Understanding the natural and forced variability of the general circulation of the atmosphere and its drivers is one of the grand challenges in climate science. In particular, it is of paramount importance to understand to what extent the systematic error of global climate models affects the processes driving such variability. This is done by performing a set of simulations (ROCK experiments) with an intermediate complexity atmospheric model (SPEEDY), in which the Rocky Mountains orography is modified (increased or decreased) to influence the structure of the North Pacific jet stream. For each of these modified-orography experiments, the climatic response to idealized sea surface temperature (SST) anomalies of varying intensity in the El Niño Southern Oscillation (ENSO) region is studied. ROCK experiments are characterized by variations in the Pacific jet stream intensity whose extension encompasses the spread of the systematic error found in state-of-the-art climate models. When forced with ENSO-like idealised anomalies, they exhibit a non-negligible sensitivity in the response pattern over the Pacific North American region, indicating that a change/bias in the model mean state can affect the model response to ENSO. It is found that the classical Rossby wave train response generated by ENSO is more meridionally oriented when the Pacific jet stream is weaker, while it exhibits a more zonal structure when the jet is stronger. Rossby wave linear theory, used here to interpret the results, suggests that a stronger jet implies a stronger waveguide, which traps Rossby waves at a lower latitude, favouring a more zonally oriented propagation of the tropically induced Rossby waves. The shape of the dynamical response to ENSO, determined by changes in the intensity of the Pacific Jet, affects in turn the ENSO impacts on surface temperature and precipitation over Central and North America. Furthermore, a comparison of the SPEEDY results with CMIP6 models behaviour suggests a wider applicability of the results to more resources-demanding, complete climate GCMs, opening up to future works focusing on the relationship between Pacific jet misrepresentation and response to external forcing in fully-fledged GCMs.
<p>Land-atmosphere interactions are largely controlled by vegetation, which is dynamic across spatial and temporal scales. Most state-of-the-art land surface models do not adequately represent the temporal and spatial variability of vegetation, which results in weaknesses in the associated variability of modelled surface water and energy states and fluxes. The objective of this work is to evaluate the effects of integrating spatially and temporally varying vegetation characteristics derived from satellite observations on modelled evaporation and soil moisture in the land surface model HTESSEL. Specifically, model fixed land cover was replaced by annually varying land cover, and model seasonally varying Leaf Area Index (LAI) was replaced by seasonally and inter-annually varying LAI. Additionally, satellite data of Fraction of green vegetation Cover (FCover) was used to formulate and integrate a spatially and temporally varying model effective vegetation cover parameterization. The effects of these three implementations on model evaporation and soil moisture were analysed using historical offline (land-only) model experiments at a global scale, and compared to reference datasets.</p><p>The enhanced vegetation variability lead to considerable improvements in correlation of anomaly evaporation and surface soil moisture in semiarid regions during the dry season. These improvements are related to the adequate representation of vegetation-evaporation-soil moisture feedback mechanisms during water-stress periods in the model, when integrating spatially and temporally varying vegetation. These findings emphasize the importance of vegetation variability for modelling land surface-atmosphere interactions, and specifically droughts. This research contributes to the understanding and development of land surface models, and shows that satellite observational products are a powerful tool to represent vegetation variability.</p>
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