This research project brings together scientists
at the Center for Ocean-Land-Atmosphere Studies (COLA) and the University of New
Hampshire, in collaboration with colleagues at CPTEC and the University of Săo
Paulo in Brazil to investigate seasonal-to-interannual variability and
predictability in the Amazonian climate system (atmosphere, ocean, land surface
and basin hydrology) with a suite of established coupled models and carefully
designed sensitivity studies.
The proposed work will attempt to determine the
local and remote influences of land and ocean on Amazonian climate variability,
to determine the contributions of land and ocean to climate predictability over
Amazonia as well as the potential limits of predictability, and to determine where
observational monitoring may be most effective to aid forecasting of climate
anomalies and associated risks (e.g., drought, fire, impacts to agriculture,
The work is divided into two phases. In Phase I,
an offline land data assimilation (LDA) built on the framework of the Global Soil
Wetness Project will be conducted over South America using the COLA Surface
Hydrology System (SHS) to generate uniform gridded ˝ resolution analyses of
surface water and energy balance terms, incorporating high-resolution rainfall
data and meteorological station data (e.g. from ANEEL) where available. This
product will be validated against in situ measurements from LBA-DIS and other
relevant data archives, as well as through use of a Water Transport Model (WTM) for basin-scale
streamflow validation. Also, the Regional Spectral Model (RSM) framework
will be incorporated into the COLA-GCM to provide improved resolution over South
America without sacrificing consistency of model physics.
In Phase II, coupled model integrations will be
used to investigate the interactions of components of the climate system, and
their impact on seasonal variability and predictability. A proven two-tier
forecast system will be used in hindcast mode. Tier one uses an anomaly coupled
ocean-atmosphere model system, including a land surface model, to generate
forecasts of SST. Tier two uses the forecast SSTs from the anomaly coupled model
as boundary conditions for an atmosphere-land-river coupled model system. This
framework will allow us to investigate the individual roles which the tropical
Pacific, tropical Atlantic, and Amazon land surface play in determining regional
climate by selectively removing feedback from the atmosphere in each region. We
will investigate not only feedbacks of ocean anomalies on climate over land, but
land anomalies on ocean circulation (via heat and moisture fluxes with the
atmosphere, or through effects on freshwater discharge to oceans), and the
impacts of climate variability on surface hydrology.
The proposed work will require four categories
of observational and analyzed data: land surface parameters necessary for the
SHS and WTM schemes, near surface meteorological data for the LDA effort of
Phase I, initial conditions of ocean and atmosphere for the coupled model
experiments of Phase II, and a wide range of data for validation of all elements
of the study.
Report of Proposed Work
What are the sources and limits of climate
predictability over the Amazon region? The climate of South America and its
surrounding oceans is marked by strong asymmetry about the equator, and varies
on a range of spatial and temporal scales. The seasonal to interannual
variations are strongly affected by the unique coastal geometry, distributions
of biota, and the steep topography of the Andes. Rainfall patterns over the
Amazon Basin are complicated by interactions with the oceanic Inter-Tropical
Convergence Zones (ITCZ) over the eastern Pacific (El Nińo related) and western
Atlantic Oceans, penetrating mid-latitude fronts, and the South Atlantic
Convergence Zone (SACZ). Because of the sharp meridional gradients in the mean
climate, small displacements in the maximum rainfall and Atlantic sea surface
temperature (SST) can lead to large climatic impacts, particularly on the
seasonal to interannual time scale.
a. Climate anomalies and the Amazon region
Climate anomalies can have significant impacts
on ecology and hydrometeorology in the Amazon Basin and surrounding areas. Water
resources in the entire Amazon Basin are affected by seasonal and interannual
variations in rainfall (Marengo 1995). Droughts lead to reduced agricultural
production and low river levels, which impede boat traffic and hydroelectric
generation, as evidenced by this year's El Nińo. Floods similarly disrupt
agriculture and transportation. Changing land use practices are driven by, and
may even affect climate variability. Slash and burn techniques for clearing
forest for planting or grazing contribute vast quantities of smoke to the
atmosphere while steadily changing the distribution of vegetation that regulates
the energy and water balance at the earth's surface. Outside the Amazon Basin,
periodic droughts in the Nordeste have grave consequences for local agriculture
and society. The atmospheric general circulation over South America advects
water vapor to subtropical and mid-latitude regions east of the Andes from over
the Amazon Basin. Virtually the entire continent is linked in some way to the
climate of Amazonia. An understanding of the causes for climate variations, be
they local or remote, can greatly aid planning and resource management in the
There are three large regions which appear to
exert some influence over climate in Amazonia: the tropical Atlantic Ocean, the
tropical Pacific Ocean, and Amazonia itself. Tropical Atlantic SST variability
has a profound influence on the region's climate variability. Anomalous
meridional gradients of SST over the equatorial Atlantic have a large impact on
the total rainfall over northeastern Brazil through the modulation of the ITCZ's
latitudinal position (Moura and Shukla 1981, Hastenrath and Greischar 1993,
Nobre and Shukla 1996). The climate variability over the Amazon and Nordeste
regions is further complicated by the fact that rainfall anomalies are also well
correlated with extreme phases of the El Nińo/Southern Oscillation (ENSO;
Kousky et al. 1984, Marengo et al. 1993, Aceituno 1988). Impacts are not
necessarily confined to regions in which the precipitation anomalies occur.
Variations in river discharge and floodplain inundation result from rainfall
anomalies that may be far upstream (Vörösmarty et al. 1996).
Most of the evidence for land feedbacks on
climate over tropical and subtropical continents come from a combination of
theory (Charney et al. 1977, Dickinson and Hanson 1984, Eltahir 1996) and
numerous deforestation modeling results (see Hahmann and Dickinson 1997 for a
partial review). Some observational evidence of trends in rainfall over Amazonia
exists (Cauduro Dias de Paiva et al. 1995, Marengo et al. 1998), but it is not
clear whether they can be ascribed to deforestation as can local changes in
surface fluxes (Gash and Nobre 1997). Although there has been some gradual
convergence, there is still a great deal of variation between estimates of the
climatic response to deforestation. It should be of greater concern to the
scientific community that so many studies of the potential impacts on climate of
tropical deforestation have been undertaken before a reasonable understanding of
the existing climate has been attained. Without a better comprehension of the
sources of climate variability over Amazonia, their relative importance and
potential predictability, there is little foundation upon which to pin
projections of deforestation impacts.
b. The need for high resolution modeling over
Understanding and predicting the climate of the
tropical South American region requires sophisticated high resolution coupled
ocean-land-atmosphere climate models that resolve the mesoscale details of the
orography and vegetation, the physics and dynamics of the continental monsoon
circulation, as well as the complex coupled interactions with the oceanic ITCZ
The complex terrain and steep topography of the
South American region in many ways dictates the need for very high resolution
models. The mesoscale model mountain/no-mountain experiments of Tanajura (1996)
suggest that the Andes act as a barrier that separates the low level circulation
associated with the subtropical high over the eastern South Pacific from the low
level circulation over South America. Simulating the subtle circulation features
associated with the Andes is beyond the resolution of current state-of-the-art
coupled general circulation models (GCMs).
Simulating the ITCZ and SACZ also dictates the
need for high resolution coupled models. For example, the northern branch of the
ITCZ extends across much of the basin, merges with the continental monsoons of
central and northern South America, but is meridionally confined to only a few
degrees of latitude between 5oN-10oN. The distribution of
rainfall over the South American continent also has great deal of mesoscale
structure, particularly in the Amazon basin extending southeast, and in the
vicinity of the Andes mountains. Even on the seasonal to annual time scales,
large differences in the distribution of precipitation are seen between high
resolution observed precipitation data sets and relatively low resolution model
simulations, as well as between low and high resolution precipitation data sets
(Costa and Foley 1998). Given these sharp rainfall gradients, any small spatial
scale displacements lead to large droughts or floods.
Non-coupled regional model simulations of Amazon
climate (da Rocha 1998) and deforestation impacts have been conducted. However,
these studies have generally been focused on local land-atmosphere feedbacks,
and not the larger scale influences of neighboring oceans.
Many areas of Amazonia and the Pantanal region,
when viewed at GCM grid resolutions have greater than 10% open water coverage.
The data set of Matthews (1989) indicates numerous areas of wetlands greater
than 20% coverage. The low resolution of global GCMs cannot resolve even large
features such as the Amazon flood plain or the Pantanal, which may have
significant impacts on regional energy and water fluxes between land and the
atmosphere. Patchiness in the pattern of vegetation and deforestation, evidenced
in satellite images over Rôndonia, is also impossible for low-resolution GCMs
to represent. Higher resolution will allow better representation of these areas
in the models. Data sets now exist, or are being compiled as part of LBA, which
make high-resolution studies more justifiable than ever before.
Coupling to river flux models over regions and
sub-basins also requires higher resolutions than traditional GCMs can supply.
For instance, the ˝ river topology of R-HydroNET (Vörösmarty et al. 1997)
could be used in dynamical prediction models if the atmospheric component were
at a comparable resolution. Higher resolution in climate models will greatly
facilitate such hydrologic studies.
The work we are proposing entails:
||Producing hindcasts of seasonal climate and|
river discharge for validation during the period ca.1968 to present.
|Conducting sensitivity studies to examine the|
relative roles of slowly evolving surface boundary conditions in determining
the variability of Amazonian climate.
|Producing budgets and climatologies of energy|
and water balance terms over Amazonia.
|Examining the utility of various LBA data|
sets for data assimilation in climate models, and their impact on climate
The proposed research has the following scientific goals:
|Determine local and remote influences of land|
and ocean on Amazonian climate variability.
|Determine the contributions of land and ocean|
to climate predictability over Amazonia and determine the potential limits
|Determine where observational monitoring may|
be most effective to aid forecasting of climate anomalies, and associated
risks (drought, fire, impacts to agriculture, transportation, etc.).
The work proposed falls into activity type 2, modeling,
as defined in the NRA. We directly address the MTPE element of seasonal to
interannual climate variability and predictability. The proposed work is
relevant to the LBA research areas of physical climate and land
This proposal addresses a number of the priority
topics spelled out in the NRA. We will utilize model experiments to assess the
interaction of the land surface in the Amazon region with the global scale
atmosphere and ocean using an established two-tier seasonal prediction scheme
with improved parameterizations of the land surface and terrestrial hydrology.
An established land surface data assimilation methodology will be enhanced with
data from pre-LBA and LBA-DIS data sets to estimate the terms of the surface
water and energy budgets over the Amazon basin and surrounding regions. This
will include estimation of the seasonal cycles and interannual variability in
the hydrologic cycle and creation of analyses of land surface conditions to
initialize seasonal coupled model integrations. Water transport modeling will be
included to assess the sensitivity of streamflow to climate variability, and as
a means of validation of simulated hydrologic balance. Coupling of atmosphere,
ocean, land surface and water transport models will create a fully closed
hydrologic system over the Amazonian region. This closure will allow
investigation of the interactions between these components, and a uniquely
thorough assessment of the roles of each component within the climate system.
This work therefore supports the goals of several international scientific
efforts, most notably IGBP, and WCRP.
3. Research Envisioned
The proposed research consists of two phases,
each expected to require approximately half of the three-year period to complete
(see the Management Plan at the end of this proposal). Phase I must be completed
in order to execute Phase II. Each phase is described below.
a. Phase I
Using the framework developed in the Global Soil
Wetness Project (GSWP; IGPO 1995, Dirmeyer et al. 1998), we will perform global
and regional (Amazon region) assimilation of observed, remotely sensed, and
analyzed meteorological and radiance data into the COLA surface hydrology system
(SHS) for the period 1986-present. The older COLA land surface scheme as used in
GSWP performed quite well in simulating the mean and annual cycle of runoff over
the Amazon Basin (Oki et al. 1997). As in GSWP, this land data assimilation (LDA)
will be performed without coupling to an atmospheric model. Our focus will be on
assimilation over the South America, capitalizing on unique data available over
the region from LBA-DIS and other sources (e.g., C. Willmott, personal
communication). Time varying remote sensing data of vegetation cover properties
will be combined with taxonomic information on vegetation and soils to update
the parameterizations of the terrestrial biosphere and soil hydrology models.
The LDA effort will produce two important data
sets. The first is a multi-year data set of global and regional surface
hydrologic and energy data. Being the product of a land surface model driven by
gridded observations and analyses, the multi-year data set will be among the
best continental-scale data sets that can be produced. Using the Water Transport
Model (WTM) of Vörösmarty et al. (1989, 1996), we will compare our surface
hydrologic estimates to streamflow data, and compare selected grid point
simulations to LBA field site measurements for purposes of validation. The data
will be available for use and comparison by the community after submission to
the appropriate LBA-DIS center, according to the LBA-DIS regulations. Second, as
a product of this assimilation, land surface initial conditions and boundary
conditions for the coupled model experiments of Phase II will be created. They
will be fully consistent with the global and regional model, since the same land
surface scheme will be used both for the assimilation and the coupled modeling.
To address the need for high resolution, we have
chosen to use a regional spectral model (RSM, Juang et al., 1997) incorporated
into the COLA GCM. The primary reason for choosing the RSM procedure over nested
grid-point models is that the physics and dynamics of the regional model are
identical to those of the host global model. This is particularly advantageous
since the identical land surface model can be used at all scales. The RSM,
developed and used at the National Centers for Environmental Prediction (NCEP),
has proven to be comparable in skill with the operational Eta weather prediction
model (Juang et al., 1997). Implementation and testing of the global/regional
model system will be conducted with enhanced resolution over the South American
domain. We plan to implement a resolution of ˝ over most of South America and
adjacent regions of the Pacific and Atlantic Oceans, with a region of higher ź
resolution over the Amazon Basin.
b. Phase II
We will bring together the developments of Phase
I to investigate the predictability of climate over the Amazon basin and
surrounding areas, and document the seasonal to interannual variability of the
region. This will be done in two ways. We will use a two-tiered approach to make
historical forecasts (hindcasts) of climate and check the validity of the
results as a function of location and season to determine the practical and
theoretical limits of predictability over Amazonia. We will also perform
sensitivity studies where greater control is exercised over the boundary
conditions of the climate model to isolate the relative impacts of the Pacific
Ocean, Atlantic Ocean, and regional land surface conditions on the climate of
the Amazon Basin.
To understand climate predictability, one must
first demonstrate that the two-tiered coupled model-RSM prediction system
produces skillful forecasts. We will perform a series of two tiered coupled
model-RSM hindcasts of South American climate variability. Tier one of the
prediction system is a global anomaly coupled GCM (the COLA atmospheric GCM
coupled to the Modular Ocean Model of Pacanowski 1995) which, in an earlier
Pacific-only version, has been successfully used for ENSO prediction (Kirtman et
al. 1997). It should be noted that the complete land surface parameterization is
used in tier one (see Table 1). A more recent version of the global anomaly
coupled model is currently being tested for SST forecasts in both Pacific and
Atlantic. Integrations of 6-9 months will be performed for each period of
interest, with the first three months discarded to help remove the influence of
initial conditions on the climate simulations.
In addition to control experiments (Control 1
in Table 2), a number of sensitivity experiments will be conducted using the
tier-one models. The interactive nature of selected components of the surface
will be shut off to investigate the separate roles that the (a) Pacific Ocean ,
(b) Atlantic Ocean, and (c) Amazonian land mass play in determining climate
variability. The goal of these sensitivity studies is to examine how the climate
of the Amazon interacts with, and is affected by the nearby oceans. Several
different case studies will be made. For example, we will repeat the forecast/hindcast
of selected El Nińo and La Nińa years without an interactive Atlantic Ocean.
Similarly, we will deactivate the Pacific Ocean or the land surface processes in
Amazonia. By comparing these sensitivity studies we can identify the relative
roles played by the Atlantic and Pacific SST anomalies and local land surface
feedbacks in determining South American anomalies in the climate system. Cases
where there are strong SST anomalies in the Atlantic will also be considered.
For the prescribed Amazon, climatological soil moisture determined from the LDA
of Phase I will be specified over South America to assess the potential
influence of land surface feedbacks on regional climate, including ocean
circulation. In addition, we will also investigate how simulated freshwater
discharge from the Amazon basin affects the simulation of the Atlantic
circulation. The proposed fresh water discharge experiments are not intended to
fully explore how river runoff affects ocean circulation. However, these
experiments will identify the potential importance of Amazon discharge on local
ocean climate. Table 2 provides a concise depiction of the planned experiments.
Table 1. Modeling components of Tier 1 hindcasts.
|Tier 1 |
|COLA-ODA (Obs. SST prior to|
(Coupled to WTM in River 1 experiment)
Directly coupled to SHS
coupled to GCM
(WTM coupled to MOM in River 1
Table 2. Experiments planned for Tier 1.
|Tier 1 Experiments|
|Coupled to GCM|
|Coupled to GCM|
|Coupled to GCM|
|Coupled to GCM|
|Coupled to GCM|
|Coupled to GCM|
|Coupled to GCM|
|Coupled to GCM (and WTM|
coupled to MOM)
|Coupled to GCM|
|Coupled to GCM|
|Coupled to GCM|
|Coupled to GCM|
|Cases to Examine|
|El Nińo and La Nińa years,|
large Atlantic anomalies
|El Nińo and La Nińa years|
|Large Atlantic SST anomalies|
|Selection from Control 1|
|Selection from Control 1|
variability interacts with Amazon
variability interacts with Amazon
|How Amazon land|
variability affects SST
discharge affects SST
In tier two, we will use the RSM procedure
incorporated into the COLA GCM (see Table 3). While the tier one global coupled
forecasts are mainly designed to produce SST forecasts, the focus of tier two
will be on ensemble climate prediction over the Amazon and environs using the
RSM. The tier one forecasts of SST anomaly will be used as ocean boundary
conditions for tier two. Probabilistic hindcasts of rainfall, temperature,
circulation features, and streamflow will be made and evaluated for
skillfulness. Validation against observed meteorological and hydrological data
will be performed.
Table 3. Modeling components of Tier 2 hindcasts.
|Tier 2 |
|Coupling to Atmosphere|
|Prescribed from Tier 1|
|Prescribed from Tier 1|
|GCM + RSM|
|Directly coupled to SHS|
|SHS + WTM|
|Directly coupled to GCM|
In order to assess the relative importance of
Atlantic and Pacific SST in influencing South American climate, we will specify
climatological SST in either the Atlantic or the Pacific. By comparing the
results with a control simulation that has tier one forecast SST in both basins,
we can assess the impact of SST variability (see Table 4). We will similarly
specify land surface conditions (vegetation properties, soil moisture) from our
Phase I LDA to isolate the regional land surface's role in governing the
region's climate. The upper limit of predictability for the RSM system will be
assessed using integrations with "perfect" boundary conditions, which
represent the ideal case where the future state of land and ocean are known
exactly in advance of the forecast. In both hindcasts and sensitivity studies,
the WTM will be used to produce time variations in river flow for validation and
comparison. Appropriate subsets of the data and analyses produced as a result of
the two tier system will also be submitted to the respective LBA-DIS center,
following the regulations that establish terms of data protocol, data policies,
Table 4. Experiments planned for Tier 2.
|Tier 2 Experiments|
|Prescribed Pacific 1|
|Prescribed Atlantic 1|
|Operational-style forecast |
|How Pacific variability|
|How Atlantic variability|
|How Amazon variability|
|Upper limit of|
predictability (perfect BCs)
Validation efforts will be greatly enhanced by
the participation of Dr. José Marengo of CPTEC. Dr. Marengo brings experience
in the LBA-Data Information System, and will collaborate in the hydrological
validation of Phase I LDA integrations and both the meteorological and
hydrological validation of Phase II hindcasts. He is also working independently
with scientists at the University of Săo Paulo and CIRES to estimate the
current water balance of the region, its seasonal and interannual variations
using aerologic, precipitation and river data. Those results will be used for
We will also collaborate with Prof. H. R. da
Rocha at the University of Săo Paulo. Prof. da Rocha is investigating the
impact of land surface and atmospheric heterogeneity below the scale of
hydrostatic models and its bearing on convective precipitation with the
mesoscale RAMS model coupled to SiB2 (Sellers et al. 1996a). We will investigate
how changes in Amazonian water balance would affect convection within and beyond
Amazonia, using the global-scale modeling results from tier two as initial and
lateral boundary conditions for RAMS integrations. Comparison between comparable
integrations with RSM and RAMS, particularly for those cases where RSM performs
poorly, will shed light on regional modeling uncertainties, and the roles that
different physics and coupling strategies may have on climate simulation over
Amazonia. This collaborative work would compliment the studies proposed above,
but would not be directly supported by an award from this opportunity.
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