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Title: Large-scale Validation of AMIP II Land-surface Simulations: Preliminary Results for Ten Models

Abstract

This report summarizes initial findings of a large-scale validation of the land-surface simulations of ten atmospheric general circulation models that are entries in phase II of the Atmospheric Model Intercomparison Project (AMIP II). This validation is conducted by AMIP Diagnostic Subproject 12 on Land-surface Processes and Parameterizations, which is focusing on putative relationships between the continental climate simulations and the associated models' land-surface schemes. The selected models typify the diversity of representations of land-surface climate that are currently implemented by the global modeling community. The current dearth of global-scale terrestrial observations makes exacting validation of AMIP II continental simulations impractical. Thus, selected land-surface processes of the models are compared with several alternative validation data sets, which include merged in-situ/satellite products, climate reanalyses, and off-line simulations of land-surface schemes that are driven by observed forcings. The aggregated spatio-temporal differences between each simulated process and a chosen reference data set then are quantified by means of root-mean-square error statistics; the differences among alternative validation data sets are similarly quantified as an estimate of the current observational uncertainty in the selected land-surface process. Examples of these metrics are displayed for land-surface air temperature, precipitation, and the latent and sensible heat fluxes. It ismore » found that the simulations of surface air temperature, when aggregated over all land and seasons, agree most closely with the chosen reference data, while the simulations of precipitation agree least. In the latter case, there also is considerable inter-model scatter in the error statistics, with the reanalyses estimates of precipitation resembling the AMIP II simulations more than to the chosen reference data. In aggregate, the simulations of land-surface latent and sensible heat fluxes appear to occupy intermediate positions between these extremes, but the existing large observational uncertainties in these processes make this a provisional assessment. In all selected processes as well, the error statistics are found to be sensitive to season and latitude sector, confirming the need for finer-scale analyses which also are in progress.« less

Authors:
; ; ; ;
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
928178
Report Number(s):
UCRL-TR-217474
TRN: US200815%%517
DOE Contract Number:
W-7405-ENG-48
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; 54 ENVIRONMENTAL SCIENCES; AIR; CLIMATES; FOCUSING; GENERAL CIRCULATION MODELS; METRICS; PRECIPITATION; SEASONS; SIMULATION; STATISTICS; SURFACE AIR; VALIDATION

Citation Formats

Phillips, T J, Henderson-Sellers, A, Irannejad, P, McGuffie, K, and Zhang, H. Large-scale Validation of AMIP II Land-surface Simulations: Preliminary Results for Ten Models. United States: N. p., 2005. Web. doi:10.2172/928178.
Phillips, T J, Henderson-Sellers, A, Irannejad, P, McGuffie, K, & Zhang, H. Large-scale Validation of AMIP II Land-surface Simulations: Preliminary Results for Ten Models. United States. doi:10.2172/928178.
Phillips, T J, Henderson-Sellers, A, Irannejad, P, McGuffie, K, and Zhang, H. Thu . "Large-scale Validation of AMIP II Land-surface Simulations: Preliminary Results for Ten Models". United States. doi:10.2172/928178. https://www.osti.gov/servlets/purl/928178.
@article{osti_928178,
title = {Large-scale Validation of AMIP II Land-surface Simulations: Preliminary Results for Ten Models},
author = {Phillips, T J and Henderson-Sellers, A and Irannejad, P and McGuffie, K and Zhang, H},
abstractNote = {This report summarizes initial findings of a large-scale validation of the land-surface simulations of ten atmospheric general circulation models that are entries in phase II of the Atmospheric Model Intercomparison Project (AMIP II). This validation is conducted by AMIP Diagnostic Subproject 12 on Land-surface Processes and Parameterizations, which is focusing on putative relationships between the continental climate simulations and the associated models' land-surface schemes. The selected models typify the diversity of representations of land-surface climate that are currently implemented by the global modeling community. The current dearth of global-scale terrestrial observations makes exacting validation of AMIP II continental simulations impractical. Thus, selected land-surface processes of the models are compared with several alternative validation data sets, which include merged in-situ/satellite products, climate reanalyses, and off-line simulations of land-surface schemes that are driven by observed forcings. The aggregated spatio-temporal differences between each simulated process and a chosen reference data set then are quantified by means of root-mean-square error statistics; the differences among alternative validation data sets are similarly quantified as an estimate of the current observational uncertainty in the selected land-surface process. Examples of these metrics are displayed for land-surface air temperature, precipitation, and the latent and sensible heat fluxes. It is found that the simulations of surface air temperature, when aggregated over all land and seasons, agree most closely with the chosen reference data, while the simulations of precipitation agree least. In the latter case, there also is considerable inter-model scatter in the error statistics, with the reanalyses estimates of precipitation resembling the AMIP II simulations more than to the chosen reference data. In aggregate, the simulations of land-surface latent and sensible heat fluxes appear to occupy intermediate positions between these extremes, but the existing large observational uncertainties in these processes make this a provisional assessment. In all selected processes as well, the error statistics are found to be sensitive to season and latitude sector, confirming the need for finer-scale analyses which also are in progress.},
doi = {10.2172/928178},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Dec 01 00:00:00 EST 2005},
month = {Thu Dec 01 00:00:00 EST 2005}
}

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  • This report presents analyses of sixteen models from the Atmospheric Model Intercomparison Project II (AMIP2) over the Australian region. It is focused on assessing how well surface climate and fluxes over this region are simulated in current Atmospheric General Circulation Models (AGCMs) forced by observed sea surface temperatures (SSTs). The importance of land-surface modeling on model predictability is also investigated. In this preliminary analysis, the Bureau of Meteorology (BoM) observational rainfall, temperature and surface evapotranspiration datasets are used in validating surface climatologies simulated by the 16 models. Specifically, the Linear Error in Probability Space (LEPS) score is calculated in assessingmore » the skill of the models in simulating surface Climate anomalies for the 17-year period (1979 to 1995). Numerous model differences are seen with some aspects of the model performance being linked to the complexity of land-surface schemes used. The connection between model skill in simulating surface climate anomalies and surface flux anomalies is explored. Lag-correlation analysis is conducted. Results reveal that ''climatic memory'' derived from land-surface processes (e.g: soil moisture) has different features in the sixteen models: some models show rapid feedback processes between land-surface and the overlying atmosphere, while others show slowly varying processes in which anomalous surface conditions have impacts on the model integrations on longer time-scales. It is found that models with simple bucket-type scheme tend to have a more rapid decay rate in the retention of soil moisture anomalies, and therefore, soil moisture conditions have a much weaker influence on forecasting surface climate anomalies. This study suggests that land-surface modeling has the potential to influence AGCM predictability on seasonal and even longer time scales.« less
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