RESUMEN
In this paper, we introduce a methodology that can improve the estimations of Gross Primary Productivity (GPP) and ecosystem Respiration (Reco) processes at a regional scale. This method is based on a satellite data-driven approach which is suitable for regions like India where there exists a serious shortage of ground-based observations of biospheric carbon fluxes (e.g., Eddy Covariance (EC) flux measurements). We relied on the Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance for capturing vegetation dynamics in the Light-Use Efficiency (LUE)-based vegetation model. Further, we utilised recently available satellite-based Solar-Induced Fluorescence (SIF) and other variables such as Soil Moisture (SM) and Soil Temperature (ST) to refine the predictions of GPP and Reco. The methodology involves establishing a relationship between SIF and GPP for different vegetation classes over India. The SIF-GPP relationship established across the biomes was then used to correct the GPP fluxes simulated by the LUE-based model. Similarly, the ecosystem respiration estimations by the model have undergone refinement by incorporating ST and SM information. This innovative method shows remarkable potential to improve biospheric CO2 uptake and release, especially for in situ data-constrained regions like India. ⢠SIF-based information is introduced to a light-use efficiency-based vegetation model. ⢠SIF-GPP relationship is established for major biomes across India. ⢠SM and ST information is incorporated into the Reco simulations in the model.
RESUMEN
A dry bias in climatological Central Indian rainfall plagues Indian summer monsoon (ISM) simulations in multiple generations of climate models. Here, using observations and regional climate modeling, we focus on a warm coastal Bay of Bengal sea surface temperature (SST) front and its impact on Central Indian rainfall. The SST front, featuring sharp gradients as large as 0.5 °C/100 km, is colocated with a mixed layer depth (MLD) front, in a region where salinity variations are known to control MLD. Regional climate simulations coupling a regional atmospheric model with an ocean mixed layer model are performed. A simulation with observed MLD climatology reproduces SST, rainfall, and atmospheric circulation associated with ISM reasonably well; it also eliminates the dry bias over Central India significantly. Perturbing MLD structure in the simulations, we isolate the SST front's impact on the simulated ISM climate state. This experiment offers insights into ISM climatological biases in the coupled NCEP Climate Forecast System version-2. We suggest that the warm SST front is essential to Central Indian rainfall as it helps to sustain deep and intense convection in its vicinity, which may be a source for the vortex cores seeding the monsoon low-pressure systems.
RESUMEN
Super El Niños, the strongest and most powerful of El Niños, impact economies, societies, and ecosystems disproportionately. Despite their importance, we do not fully understand how super El Niños develop their intensity and unique characteristics. Here, combining observational analyses with simple numerical simulations, we suggest that eastern Pacific intensified super El Niños result from the interaction of an El Niño and a positive Indian Ocean Dipole. Further, we identify a self-limiting behavior inherent to El Niño Southern Oscillation (ENSO) dynamics. This behavior-a consequence of the atmospheric Kelvin wave response that develops to the east of ENSO's convective anomalies-dampens sea surface temperature (SST) variations in the eastern Pacific, thereby preventing super El Niños from developing through tropical Pacific dynamics alone. Our model explains the features of the large 1972, 1982, and 1997 El Niños; the large SST anomalies during the 2015 El Niño, however, were likely enhanced by strong decadal variability.