PI: Paul Dirmeyer Center
for Ocean-Land-Atmosphere Studies dirmeyer@cola.iges.org
Co-I: Ben Kirtman Center for
Ocean-Land-Atmosphere Studies kirtman@cola.iges.org
Co-I: Charles J. Vörösmarty
University
of New Hampshire charles.vorosmarty@unh.edu
Collaborator: Humberto R. da RochaUniversidade
São Paulo, Brazil humberto@model.iag.usp.br
Collaborator: José A. MarengoCPTEC,
Brazil marengo@cptec.inpe.br
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 São 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; determine the contributions of land and ocean to climate predictability over Amazonia as well as the potential limits of predictability; and 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 framework of the Global Soil Wetness Project will be conducted over South America using the COLA Surface Hydrology System (SHS) to generate uniform gridded ½ resolution analyses of surface water and energy balance terms, incorporating high-resolution rainfall data, and meteorological station data (e.g. from DNAEE) where available. This product will be validated against in situ measurements from LBA-DIS and other relevant data archives, and using a Water Transport Model (WTM) for basin-scale streamflow validation, and. 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 affects 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.