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PC-04 Abstract

Coupled High-Resolution Ocean-Land-Atmosphere Simulation of Seasonal-Interannual Climate Variability over Amazonia

Paul A Dirmeyer — Inst of Global Envir & Society Inc (US-PI)
Jose A. Marengo — CPTEC - Centro de Previsăo do Tempo e Estudos Climáticos (INPE) (SA-PI)
Humberto Rocha — USP (Universidade de Sao Paulo) (SA-PI)

Project Summary

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,

transportation, etc.).


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

1. Motivation

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

region.


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

Amazonia


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

and SACZ.


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.


 


2. Goals

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

predictability.


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

of predictability.
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

surface hydrology.


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.


 


















































Tier 1 


Components
Model Initial Conditions Coupling
Ocean MOM COLA-ODA (Obs. SST prior to

1986)
Anomaly coupled

to GCM 


(Coupled to WTM in River 1 experiment)
Atmosphere COLA-GCM NCEP Anomaly coupled

to MOM 


Directly coupled to SHS
Land SHS COLA-LDA Directly

coupled to GCM 


(WTM coupled to MOM in River 1

experiment)
Table 1. Modeling components of Tier 1 hindcasts.


 


































































































Tier 1 Experiments Control 1 Prescribed

Pacific 1
Prescribed

Atlantic 1
Prescribed

Amazon 1
River 1
Pacific Coupled to GCM Climatology Coupled to GCM Coupled to GCM Coupled to GCM
Amazon Coupled to GCM Coupled to GCM Coupled to GCM LDA Climatology Coupled to GCM (and WTM

coupled to MOM)
Atlantic Coupled to GCM Coupled to GCM Climatology 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
Objective Baseline

predictability integrations 
How Atlantic

variability interacts with Amazon
How Pacific

variability interacts with Amazon
How Amazon land

variability affects SST
How river

discharge affects SST 
Table 2. Experiments planned for Tier 1.


 


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.


 


















































Tier 2 


Components
Model Initial Conditions Coupling to Atmosphere
Ocean Prescribed from Tier 1 Prescribed from Tier 1 None
Atmosphere GCM + RSM NCEP Directly coupled to SHS
Land SHS + WTM COLA-LDA Directly coupled to GCM
Table 3. Modeling components of Tier 2 hindcasts.


 


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,

metadata, etc.


 


































































Tier 2 Experiments Control 2 Prescribed

Pacific 2
Prescribed

Atlantic 2
Prescribed

Amazon 2
Perfect 2
Ocean Control 1 Prescribed Pacific 1 Prescribed Atlantic 1 Control 1 Observed
Land Interactive Interactive Interactive Climatology LDA
  Operational-style forecast  How Pacific variability

impacts climate
How Atlantic variability

impacts climate
How Amazon variability

impacts climate
Upper limit of

predictability (perfect BCs)
Table 4. Experiments planned for Tier 2.


 


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

cross-validation.


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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March 1999  

 







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