Model Studies

CEOP has initiated a number of Modeling Studies organized around global, regional, and land surface models as well as a particular hydrologic applications component.

Global Models

http://gmao.gsfc.nasa.gov/research/modeling/validation/ceop.php

Chair: Mike Bosilovich (michael.bosilovich@nasa.gov)

Evaluating CEOP global analyses has primarily been through the single point MOLTS collocated with CEOP reference sites. To get at the intercomparison of global grids, CEOP is developing an ensemble global Model Analysis Comparison (MAC). This project serves several purposes. First, the variance of the analyses can provide a measure of uncertainty in analyses. It also provides a range of the state-of-the-art analyses. Second, this ensemble may make a better benchmark for comparing individual analyses than simply differencing one against another. We can also test the veracity of the ensemble against global independent observations (e.g. the Global Precipitation Climatology Project (GPCP), the International Satellite Cloud Climatology Project (ISCCP), urface Radiation Budget (SRB), etc). Lastly we would like to demonstrate the benefit of such a Multi- Model analysis for global atmospheric data assimilation systems for future longer-term studies.

Each of the global models contributing to CEOP has its own grid, frequency and variable list. MAC will provide a way for CEOP science activities to better accessibility of the data. In order to absolutely understand the results of an ensemble of analyses and the variance in the ensemble and outliers, the contributing NWP centers will be entrained into the MAC analysis. The centers themselves are best positioned to take the results, and review their model code and physical parameterizations to understand and improve their own analysis systems. Regardless, even external evaluations of the data have already contributed to some center's attention to specific biases and deficiencies. In addition the resulting MAC data set should be useful to other CEOP science activities, such as WEBS, Semi-Arid Regions and Extremes. The MAC data will be a simplified set of physical parameters and their range across the contributing models. The MAC scientific objective is to contribute to understanding the level of uncertainty to model analyses for these projects.

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Regional Climate Models

CEOP has a special focus on regional climate models, not only for particular regions, but also as part of an ongoing transferability intercomparison begun as a part of the previous ‘CEOP’. There are now two recognized regional model projects, Inter-Continental scale Transferability Study (ICTS) and Scale Interaction EValuation Experiment (SIEVE).

Inter-Continental Transferability Study (ICTS)

http://icts.gkss.de

Chair: Burkhardt Rockel (burkhardt.rockel@gkss.de)

Controlled numerical simulations of regional climates are currently being conducted over areas having fundamentally different climate regimes (e.g., tropical, midlatitude, polar) focused on particular climate characteristics (e.g., monsoons, low-level jets, mesoscale convective systems). In particular, the ICTS, which is an outgrowth of the former ‘CEOP’/WESP/ contribution, is making continuous multiple regional simulations to the CEOP model archive and in turn uses the CEOP global analyses, in-situ, and satellite data to evaluate these regional simulations. The goal of ICTS is to understand the physical processes underpinning the global water and energy cycles through systematic intercomparison of regional simulations of diverse climates to CEOP observations and global model analyses. This way the best parameterizations will be localized to simulate certain regional scale meteorological conditions, which we believe will also help to improve future global climate models.

To summarize, the main objectives of the ICTS are to:

Study the transferability of regional climate models to areas of different continental scale experiments (i.e. to different climate regimes).
Apply CEOP (satellite, reference sites, global analysis and model data) and other available observational data sets to evaluate the energy and water cycles in regional models.
Assess the influence of different driving global re-analyses.

In order to understand ensemble means, the variance in the ensemble and outliers, the contributing NWP centers will be entrained into the MAC analysis. They are best positioned to take the results, and review their model code and physical parameterizations to understand and improve their own analysis systems. In addition the resulting MAC data set should be useful to other CEOP science activities, such as WEBS, Semi-Arid Regions and Extremes.

The results from ICTS simulations in ‘CEOP’ will be further analyzed. Presently available data sets from RHPs are in use for these analyses. Upcoming new data sets in CEOP will also be taken into account. From several options for additional simulations in CEOP the following are the most likely ones:

Simulations with higher resolution.
Different analysis forcing for areas where differences between global analyses are significant.
Extend time period of simulation into CEOP.
Add new domain according to NEESPI.

Scale Interaction Evaluation Experiment (SIEVE)

Chair: Ray Arritt (rwarritt@bruce.agron.iastate.edu)

The unifying scientific question for the companion SIEVE is to clarify the mechanisms by which large-scale disturbances ultimately produce these extremes at regional scales. SIEVE will use nested regional climate models as tools to investigate these mechanisms. Large-scale aspects of seasonal extremes may have their origin as sea surface temperature anomalies (e.g., ENSO) or as planetary-scale circulation anomalies (e.g., the blocking pattern associated with the 2003 European heat wave). Regional-scale extremes then result or are intensified by interaction of these large-scale disturbances with regional processes such as orographic flows or land surface feedbacks. The latter can be diagnosed in detail from regional model results, or manipulated in controlled numerical experiments. This diagnosis also will help to uncover deficiencies in physical parameterizations when applied to such extremes, and will point to specific needs for model improvement.

The objective of SIEVE is to study the mechanisms by which large-scale climate anomalies are manifested as seasonal extremes on regional scales. These seasonal extremes occur on larger spatial and temporal scales than individual extreme events such as tropical storms or localized floods. Examples of such seasonal extremes are the summer 1993 regional flood over the central U.S. and the summer 2003 European heat wave.

SIEVE will interact with several CEOP projects. Interaction with MAC will be necessary to ascertain the uncertainty of analyses and data assimilation systems in representing both extreme large-scale anomalies and their regional manifestations. Such analyses are needed both as initial/boundary conditions and verification data for regional extremes in SIEVE. In addition, the potential exists for strong complementary between the primarily observation-oriented studies in Extremes and the primarily model-oriented studies of SIEVE.

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Land Surface Models (LSM)

http://ldas.gsfc.nasa.gov/GLDAS/docs/news.shtml

Chair: Matt Rodell (matthew.rodell@nasa.gov)

Land Surface Models have been developed and run by dozens of groups. There have been a few efforts to coordinate their activities, beginning with the local-scale Project for Intercomparison of Land Surface Parameterizations (PILPS) experiments. The first coordinated global scale land surface modeling activities were the Global Soil Wetness Project (GSWP) phases 1 and 2, which aimed to:

Produce state-of-the-art global data sets of land surface fluxes, state variables, and related hydrologic quantities.
Develop and test large-scale validation, calibration, and assimilation techniques over land.
Provide a large-scale validation and quality check of the ISLSCP data sets.
Compare Land Surface Schemes, and conduct sensitivity studies of specific parameterizations and forcings, which should aid future model and data set development.

With the completion of GSWP-2, several researchers present at the CEOP Implementation Planning Meeting in Washington DC expressed interest in forming a new working group "to coordinate global land modeling activities and share data, towards the goal of improved understanding and prediction of the land surface water and energy cycles at the global scale", which would contribute to CEOP research Objective 1. Land surface models can also contribute to Objective 4 by assessing the anthropogenic impacts on the water and energy cycles at regional to continental scales. New CEOP land surface modeling activities need to include groups traditionally active in CEOP LSM such as the University of Tokyo, the NASA/Goddard Space Flight Center (GSFC) Global Land Data Assimilation System (GLDAS) project, amongst others. Additionally, the RHPs must have modeling groups to run regionally developed LSMs and to help analyze model output for their region, since they are most familiar with their hydroclimate.

The CEOP LSM activity will identify, gather and analyze gridded global forcing data sets that are available for regional to global off-line LSM simulations. Current contributions by existing groups include contributions of both MOLTS and global, gridded model output datasets. GLDAS maintains a large archive of surface meteorological forcing data, land parameters, and output datasets, much of which is made publicly available. These data sets will be augmented by additional global forcing data sets (e.g. from Princeton University Land Hydrology Group, NCAR from A. Dai, and various reanalysis land surface meteorological data sets), and regional forcing data sets from the RHPs. In the later case it is expected that the RHP data sets will be of a higher quality than the available global sets.

The CEOP LSM activity will analyze the consistency among the data sets to help assess the uncertainty in the global terrestrial surface meteorology and radiation fields. The goal of the LSM activities under CEOP, then, is to generate physically coherent fields of land surface states and fluxes by optimally merging disparate data products, and by using a suite of advanced land surface models, to estimate the terrestrial component of the Earth's energy budget and water cycle, including an estimate of the error. One approach is to utilize NASA’s Land Information System (LIS) software package, which is able to drive multiple LSMs at high resolutions with various user-defined configurations and forcing options, but alternative approaches for running multi-model systems need to be developed since many RHP regional models are not in the LIS software package.

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Hydrologic Applications Project (HAP)

Chair: Eric Wood (efwood@princeton.edu)

The Hydrologic Applications Project addresses CEOP’s research Objective 4 as it relates to demonstrating the value of GEWEX research, data sets and tools for assessing the consequences of climate predictions and global change for water resources. HAP is a CEOP activity that crosses ESSP projects, particularly with the GWSP. The HAP was formulated with the goals being:

Developing procedures for assessing current hydrologic conditions (nowcasting) through application of GEWEX supported data products, including remotely sensing.
Developing and testing of reliable, skillful hydrologic ensemble forecast procedures based on seasonal climate model forecasts.
Demonstrating that the procedures can be applied at scales useful for water resources through test-bed sites and demonstration projects.

Thus HAP will help foster and develop the science behind skillful ensemble hydrologic seasonal forecasts, and demonstrating their usefulness.

In addition, HAP will work with the IAHS Project on Ungauged Basins (PUB) to demonstrate how remote sensing data, land data assimilation products and hydrological prediction can improve the decisions made by water resource managers. This activity offers GEWEX science and data products to the applications community. GEWEX will also promote strategies to work more closely with the WMO Hydrology and Water Resources Department, operational hydrometeorological services and the United Nations Educational Scientific and Cultural Organization (UNESCO)’s International Hydrology Programme. These lead to HAP’s fourth goal:

Working with related projects, like Extremes, Hydrologic Ensemble Prediction EXperiment (HEPEX), the PUB, and other GEWEX panels, particularly the GMPP.

HAP activities will have a number of regional foci and test-bed activities. A very important activity is the development of a Global Earth Observation System of Systems (GEOSS) Asian Water Cycle Initiative (AWCI) being lead by the University of Tokyo. The regional water management concerns include water extremes (floods and droughts, characterized by the large fluctuations of the Asian-Australian monsoon rainfall system and subsequent large human and socio-economic impacts); water scarcity, pollution and environmental degradation in a region with the largest population increment in the world; and concerns about adverse impacts of global climate change. Equally important will be the development of RHP-based and lead HAP test-beds, which will assess similar issues as well as the usefulness of seasonal forecasts in managing water resources. The RHP activity requires the participation of RHP-based scientists, and an important contribution of the RHP coordinators is the identification of the relevant RHP test bed participants.

It is anticipated that HAP will contribute to CEOP through a variety of activities that include:

Launch at least two pilot projects in GEWEX RHP basins that will utilize hydrological ensemble prediction techniques. This would be done in conjunction with HEPEX initiative, and should get underway in 2007.
Downscale and evaluate seasonal climate re-forecasts over the RHP regions using NOAA and DEMETER seasonal models output archive. These re-forecasts need to be assessed by RHP scientists, and is an important contribution to HAP by the RHPs.
Evaluate seasonal hydrological forecasts from re-forecasting studies for the initial two pilot studies; and launch additional pilot studies in conjunction with RHP scientists.
Using GLDAS and the IAHS/PUB, launch several studies to demonstrate the value of using remote sensing data in un-gauged (or under-gauged) basins.
Contribute to the Global Land/Atmosphere System Study (GLASS) goal to evaluate the predictive capability from land initialization conditions to the prediction of terrestrial hydrological variables at different time scales.
In collaboration with GLDAS and IAHS/PUB, determine the contribution of remote sensing to hydrologic modeling, with particular focus on un-gauged and poorly gauged basins.
In collaboration with HEPEX, evaluate newly developed procedures for hydrologic ensemble generation and their impact on seasonal hydrologic prediction for the RHP pilot test beds.

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