Preprints
https://doi.org/10.5194/egusphere-2023-3051
https://doi.org/10.5194/egusphere-2023-3051
05 Jan 2024
 | 05 Jan 2024
Status: this preprint is open for discussion.

Evaluation of the coupling of EMACv2.55 and the land surface and vegetation model JSBACHv4

Anna Martin, Veronika Gayler, Benedikt Steil, Klaus Klingmüller, Patrick Jöckel, Holger Tost, Jos Lelieveld, and Andrea Pozzer

Abstract. We present the coupling of the Jena Scheme for Biosphere-Atmosphere Coupling in Hamburg (JSBACH) version four to the ECHAM/MESSy Atmospheric Chemistry (EMAC) model. With JSBACH, the soil water bucket model in EMAC is replaced by a diffusive hydrological transport model for soil water that includes water storage and infiltration in five soil layers, preventing too rapid soil drying and reducing biases in soil temperature and moisture. A five-layer snow scheme is implemented and phase changes of water in the soil are considered. Multiple land cover types are included to provide a state dependent surface albedo, which accounts for the absorption of solar radiation by vegetation. Plant net primary productivity, leaf area index and surface roughness are calculated according to the plant functional types. This paper provides a detailed evaluation of the coupled model based on observations and reanalysis data, including ERA5/ERA5-Land datasets, Global Precipitation Climatology Project (GPCP) data, and MODIS satellite data. In particular, land surface temperature (LST), terrestrial water storage (TWS), surface albedo (α), top of atmosphere radiation flux (RadTOA), precipitation (precip), leaf area index (LAI), fraction of absorbed photosynthetic active radiation (FaPAR) and gross primary productivity (GPP) are evaluated. The strongest correlation (r) between reanalysis data and the newly coupled model are found for LST (r = 0.985, with an average global bias of -1.546 K), α (r = 0.946, with an average global bias of -0.015) and RadTOA (r = 0.907, with an average global bias of 3.56, W m-2). Precipitation exhibits a correlation with the GPCP dataset of 0.523 and an average global bias of 0.042 mm day-1. The LAI climatology in EMAC has been substituted with a refined method for directly calculating LAI. This optimisation yields a correlation of 0.637 with observations and a global mean deviation of -0.212. FaPAR and GPP exemplify two of the many additional variables made available through JSBACH in EMAC. FaPAR and observations show a correlation of 0.663 with an average global difference of -0.223, while the correlation for GPP and observations is 0.564 and the average global difference is -0.001 kg Carbon km-1. Benefiting from the numerous added features within the simulated land system, the representation of soil moisture is improved, which is critical for vegetation modelling. This improvement can be attributed to a general increase in soil moisture and water storage in deeper soil layers, leading to a reduction in normalised root mean square error (NRMSE) and a closer alignment of simulated TWS with observations, mitigating the previously widespread problem of soil drought. The correlation of TWS and observations is 0.251 and the average global difference is 0.052 m water equivalent. We show that the numerous newly added components strongly improve the land hydrology, e.g. soil moisture, while surface parameters, which were mostly prescribed according to climatologies, remain similar. The coupling of JSBACH brings EMAC a step closer towards a holistic comprehensive Earth system model and extends its versatility.

Anna Martin, Veronika Gayler, Benedikt Steil, Klaus Klingmüller, Patrick Jöckel, Holger Tost, Jos Lelieveld, and Andrea Pozzer

Status: open (until 10 Mar 2024)

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Anna Martin, Veronika Gayler, Benedikt Steil, Klaus Klingmüller, Patrick Jöckel, Holger Tost, Jos Lelieveld, and Andrea Pozzer
Anna Martin, Veronika Gayler, Benedikt Steil, Klaus Klingmüller, Patrick Jöckel, Holger Tost, Jos Lelieveld, and Andrea Pozzer

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Short summary
The study evaluates the land surface and vegetation model JSBACHv4 as a replacement for the simplified submodel SURFACE in EMAC. JSBACH mitigates earlier problems of soil dryness, which are critical for vegetation modelling. When analysed with different data sets, the coupled model shows strong correlations for key variables such as land surface temperature, surface albedo and radiation flux. The versatility of the model is significantly increased, while the overall performance is not degraded.