Long-term annual and seasonal mass balance reconstruction and sensitivity analysis of Chhota Shigri Glacier in Western Himalaya.

Glaciers, in general, are sensitive to changes in the climate but Himalayan glaciers, in particular, are highly affected by climate change. Mass balance (MB) of glaciers is one of the important parameters to examine the response of glaciers to climate variability and change. The study of mass balance sensitivity (MBS) due to climate perturbations for glaciers is also important to understand future behavior of the glaciers. For Chhota Shigri Glacier, research on the estimation of long-term annual and seasonal MB and MBS as well as equilibrium-line altitude (ELA) and accumulation area ration (AAR) sensitivity analysis is not reported in detail. Accordingly, the present study carries out a detailed analysis of annual and seasonal MBS from 1953 to 2014 using annual and monthly climate perturbations as well as ELA and AAR sensitivities for the Chhota Shigri Glacier. The long-term annual and seasonal MB of Chhota Shigri Glacier from 1953 to 2014 is reconstructed using distributed temperature-index model by simulating minimal model parameters, namely melt factor, snow, and ice radiations using Monte-Carlo simulation. The mean annual MB of Chhota Shigri was -0.28 ± 0.41 m w.e./year during 1953-2014. The annual MB decreased from - 0.09 ± 0.41 m w.e./year (1953-1968) to - 0.57 ± 0.41 m w.e./year (2000-2014). The estimated MBS of Chhota Shigri Glacier is 0.57 m w.e./°C due to temperature change which is high and can be attributed to the presence of significantly less debris-covered ice in Chhota Shigri Glacier. It is analyzed that ELA and AAR of Chhota Shigri Glacier will change to + 107.7 m a.s.l. and - 15.03% respectively due to increase in temperature by + 1 °C. Further, ~ 38% more precipitation is required to compensate for the change in MB, ELA and AAR which will occur due to + 1 °C temperature rise. The findings of the present study will also support the estimation of future MB of Chhota Shigri Glacier using minimal simulated model parameters for distributed temperature-index model which have been found to produce good results using long term high resolution climate data.

Assessment of biomass burning smoke influence on environmental conditions for multi-year tornado outbreaks by combining aerosol-aware microphysics and fire emission constraints.

We use the WRF system to study the impacts of biomass burning smoke from Central America on several tornado outbreaks occurring in the US during spring. The model is configured with an aerosol-aware microphysics parameterization capable of resolving aerosol-cloud-radiation interactions in a cost-efficient way for numerical weather prediction (NWP) applications. Primary aerosol emissions are included and smoke emissions are constrained using an inverse modeling technique and satellite-based AOD observations. Simulations turning on and off fire emissions reveal smoke presence in all tornado outbreaks being studied and show an increase in aerosol number concentrations due to smoke. However, the likelihood of occurrence and intensification of tornadoes is higher due to smoke only in cases where cloud droplet number concentration in low level clouds increases considerably in a way that modifies the environmental conditions where the tornadoes are formed (shallower cloud bases and higher low-level wind shear). Smoke absorption and vertical extent also play a role, with smoke absorption at cloud-level tending to burn-off clouds and smoke absorption above clouds resulting in an increased capping inversion. Comparing these and WRF-Chem simulations configured with a more complex representation of aerosol size and composition and different optical properties, microphysics and activation schemes, we find similarities in terms of the simulated aerosol optical depths and aerosol impacts on near-storm environments. This provides reliability on the aerosol-aware microphysics scheme as a less computationally expensive alternative to WRF-Chem for its use in applications such as NWP and cloud-resolving simulations.