 11 I I I 1
v
I V./ F-
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.
Figure 27. As in Figure 21, but for Southern South America.
100
0
:3
CLQ.E0
ECM
J M M J S NMonth
20
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c.. 10
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J M M J S NMonth
E
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Month
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Month
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Month
J M M J S NMonth
...... observationsccm2
- _ ccm2+ccm2+ with Ism
-- ----
I I I-1 I I I-u i I 9 f
I
I
i I I
100 I I I I I I I-I IIrl I I
0
I·~ · ·~~
Figure 28. As in Figure 21, but for Central Europe.
102
4
0
3EEC'
o 2
._0
I-0
J M M J S NMonth
I I I I 1 1/ 1 I I '
.I
I I I I . I I . I I I
J M M J S NMonth
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0
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J M M J S NMonth
4
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observationsccm2ccm2+ccm2+ with Ism
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200
150
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i
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I
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Figure 29. As in Figure 21, but for Northern Europe.
104
northerneurope20
0
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L
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-20
4
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02
0._
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200
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I I I I I I I I I I IA
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I I I I I I I I I I
J MM J S NMonth
60
4U
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Month
IE
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J M M J S NMonth
10
cn-5E
3U.
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5
0
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Month
...... observations-__ ccm2
- _ ccm2+ccm2+ with Ism
I
I I I I I i I I I I fl
I
1
I I I I I I I I I I I I
I
I I I I I I I I I I I
17 1 17 r- \ 1 I
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t
I I I I I 1 I I I I r =
Figure 30. As in Figure 21, but for West Siberia.
106
40
20
0
-20
-40J M M J S N
Month
200
150
100
50
0
-50J M M J S N
Month
0.44
E 0.42E
EE 0.40
0
· 0.38cn
0.36
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J M M J S NMonth
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60
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20
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J M M J S NMonth
C-1f
E-'aE
x30L
o0
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J M M J S NMonth
J M M J S NMonth
J M M J S NMonth
...... observations___ ccm2-__ ccm2+
ccm2+ with Ism
C)
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C* _'-o
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I I I I I I I I I I I
F
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I
1
Figure 31. As in Figure 21, but for East Siberia.
108
40
(-
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0
a)QL
E
E '
J M M J S NMonth
6
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J M M J S NMonth
0
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w
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J M M J S NMonth
I I 111I II I I II T
I
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E
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J M M J S NMonth
60~~~ --
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0
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60 ' I 1
40
20
0
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J M M J S NMonth
11 1111111 III
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o
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cU)
J M M J S NMonth
in
r,
E
E
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J M M J S NMonth
...... observations_ ccm2
__ ccm2+ccm2+ with Ism
200
_ 150
E
100
cr
Z0'O 50
)-z n
-50
0.35
EE 0.30EE
0)
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0.20
-I I I I I I I I I I I I 1 I
I i"~
i
I
I I I I I I I ' I I I I I
1
Iu
4I
Figure 32. As in Figure 21, but for the Sahara and Arabian Peninsula.
110
40
,n
3 30aLt-a-E
*. 20
E("4
10J M M J S N
Month
160
140
E120
o o100
§ 80
60
4040
0-D
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0
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J MM J S NMonth
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J M M J S NMonth
50
40
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0
10
0
1.0
J M M J S NMonth
0.20
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IE 0.16E
. 0.14o
an 0.12
0.10
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0.2
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80
60
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I I I L I I I � I I I 1
J M M J S NMonth
2
i
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0C-
0O
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Month
...... observations-__ ccm2
ccm2+ccm2+ with Ism
. I I I I I I I I I I I I
I
II I
I I
I
I .
I I
- I I I -F I fI I I I I I-
II I I - A I I I I -I I I
I .
II I I I I I I I I I I I :
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4
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---
1
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Figure 33. As in Figure 21, but for the Congo Basin.
112
.
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J M M J S NMonth
1
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EE
c
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Q-%.
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160
140
E 1203:_
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e 800-j
60
40
J M M J S NMonth
I I I I I I I I I I I
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J M M J S NMonth
oI
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o
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J M M J S NMonth
J M M J S NMonth
0
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I I I I I I 1 1 I
80
60E
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J M M J S NMonth
¢",U)
E
E
x
UL
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J M M J S NMonth
...... observations_ ccm2
-_ ccm2+ccm2+ with Ism
26
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18
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Figure 34. As in Figure 21, but for South Africa.
114
30
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10
CM
10o I I I I I I I *I I I
J M M J S NMonth
200
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150
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J M M J S NMonth
100
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20
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4-x 0
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TI A I1
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I I I I I I I I I I I
J M M J S NMonth
J M M J S NMonth
.... . observations- ccm2
___ ccm2+ccm2+ with Ism
1%~~~~~~~~~~~~~~~~~-
I I
I
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I I I I I I I I I I II I I I I I I I I I I I II
I
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r
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4
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Figure 35. As in Figure 21, but for the Tibetan Plateau.
116
tibet_ploteau20 I I r I I I I I I
*' / *.
, / \ *
/' /.\ N
., I / \\ "_*' / / v '**~~~~~*~~~~~~~~~~~~~~0 I/V
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I .
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i I I I I i I I I I I
I aI I I I I I i I I I I
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1
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0-
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x
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J M M J S NMonth
...... observations-__ ccm2
-__ ccm2+ccm2+ with Ism
q
-
£ 10
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- -10
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E 20(",
-30
200
150
E
3 100c.oo
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020
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0.16
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Figure 36. As in Figure 21, but for India.
118
40
o30
L
0
a 20E
<C
E 10(',,
0 I I i I I I I I I I I I
J M M J S NMonth
200
150E
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100
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u,n
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observationsccm2ccm2+ccm2+ with Ism
� I I I I I I I I I I I I ,I
II
I
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IF
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Figure 37. As in Figure 21, but for Indochina.
120
r>0'D
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0)
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0,-
I I I _ I .I it _ I I I
J M M J S NMonth
indochina15
c-. 10
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0
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Month
i I I I I I I I . i .
J MM J S NMonth
I II / 1 I I I5 1 1
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200
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c
50.9 50
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-
0
U)cC0
V)
CO
60
40
20
0
-20J M M J S N
Month
t I I I ' I
J M M J S NMonth
u)
7
E
E
x
-C.
o0.
J M M J S N J M M J S NMonth Month
...... observations-__ ccm2
_ ccm2+ccm2+ with Ism
30
0: 200Q)Q.
E
(N
0
E
200
.1~1
E
C0
0-o0
rr
0z
0.38
E 0.36E
EE 0.34
03.
io 0.32
0.30J M M J S N
Month
I I I I I I I II-I I II I I I I I I I I I I I I
I
I
I
- II I I I - I I I I I I I I I) I I I I I I I I I~
I
I
II
I
II II. II
I
_II I I ) I I 1 I I
Figure 38. As in Figure 21, but for Indonesia.
122
20
0
15IE.EC00.
._a
Q _ 5
o
0
o
" 1 F- i , 1 - i i i -I 1
I t ! ! I I I I t i
J M M J S NMonth
indonesio
J M M J S NMonth
15
0ED
E
'Ia.
10
5
0
-5J M M J S N
Month
200 ' ' ' , '' , ' ' '
180 _
/ "\~ - / \160 / \ / \
140
120
100 . . . . . . .J M M J S N
Month
0.45
0.40
0.35
0.30
0.25
200
E 150
or
cS 100.J
50
1.0
. 0.8
0I 0.6EE3 0.4o
in
0.2
0.0J M M J S ,N
Month
E
003:a)
cw(n
60
// \ /_ \\/
4,4r . . . 4. . . .I
I -I - I --- I - I I I
J M M J S NMonth
15
I
m 107Eo'0
o o
-5J M M J S N
Month
40
20
0
-20
;/, _ ~' _! . I I I I I . i I I
J M M J S NMonth
·. . . . . . . . .
I - - I I I I-I I I I I1
J M M J S NMonth
...... observations-__ ccm2-__ ccm2+
ccm2+ with Ism
30
? 28
Q.
- 26
E- 24
22
20
E3:
oc.0
as
z
EE
EE
0)0
C,)
I I I I I I I I I II. I
I
II I I I I I I I I I r-l- II I I I I I I~ I I f I I
i
I
I
II IIIIIIFIr7I
I I
I
I
I
I I I I i I i - I - I t -
1 I I I I I I I I I r- -I I I I I I I I I I I I
I
I
I
I
I
u f- #- f I a I A I I f
Figure 39. As in Figure 21, but for Australia.
124
oustrolioI I I I I I I I I i I "I 1 1 I ' a I I I I I
tI
O 4
EE-" 3co0.5 2u.60.
05 1--
.0
0J M M J S N
Month
I I I I I 1111 1r1
I I I I I I I I 'I I I I
J M M J S NMonth
J M M J S NMonth
120
100
E
310o
c
-j
80
60
40
20
0
/
\
V //n" '. \\ /
I I I I I I I I I t I
J M M J S NMonth
J M M J S NMonth
1.0
0.8
r 0.6EE
o¢ 0.4c(f
0.2
0.0J M M J S N
Month
(n
EoE
x:3LL.
0(J
0'-
EEwLUI
Q-
0
-1
-2J M M J S N
Month
ES.
oI
C
nof~
J M M J S NMonth
J M M J S NMonth
...... observations___ ccm2___ ccm2+
ccm2+ with Ism
?t-.U)
, 200U)Q.EQ)a-
10 10
E0N
0
200
150E
c
. 100o'5
0z 50z I
0
E 0.20EE 0.20EE
0. 0.18
/)
0.16
30 5 1
I I I I I I I I I - I -
I
-I I I I I I I I I I
l
II
I I I I I I I I -I I I I
. i I I I I I I I I #I I L - I I I I I I
Figure 40. As in Figure 21, but for Greenland.
126
3
*>o
EE 2c0
0C
O.U _. 1CLQ-
0
O
.s.
J M M J S NMonth
I I I I I I I I I I I
I I I L I I I I I I
J M M J S NMonth
I I I I I I I I I I S I
J M M J S NMonth
E
0a
0-ja
greenland, i I , i i i ! I I i i
I
J M M J S NMonth
J M M J S NMonth
oI
EE
oC0n
1.0
'l 0.5
E
E 0.0=.x-
" -0.50
-1.0
0-o
wE
Ia.
J M M J S NMonth
20
E
oa)I0)a)
a)0
0
-20
-40 I I I I I I I I 'L I .
J MM J SN
I
J M M J S NMonth
Month
I I 1111111 I I
I I I I I I I I I I 1 I
J M M J S N'Month
...... observations- ccm2
-__ ccm2+ccm2+ with Ism
10
O
Q)
o -10
Q.
E- -20
E 30
-40
100
EE 50
C
0
0
oz O
)£
-50
0.950
^ 0.940E
E 0.930E
° 0.920
0 0.910
0.900
I
-- - I I I I I I . I
I1
I I - I I I I , 1 -
1-
--
)r
1-4
I -I I I I I I I I I I I
Figure 41. As in Figure 21, but for Antarctica.
128
I I I I I I I I I I I I
0 ,
/
3
0
EE 2cC0
0.
a-
0
0 1OI I I I I SI I I Nt
J M M J S NMonth
a- ntorctico1 / I I i I I I I I I
* * *. * . .* . . ·
: : ** : :
le . e
* **
l- ··· ·
··e
J M M J S NMonth
1.0
0.8
o0o 0.6EE
L 0.4
Q.
0.2
0.0 ! I I I I I I I I I
J M M J S NMonth
i i -1 i i I -1 'I I r-. i
,I/
'v% ~II
I I L I I I I I I I. .
J M M J S NMonth
10
E 5
-:
o0I
v O
-J
-5
1200
1000
o" 800I
EE 600
0c 400
200
0I I I I I I I I I I
J M M J S NMonth
i.\ /'\~ /
J M M J S NMonth
I1I I III IC C111I~ ~jJ M M J S N
Month
0
E -10E
o -20-r
0
CD
-40
-40
1.0
un 0.5, .EoE 0.0
:3x
i,
o -0.5
-1.0
-1.0
\I I I
J M M J S NMonth
J M M J S NMonth
...... observations-__ ccm2
_ ccm2+ccm2+ with Ism
-10
' -20
0)
3
o -300)
- -40
_50
-60
20
E
c'. -20*0
O
0 -40z
-60
1.06
1.04
EEE 1.02EE 1.000_
0
( 0.98
0.96
0.94
\ I/
III I1 rIIII ( 1
I
II I
, I1
I I I I ~I I I I I I I I I I I I f Ii I -1 I I i
k J
( I I I -I I I -I I r -r I I I ---I I I I I I I I
Ii
I I I I I I I I I I I a
------ -------- ---- -- ----
I I i I I!. I i I I
Figure 42. Vertical profiles of temperature, specific humidity, and zonal wind in January
and July for the region of Alaska and North West Canada. Data are time-averages of the
spatial-averages for the two 10 year simulations with (LSM) and without (CCM2+) LSM.
Horizontal bars indicate the 95% confidence intervals for the simulations with and without
LSM based on the models' interannual variability.
130
0
200
E 400
= 600V)en
a- 800
1000
a-
E0)
)3U)Q)L
0-
-60 -50 -40 -30 -20 -10Temperature (*C)
January
0.2 0.4 0.6 0.8 1.0Specific Humidity (g kg')
January
-60 -40 -20 0 20Temperature ('C)
July
.0
E
a)U)aa-
1.2
0 5 10 15 20 25Zonal Wind (m s-')
January
80 2 4 6Specific Humidity (g kg"')
July
E0)
U)0)
a.
0 5 10 15Zonal Wind (m s')
July
.0
Ea)
V)(/)
a-
1
2C.0
E 4C
' 6C()
a. 8C
10C
I
1
Figure 43. As in Figure 42, but for Western United States.
132
solid = Ism, dash = ccm2+OG ..
200
400
600
800
I /.AJU [0 -0 -0..... -2.
-80 -60 -40 -20 0Temperature ('C)
January
0 1 2 3Specific Humidity (g kg-')
Jonuary
D.E
U)
U)
-Q
20
D.
E
U)
U)
0-
20
40
60
80
100
0
0 -
0 -
n L
-80
400
500
600
700
800
900
1000
4
0 -5 10 15 20 25 30Zonol Wind (m s')
Jonuory
-60 -40 -20 0 20Temperature ('C)
July
0 2 4 6 8Specific Humidity (g kg-')
July
.0
E
0V)0.a.
40
10
0 5 10 15Zonol Wind (m s')
July
Of I v . .
a.
E
U)
0)
V)
Un
a.
I" I
400
500
600
700
800
900
1000
I 0 I . . - .
I I
.0
E03
0L
a.
1
II
- - -
. i
- .]
I
1%.
western-us
1
Figure 44. As in Figure 42, but for Central United States.
134
solid = Ism, dash = ccm2+-, I ... ,. .. ,. . , 7 . . . . . .. ., . - .. ..
-80 -60 -40 -20Temperature ('C)
January
200
E 400
3 600V)
Oa 800
i rnrr
0 20I Uj.. r . ..... ... .. I. ..
-80 -60 -40 -20 0 20 40Temperature ("C)
July
400
500
S 600E'- 7000' 800U)
a,
a. 900
1000
0.5 1.0 1.5 2.0 2.5 3.0 3.5Specific Humidity (g kg"')
January
0 10 20 30 40Zonal Wind (m s')
January
.0E
a.
122 4 6 8 10Specific Humidity (g kg'')
July
-2 0 2 4 6 8 10 12Zonal Wind (m s')
July
20.0E 40
= 60UEnU)
a. 80
100
400
500
D 600Ev 700:3I 800
900
1000
.0
E0)
v,
a.:3
U)
O.
centrol-us
a - - - - - , - - - -
W.
� A
Figure 45. As in Figure 42, but for Eastern United States.
136
easternus
-60 -50 -40 -30 -20 -10Temperature ('C)
January
0.5 1.0 1.5 2.0 2.5Specific Humidity (g kg-')
January
0 10 20 30Zonal Wind (m s")
January
0
3.0
40
0-11
.0
E
L-
U)a)0~I-0-
0
2001-1
.0
E
V)a.
400
600
800
1000
solid = Ism, dash = ccm2+
80 -60 -40 -20 0Temperoture (*C)
July
20 40
2 4 6 8 10 12Specific Humidity (g kg-')
July
E.0
:3,
a.
0 155 10Zonal Wind (m s"')
July
2C.0
Ea)
a-0
a.
4C
6C
8C
10C
-70JU" [ --.-.-.... .. I... ... ........ ...- I I . . . ..... . . . I . . . . .. . . . i
.0E
usV)
(n0)
.0
E
L
U)
a.
14
I , , ' . I . I . . I I I I . I . I
I
O p ........... .. I . ... . .. ....... ..... -.
)0-
)O
I
Figure 46. As in Figure 42, but for Central America.
138
200
E 400
3 6000)
a- 800
solid = Ism, dash = ccm2+
1 UUU
-80 -60 -40 -20 0 20 40Temperature ('C)
January
20.0E 4040
= 600
cL 80
10C
0 -
)0
-80 -60 -40 -20 0 20Temperature ('C)
July
400
500
D 600E
700L)
w 800U)
0. 900
1000
110002 4 6 8 10 12
Specific Humidity (g kg"')Jonuory
5 10 15 2Specific Humidity (g kg-')
July
I I ' , . .' ·! I I -! I I , ' I ' , '
.0E0)
U)
L
nv,a.
-10 -5 0 5 10 15 20Zonal Wind (m s'')
Jonuary
0
.M
E
U)u)L.U)
0~
I UUU
-12 -10 -8 -6 -4 -2 0 2Zonol Wind (m s')
July
oF - IT* I. I
F : ., .. .140
.0E
,)
-
1
(n
10 0
I
- ......... .
. . ..
I ....
I. .. . . I . . . .
.. . . . · · ·I . . . . . . .
F
central-omerica
'
-
I . . . I -.1....................
Figure 47. As in Figure 42, but for the Amazon Basin.
140
200
400
600
800
i nnn
amozon_basin
I W W ....... .... ...... .. . .
-100-80 -60 -40 -20 0 20Temperature ('C)
January
4UU
500
600
700
800
900
1000
11000 5 10 15
Specific Humidity (g kg'')January
.0E
0
U)
a0
40
-.0
U)0
0Q
20
u
0
200
400
600
800
1000
400
500
600
700
800
900
1000
1100
solid = Ism, dash = ccm2+
F -- X
80 -60 -40 -20 0 20 40Temperoture ('C)
July
0
-10Zonol Wind (m s')
January
2 4 6 8 10 12 14Specific Humidity (g kg-')
July
300 10 20Zonol Wind (m s"')
July
I I I . . .
-J~~.
1-s.0ECD
(/)n(n0
Ea
u0
-sE0
a-
CL
I
"1
� 4
i
j
~~. . ... ... ............... .... s. --A rn , . - ... . I .-* I .* .* , I * * I
; . . . · . . I . .** . . . . . . . . . . . . .. . . . . . . . . .
. ......... . .. .
. .. . . .. . . .. . . . . . .. . . . . .. . ..`~~`~`
01............. ---- ~ -··
-I-
L-
%I-
II
4
1
Figure 48. As in Figure 42, but for Southern South America.
142
south-southam
200
E 400
600Q)
a- 800 -
I nnnI %W W J I ..
-80 -60 -40 -20 0 20Temperature ('C)
January
1-.aE
,.:3V)0)
a.
1
-80 -60 -40 -20 0Temperoture ('C)
July
40
.0E
UV
(0a.
2 4 6 8 10 0 1 2 3 4 5Specific Humidity (g kg-') Specific Humidity (g kg-')
January July
L0)1
.0
3
E
(0()U)U)a)
0.
1
0 5 10 15 20 25 30Zonal Wind (m s')
January
V
m
Ea)L-
U)
a)(0
0 5 10 15 20 25 30Zonal Wind (m s"')
July
20
500
?' 600E- 700
ui 800900
a. 900
1000
11000
4%\
6
L35
Ann . I I I I . . . I I . . I - I
!
. . . . I . . . .I . I ' II I I .I . . . | . .' ' I . . . ..
.... a!. ... I. .!.. · . .· .. I I .... I . . . .
,~-- .-, . -,.--,... I ..
u
Figure 49. As in Figure 42, but for Central Europe.
144
nEa,
a,a-0I.:3
200
400
600
800
{{nn -
-60 -40 -20 0 20Temperature ('C)
January
400
500
600
700
800
900
1000
1100
0 1 2 3 4Specific Humidity (g kg"')
January
0
200
400
600
800
1000
0 5 10 15Zonol Wind (m s"')
January
E
CLa-
9
1
1 c
1 1
solid = Ism, dash = ccm2+
. . . !. . . i . , , , ..I
-60 -40 -20 0 20 4'Temperature ('C)
July
400 ---- I' . ' .. ' . . ..' . - .....___ ..
600300
700
300
900
nn0
5
0
0
100 . . . . .. . . . . .0 2 4 6 8 1J
Specific Humidity (g kg-')July
0-%.0E
L-
U)U)
L23.
20
u
200
400
600
800
1000
0 5 10 15Zonal Wind (m s-')
July
20 25
E
L)U3U)v,
E
(0
a.a-
I . Ia I
.0
E
LU)
U)
a,a-
- - -/- I . . I - - - -
0I .. . . . . - , , , , , , , , --, , I
4
k�
I I · I- . .' . I I ·- " . -. - I --
i... .................................... 2- .......-
. . .. . . . . . . .. ..
f
1 (
I
Figure 50. As in Figure 42, but for Northern Europe.
146
or
.s
E
0V)
a.
200
400
600
800
1 inn
-60 -50 -40 -30 -20 -10Temperature ("C)
Jonuary
0.5 1.0 1.5Specific Humidity (g kg"')
Januory
0 5 10Zonol Wind (m s'')
Januory
.0
E
U)
u)0L-U)
a.
2.0
.0Ea)L.
U)U)0)a-
400
500
600
700
800
900
1000
1100
0
200
400
600
800
1000
15 20
solid = ism, dosh = ccm2+
60 -40 -20 0 20Temperoture ('C)
July
0 2 4 6 8Specific Humidity (g kg-')
July
10
0 2 4 6 8 10 12Zonol Wind (m s')
July
0
200
400
600
800
1000
-70
400
500
600
700
800
900
1000
11000.0
· - -v t .
.0
E0I3U)U)
0
.0
E
a)L3
a)
a-
0.
a-
. .I . I. . . . I
0
200
400
600
800
1000
'5F . .. . I . .. . I _ . . I
__ ·s ww
-- 7 - - I
i
. . . I - . . . I - . . . . . . - .
I , , , I I I I I I ' , I . . . I I
j
-I
Figure 51. As in Figure 42, but for West Siberia.
148
westsiberio
a)
U).U)(na)ta-
-70 -60 -50 -40 -30 -20 -10Temperature ('C)
January
solid = Ism, dash = ccm2+
-60 -40 -20 0 20Temperature (C)
July
.0
E
TU)U)a)03tn
4UU
500
600
700
800
900
1000
11000.2 0.4 0.6 0.8 1.0 1.2 1.4
Specific Humidity (g kg-')January
0 5 10 15Zonal Wind (m s')
January
0 2 4 6 8Specific Humidity (g kg'')
July
Ea0
3U/)
03
20
10
0 2 4 6 8 10 12Zonal Wind (m s"')
July
u
200.0E 400L)
: 600vE
a- 800
1000
.0Ea)U-:3uncn
111
.0E
L.
()
a.
n\ rr ... I I v , , ................
_IL'··
A ̂ d . . . . ... . .X * * ... .
. . . . . . . .
Figure 52. As in Figure 42, but for East Siberia.
150
eastsiberia
-60 -50 -40 -30Temperature (*C)
January
1-o1Ea)L-
:3U)U)a)to
a.
-20
0.10 0.20 0.30 0.40 0.50 0.60 0.70Specific Humidity (g kg-')
January
0 5 10 15Zonal Wind (m s"')
January
solid = Ism, dash = ccm2+
-60 -40 -20 0Temperature ('C)
July
-oE
0
n
L
U-
a-:1
4UU
500
600
700
800
900
1000
1100
20 40
100 2 4 6 8Specific Humidity (g kg')
July
-2 0 2 4 6 8 10 12Zonal Wind (m s'')
July
E
U):3
tn
.0Euo
an
0
.0
v
E
:3
..
a)
A no ... ..I I .i ... , -- I . . # .-- . .
; . ! . . .. . . ... . . . . .
'V
I A-
Figure 53. As in Figure 42, but for the Sahara and Arabian Peninsula.
152
2C
E 4c
g 6Cen
a 8C
10C
-80 -60 -40 -20 0Temperature (C) ,
Januory
E
L-a3U1(na)L.
1
1
0 1 2 3 4Specific Humidity (g kg'')
January
0 10 20Zonal Wind (m s"')
January
20
solid = Ism, dash = ccm2+00 - '- -.'-....-..-.
200
E 400
g 600
CL 800
1000
-80 -60 -40 -20 0 20 40Temperature ('C)
July
A n'N*+uu
500
D 600E
700
U, 800U)
a. 900
1000
11005 .0
s-
u)u)
1n
CL
1
30 40
102 4 6 8Specific Humidity (g kg-')
July
-20 -15 -10 -5Zonol Wind (m s")
July
0 5
0
200-E 400
= 6000 0
C1 8000
1000
-10
7 . I I · - -I .I r I - . I - · -
I . a . . . D . . X . S . . . . . ·
. ... ,.. I,..,,,,... r.
.i a ... .......... ................ ............. .
IL.
Figure 54. As in Figure 42, but for the Congo Basin.
154
u
20
E
U)V)0
a.
40
60
80
inn
-80 -60 -40 -20 0 20 40Temperature ("C)
January
solid = Ism, dash = ccm2+,\ , · - . . .. . . .1 I ~ .... ...
-80 -60 -40 -20 0 20 40Temperature ('C)
July
4uVU
.0E0
V)U)0a-
11
2 4 6 8 10 12 14 16Specific Humidity (g kg-')
January
1100o 2 4 6 8 10 12
Specific Humidity (g kg'')July
.0
E0U)L.
U)
0
0..
-6 -4 -2 0Zonal Wind (m s-')
January
.
E
v
:30
0.
2 4 -10 -5 0 5Zonal Wind (m s')
July
20
-40
3 6006
0- 80
100
.0E
U)(n0
a-
10 15
. . . . . . . . . . . . . . . I . . . I . . .
A t-in E I * * - I -. -. . I - . r
. . . . . . . . . . . . . . . . . . . . . .
L
I
i
I,I vv
Figure 55. As in Figure 42, but for South Africa.
156
solid = Ism, dash = ccm2+
....... ..... . . . ....
-80 -60 -40 -20 0 20 40Temperature ("C)
January
0
200
E 4000
I 600en
- 800
1000
20-80 -60 -40 -20 0Temperature (*C)
July
E0
13
a.
111001100
04 6 8 10 12 14Specific Humidity (g kg-')
Januory
-4 -2 0 2 4Zonol Wind (m s"')
Januory
6 8
O F '' , ̀ 1 ' oi , . .. I... ... ..
20.0E 400L-
: 60Un
L 80
100
0
0
0
0
-10
2 4 6Specific Humidity (g kg"')
July
0 10 20Zonol Wind (m s"')
July
30
200.0E 400
3 600U)
.- 800
I nnn
.0Ea,U3
0.
1
1
2
0
200-0E 4000
: 600(n0
O 800
1000
8
F'j [ ... .. . . . .I . . . La .. . . . .. . ..I I I I I I I I I
South-o~af ric arh l - · - I I · I .- . - I I
-
I . . . .
4
U[ -
i
Figure 56. As in Figure 42, but for the Tibetan Plateau.
158
tibet_ploteou
-60 -40 -20Temperature ("C)
January
DE
-L
0
0La.
0
200
400
600
800
1000
0 20
solid = Ism, dash = ccm2+
-80 -60 -40 -20 0Temperature ('C)
July
E
L
s0a.
0.50 0.60 0.70 0.80 0.90 1.00Specific Humidity (g kg-')
January
0 10 20 30Zonol Wind (m s-')
January
20 40
5.0 5.5 6.0 6.5 7.0 7.5Specific Humidity (g kg'')
July
.0E0
I)U)Q
40 -5 0 5 10Zonal Wind (m s'')
July
15
0
200
400
600
800
nE0
L-
0Q.
1000
-f
I...1... .
I i
F ............ . -. .. - - .1
.0
E
0L-3a)U,
a.ml
.0
E0
U,(0aC
I I I I I I.. I . I~~~~~~~~-
. I j
80
1
1
1
Figure 57. As in Figure 42, but for India.
160
solid = Ism, dash = ccm2+
20
E 40Q)= 60U)
a. 80
100
-80 -60 -40 -20Temperature ('C)
January
0 1 2 3 4 5Specific Humidity (g kg-')
January
-10 0
0 20 -80 -60 -40 -20 0 20 40Temperature ('C)
July
400
500
S" 600E
7000)
w 800
a. 900
1000
11006
10 20 30 40 50Zonal Wind (m s"')
January
2 4 6 8 10 12 14 16 18Specific Humidity (g kg-')
July
.01DE0)L-
U)U)U)
a.
-30 -20 -10 0Zonal Wind (m s")
July
10 20
a.-
L
'uu
500
' 600E
700
800U)0,
& 900
1000
1100
E
U)
0)a.
1
. . . . . . . . . . . . . . L. . .'. ...i....A r%^ ..... I.... . . ... I ..... .I. ........ I.. ....... .. .... .I I
i .. . a . I . . I .... .. . i. I . .· ! .I - - ,*~ ........ ... ... ....! ...... ~ . . ..... I .... .............
indio
V~~~~~4
Figure 58. As in Figure 42, but for Indochina.
162
indochino
-I. .. ,. . , . i , , . . .,-80 -60 -40 -20 0 2(
Temperature ('C)January
O ...............
0
200-0
E 400
' 600V)
0)0 800
1000
0
E0)-
v,
Q>
U,V)
a)
OI ................0 2 4 6 8
Specific Humidity (g kg-')January
3 F^"~-------------1 ':"'" """`'~`~~~`~~r~~`"~~"~
-10
mEa)0U,U,a)0~Q.
0 10 20 30 40 50Zonal Wind (m s')
January
-80 -60 -40 -20 0 20 40Temperoture ('C)
July
4 6 8 10 12 14 16 18Specific Humidity (g kg-')
July
-25 -20 -15 -10 -5 0Zonal Wind (m s')
July
5
0
0
0 -
0
o -
0
D
D
D
0
0
20
E 40
(0L-
D 60U,
a- 80
100
40'
50(
Z' 60(E
t 701a)
, 80(U,
a. 90(
100(
110(
20(
E 40(
.
= 60(U,
Q 80(
100<
0
0
33
"F..... .,.........,. ............ ..,.
0 1 - I I . I I I . I I III I
-r 6
3
Figure 59. As in Figure 42, but for Indonesia.
164
indonesia
V. ... i i. . . . i. .. .. .... t
100 -80 -60 -40 -20 0 20 40Temperature ("C)
January
I . . . I . I . I I I . I
0 5 10 15Specific Humidity (g kg-')
January
EU
V)V)en
L0
-80 -60 -40 -20 0 20 40Temperature ("C)
July
E
a)
Q
20
.OE
3(/)(U
Q.
400
500
600
700
800
900
1000
1100
0 5 10 15Specific Humidity (g kg"')
July
20
-8 -6 -4 -2 0 2 4 -30 -20 -10 0 10Zonal Wind (m s') Zonal Wind (m s')
January July
I. . . . . . . . . I0
200
E 400a)
' 600(n(u
aL 800
1000
-1
E0
3n
0-
400
500
600
700
800
900
1000
1100
. . . I ~ ~ ~ I . ~~ - ~ . . . I
I.... I
0
200
E 400
' 600I)0)
a- 800
1000
i
I
II . .
Figure 60. As in Figure 42, but for Australia.
166
-60 -40 -20 0Temperature ("C)
January
20 40
2 4 6 8 10Specific Humidity (g kg"')
January
0 5 10 15 20Zonal Wind (m s')
January
E-o2t0
0)
a
Q0
E
-o
:3
0
UnU)
QL
alb.
w,
12
solid = Ism, dosh = ccm2+
-80 -60 -40 -20Temperature ('C)
July
4UU
500
600
700
800
900
1000
11000
0
200
400
600
800
1000
25
0 20
1 2 3 4 5Specific Humidity (g kg"')
July
6
-10 0 10 20 30 40 50Zonal Wind (m s')
July
&.
E
DV)u)0
a.cu
2
:3
t0
Q.(U)
0
.0
0
0
200
400
600
800
1000
-80
400
500
600
700
800
900
1000
11000
0 _
200
400
600
800
1000
-5
· _ I -v . . . . . . .A nt II........I. II II I I I ......... ........ ....... ..i. .. . ..
. I . I . . I ... i . . , _ . . -.. .. ... .... ....I ... ... ... I ..........
' ' ' I .* I .* . I I I I I 1. I w w * l
...................... .... ,
-
I
1
i
Figure 61. As in Figure 42, but for Greenland.
168
greenland
E
a.Q>
. ___--- ..................... I .......... .... ..I.... ...
-70 -60 -50 -40 -30 -20 -10Temperature (°C)
January
0.10 0.20 0.30 0.40Specific Humidity (g kg'')
January
-5 0 5 10Zonal Wind (m s"')
January
200
400
600
800
1000
Solid = Ism, dash = ccm2+
-60 -40 -20 0 20Temperature ('C)
July
400 ........ ...............................Cf _
1(
1
3uu
600
700
800
900
000
100 ....0.50 0
.0
E
a)U)L-In
a.
15
1 2 3Specific Humidity (g kg-')
July
-1 0 1 2Zonal Wind (m s"')
July
4
3 4
...... ;- ...
200
400
600
800
1000
Or
1-o-EG)
U)a)0)cn
a.
.0
Ea)
V)(n
CL
141(
1
.0
Ea)
0
2C
4C
60
80
100
I I I r I ·. . . .
---I . . . . . I . . . . . , Ii
Figure 62. As in Figure 42, but for Antarctica.
170
antarctica
-
D
U)a)
-60 -50 -40 -30 -20 -10 0Temperature ('C)
January
0.2 0.4 0.6 0.8Specific Humidity (g kg"')
January
0
200.0
E 400
600= 600uE0)
a- 800
1000
-4 -2 0 2Zonal Wind (m s')
January
1.0
-90 -80 -70 -60 -50 -40 -30Temperature (*C)
July
.111
.0
E
L
U)4)
a-
0.08 0.10 0.12 0.14 0.16 0.18Specific Humidity (g kg-')
July
o r . . I . I . . . . .20
.-
Ea,
La.
40
60
80
100
4 6
0
0 -
0
0
A
-5 0 5 10Zonal Wind (m s')
July
15
1-1
.0
E
U)U)
.-I01z
a.
.0
EU)
U)U)U)
L-
I. , .i I . .
I* . . ,I .,I ,..., I I r. J . I . .Iii IA
