14 Apr 2023
 | 14 Apr 2023
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Modeling atmosphere-land interactions at a rainforest site – a case study using Amazon Tall Tower Observatory (ATTO) measurements and reanalyis data

Amelie U. Schmitt, Felix Ament, Alessandro C. de Araújo, Marta Sá, and Paulo Teixeira

Abstract. Modeling the interactions between atmosphere and soil at a forest site remains a challenging task. Using tower measurements from the Amazon Tall Tower Observatory (ATTO) in the rainforest, we evaluated the performance of the land model JSBACH focusing especially on processes influenced by the forest canopy.

As a first step, we analyzed whether ERA5 and MERRA-2 reanalysis data are suitable to be used as land model forcing. Comparing five years of ATTO measurements to near-surface reanalysis data, we found a substantial underestimation of wind speeds by about 1 m s−1. ERA5 captures monthly mean temperatures quite well but overestimates annual mean precipitation by 30 %. Contrarily, MERRA-2 overestimates monthly mean temperatures in the dry season (August–October) by more than 1 K, while mean precipitation biases are small.

To test how much the choice of reanalysis data set and the reanalysis biases affect the results of the land model we performed spin-up and model runs using either ERA5 or MERRA-2 and with and without a bias correction for precipitation and wind speed and compared the results. The choice of reanalysis data set results in large differences of up to 1.3 K for soil temperatures and 20 % for soil water content, which are non-negligible especially in the first weeks after spin-up. Correcting wind speed and precipitation biases also notably changes the land model results – especially in the dry season.

Based on these results, we constructed an optimized forcing data set using bias-corrected ERA5 data for the spin-up period and ATTO measurements for a model run of two years and comparing the results to observations to identify model shortcomings. Generally, the shape of the soil water profile is not reproduced correctly, which might be related to a lack of vertical variability of soil properties or of the root density. The model also shows a positive soil temperature bias and overestimates the penetration depth of the diurnal cycle. This problem could possibly be addressed by including a separate canopy layer into the model to improve the processes related to storage and vertical transport of energy within the model.

Amelie U. Schmitt et al.

Status: open (until 04 Jun 2023)

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Amelie U. Schmitt et al.

Amelie U. Schmitt et al.


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Short summary
Tall vegetation in forests affects the exchange of heat and moisture between the atmosphere and the land surface. We compared measurements from the Amazon Tall Tower Observatory to results from a land model to identify model shortcomings. Our results suggest that soil temperatures in the model could be improved by incorporating a separate canopy layer which represents the heat storage within the forest.