NMIPNational Multicultural Interpreter Project
NMIPNuevo Movimiento Independentista Puertorriqueno (Spanish: New Puerto Rican Independence Movement)
NMIPNorthern Manhattan Immunization Partnership (New York)
NMIPNorth Mecklenburg Industrial Park (Huntersville, NC)
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Specific objectives of NMIP are to 1) unravel the major N cycling processes controlling [N.sub.2]O fluxes in each model and identify the uncertainty sources from modeling structure, input data, and parameters; 2) quantify the magnitude and spatial and temporal patterns of global and regional [N.sub.2]O fluxes during 1860-2015 and attribute the relative contributions of multiple environmental factors to [N.sub.2]O dynamics; and 3) provide a benchmark estimate of global/ regional [N.sub.2]O fluxes through synthesizing the multimodel simulation results and existing estimates from ground-based observations, inventories, and statistical/empirical extrapolations.
To minimize the uncertainty that results from input datasets, the NMIP provided consistent model driving datasets for all modeling groups.
Except for bottom-up model simulations, the NMIP also plans to synthesize multiple sources of terrestrial soil [N.sub.2]O emission data to provide a benchmark for evaluating model estimates.
At the current stage, the NMIP has included 10 ecosystem models with explicit terrestrial N cycling processes (Table 2; Fig.
In the NMIP, we applied the progressively reducing factor experimental scheme (i.e., first experiment includes all factors and then reduce one factor each time; the effect of this factor is equal to the difference between the previous and current experiment) to simulate the impacts of individual environmental factors on [N.sub.2]O fluxes.
Model input data and model results will be made available to the broader research community once the results of the first NMIP are published.
Based on model results, the NMIP team will provide multimodel ensemble estimates for terrestrial [N.sub.2]O fluxes at various scales from country, sector, continental, to global and also assess differences and uncertainties among participating models.