Effects of experimental protocol on global vegetation model accuracy: A comparison of simulated and observed vegetation patterns for Asia

Prognostic vegetation models have been widely used to study the interactions between environmental change and biological systems. This study examines the sensitivity of vegetation model simulations to: (i) the selection of input climatologies representing different time periods and their associated atmospheric CO₂ concentrations, (ii) the choice of observed vegetation data for evaluating the model results, and (iii) the methods used to compare simulated and observed vegetation. We use vegetation simulated for Asia by the equilibrium vegetation model BIOME4 as a typical example of vegetation model output. BIOME4 was run using 19 different climatologies and their associated atmospheric CO₂ concentrations. The Kappa statistic, Fuzzy Kappa statistic and a newly developed map-comparison method, the Nomad index, were used to quantify the agreement between the biomes simulated under each scenario and the observed vegetation from three different global land- and tree-cover data sets: the global Potential Natural Vegetation data set (PNV), the Global Land Cover Characteristics data set (GLCC), and the Global Land Cover Facility data set (GLCF). The results indicate that the 30-year mean climatology (and its associated atmospheric CO₂ concentration) for the time period immediately preceding the collection date of the observed vegetation data produce the most accurate vegetation simulations when compared with all three observed vegetation data sets. The study also indicates that the BIOME4-simulated vegetation for Asia more closely matches the PNV data than the other two observed vegetation data sets. Given the same observed data, the accuracy assessments of the BIOME4 simulations made using the Kappa, Fuzzy Kappa and Nomad index map-comparison methods agree well when the compared vegetation types consist of a large number of spatially continuous grid cells. The results of this analysis can assist model users in designing experimental protocols for simulating vegetation.     

A climatology of Be at four high-altitude stations at the Alps and the Northern Apennines

The ⁷Be activity concentrations measured from 1996 to 1998 at four high-altitude stations, Jungfraujoch—Switzerland, Zugspitze—Germany, Sonnblick—Austria and Mt. Cimone—Italy, were analyzed in combination with a set of, meteorological and atmospheric parameters such as the tropopause height, relative and specific humidity and also in conjunction with 3D back-trajectories in order to investigate the climatological features of ⁷Be. A frequency distribution analysis on ⁷Be activity concentrations revealed the existence of two concentration classes around 1.5 and 6 mBq m⁻³ and a transition class between the two modes of the distribution at 3–4 mBq m⁻³. Cross-correlation analysis performed between ⁷Be and a number of meteorological and atmospheric parameters at the first three stations showed a strong negative correlation with relative humidity (−0.56, −0.51, −0.41) indicating the importance of wet scavenging as a controlling mechanism. Also, the positive correlation with the height of 3-days back-trajectories and tropopause height (+0.49/+0.43, +0.59/+0.36, +0.44/+0.38) shows that downward transport from the upper or middle to lower troposphere within anticyclonic conditions plays also an important role. Trajectory statistics showed that low ⁷Be concentrations typically originate from lower-altitude subtropical ocean areas, while high concentrations arrive from the north and high altitudes, as is characteristic for stratospheric intrusions. Although the ⁷Be activity concentrations are highly episodic, the monthly means indicate an annual cycle with a late-summer maximum at all stations. The correlation coefficients calculated for monthly means of the ⁷Be and atmospheric data suggest that the main predictor controlling the seasonality of the ⁷Be concentrations is tropopause height (+0.76, +0.56, +0.60), reflecting more vertical transport from upper tropospheric levels into the lower troposphere during the warm season than during the cold season.     

Association of retreating monsoon and extreme air pollution in a megacity

The world's top ranked mega city Delhi is known for deteriorated air quality. However, the analysis of air pollution data of 5 years (2014–2018) reveals that years 2016 and 2017, which were marked by an unusual delayed withdrawal of monsoon, witnessed an unprecedented extreme levels of toxic PM_2.5 particles (≤2.5 µm in diameter) touching a peak level of $～$760 µg/m³ (24 hr average), immediately after the monsoon retreat, surpassing WHO standards by $～$30 time and Indian national standards by $～$12 times, jeopardising lives of its citizens. However, the normal monsoon withdrawal years do not show such extreme levels of pollution. The high resolution WRF-Chem model along with meteorological data are used in this work to understand that how the delayed monsoon withdrawal and associated vagarious anti-cyclonic circulation resulted in trapping externally generated pollutants ceaselessly under colder conditions, leading to historic air quality crisis in landlocked mega city in these selected years. The sensitivity analysis confirmed that when WRF-chem model forced the climatology of normal monsoon year (2015) to simulate the pollution scenario of 2016 and 2017 for the above time period, the crisis subsided. Present findings suggest that such unusual monsoon patterns are on the hook to spur extreme pollution events in recent time.