Sergey Venevsky

Vulnerability of permafrost carbon to climate change: Implications for the global carbon cycle

ABSTRACT Thawing permafrost and the resulting microbial decomposition of previously frozen organic carbon (C) is one of the most significant potential feedbacks from terrestrial ecosystems to the atmosphere in a changing climate. In this article we present an overview of the global permafrost C pool and of the processes that might transfer this C into the atmosphere, as well as the associated ecosystem changes that occur with thawing. We show that accounting for C stored deep in the permafrost more than doubles previous high-latitude inventory estimates, with this new estimate equivalent to twice the atmospheric C pool. The thawing of permafrost with warming occurs both gradually and catastrophically, exposing organic C to microbial decomposition. Other aspects of ecosystem dynamics can be altered by climate change along with thawing permafrost, such as growing season length, plant growth rates and species composition, and ecosystem energy exchange. However, these processes do not appear to be able to compensate for C release from thawing permafrost, making it likely that the net effect of widespread permafrost thawing will be a positive feedback to a warming climate.

Health and climate change: policy responses to protect public health

The 2015 Lancet Commission on Health and Climate Change has been formed to map out the impacts of climate change, and the necessary policy responses, in order to ensure the highest attainable stand ...

Modelling of time-dependent biome shifts under global climate changes

Abstract The study of biosphere response to climate changes due to anthropogenic emission of greenhouse gases is a very important scientific and practical problem. The core of its solution -modelling and long-term forecast of global terrestrial vegetation changes -is considered in this paper. There are a few approaches to modelling long-term vegetation dynamics; their principles are discussed in brief. A model approach based on bioclimatic schemes has been chosen for further considerations. A model based on Holdridges bioclimatic scheme was designed for simulation of time-dependent biome shifts. A procedure of time delays was designed to take into account inertia of vegetation response to climate change. It was based on expert estimates of biome replacements. Computer experiments have been designed to investigate the consequences of time-dependent climate changes for the vegetation of Russia. The results showed the importance of the temporal factor for long-term forecasts. The obtained assessments demonstrated complex non-linear dynamics of vegetation even under the simplest climatic scenario. Further prospects of suggested model were discussed.

A System for Evaluation of Growth and Mortality in Russian Forests

A modelling system (MS) for evaluation of current increment (net and gross growth) and mortality in Russian forests is presented. The informational basis of the MS are the data of the Russian forest state account (growing stock by dominant species, age, site indices, stocking) and some additional indicators (types of age structure, average species composition, type and intensity of disturbance regimes by ecoregions and/or landscapes. The MS includes model blocks generated with available information including yield tables (general, regional, full-stocked, with different stocking and types of age structure), growth tables, models, and experimental data for managed and unmanaged, disturbed and undisturbed forests. Two-dimensional (by age and site index) and three-dimensional (age, site index and stocking) models by species and ecoregions, based on regularities of coefficients of Mitscherlich growing functions, were used as the nuclei of the MS. The preliminary results, received for several Russian forest forming species gave a good fit to experimental data.

SEVER: A modification of the LPJ global dynamic vegetation model for daily time step and parallel computation

The atmospheric chemistry and climate study research needs simulation tools, which allow description of dynamics of land biogeochemical processes, regulating carbon and water fluxes and pools, as well as changes of optical and mechanical properties of surface with fine spatial and temporal resolution. Despite of considerable amount of scientifically sounded large-scale biospheric models that were designed recently (as a rule they named Dynamic Global Vegetation Models e DGVMs) simulation of land biosphere with daily time step and with ecologically feasible spatial resolution (50e1 km) is still problematic. Particularly for this reason, comparison of the six state-of-the-art DGVMs (Cramer et al., 2001) was made for monthly climate data. The major technical difficulty is the requirement to combine sounded process-oriented description of the biosphere at a daily step with a high calculation efficiency of a model. High calculation efficiency can be achieved using parallel computation combined with fast reading of large input climate data files. Process-oriented description of the biosphere at daily time step and medium scale spatial resolution (50e10 km) can be

Regional adaptation of a dynamic global vegetation model using a remote sensing data derived land cover map of Russia

The dynamic global vegetation model (DGVM) SEVER has been regionally adapted using a remote sensing data-derived land cover map in order to improve the reconstruction conformity of the distribution of vegetation functional types over Russia. The SEVER model was modified to address noticeable divergences between modelling results and the land cover map. The model modification included a light competition method elaboration and the introduction of a tundra class into the model. The rigorous optimisation of key model parameters was performed using a two-step procedure. First, an approximate global optimum was found using the efficient global optimisation (EGO) algorithm, and afterwards a local search in the vicinity of the approximate optimum was performed using the quasi-Newton algorithm BFGS. The regionally adapted model shows a significant improvement of the vegetation distribution reconstruction over Russia with better matching with the satellite-derived land cover map, which was confirmed by both a visual comparison and a formal conformity criterion.

The effects of the China–Russia gas deal on energy consumption, carbon emission, and particulate matter pollution in China

After more than two decades of negotiation, the China–Russia gas deal represents a new era of energy cooperation between China and Russia. In total, this is a win–win deal for both sides. For China, the deal will decrease energy consumption and carbon emission but will not significantly influence air quality; for Russia, it will provide a new market for its gas resources. In this study, we calculated the energy consumption, carbon emission, and particulate matter pollution (PM2.5 and PM10) in China in 2020, 2030, 2040, and 2050 under four IPCC representative concentration pathways (RCPs 8.5, 6.0, 4.5, and 2.6). We found that energy consumption and carbon emission decreased under the gas deal in RCPs 8.5, 6.0, and 4.5, although the rate of decrease slowed over time; however, in RCP 2.6, the rate of decrease of energy consumption and emission increased over time. PM2.5 and PM10 emission showed similar trends but with increasing rate, although the gas deal would mitigate air pollution in the short term. Although China’s government hopes to reduce carbon and pollutant emission under the deal, our results suggest that additional mitigation measures will be necessary to achieve this goal. Nonetheless, the reduction in carbon emission suggests that the China–Russia gas deal provides a model that other countries can follow to slow climate change.Sustainability: Gas deal with Russia helps to protect China’s air, but in short termThe China–Russia gas deal is supposed to decrease carbon emissions and improve air quality and it does, but with time these effects slow down. A team led by Sergey Venevsky from Tsinghua and Minzu Universities in Beijing calculated the energy consumption, carbon emission, and particulate matter pollution in China in 2020, 2030, 2040, and 2050 under four development scenarios of the world’s development, suggested by international experts. Energy consumption and carbon emissions are decreased under the gas deal for all but one (the worst) world development scenario, but the rate of decrease slowed over time. Air particulate pollution emissions showed similar trends. China’s government hopes to reduce carbon and pollution emissions under the deal, however, our results show that additional mitigation measures are necessary for achieve this goal.

NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang River Basin from 1982 to 2015

Vegetation in Northern Hemisphere, being sensitive to climate change, plays an important role in the carbon cycles between land and the atmosphere. The response of vegetation to climate change was analyzed at pixel, biome and regional scale in Amur-Heilongjiang River Basin (AHRB) for growing season, spring, summer and autumn using Normalized Difference Vegetation Index and gridded climate data for the period 1982-2015. NDVI and climate variables trend detection methods and correlation analysis were applied. The potential impacts of human activities on growing season NDVI dynamics were investigated further using residual trend analysis. Results showed that at river basin scale, growing season vegetation experienced a discontinuous greening trend with two reversals, demonstrating that NDVI initially increased to mid-1990s, then declined to mid-2000s, and finally rebounded to 2015. This may be attributed to the shifting between drought and wet trends, indicating growing season NDVI was mainly regulated by precipitation. Temperature was the dominant factor on affecting spring vegetation growth while autumn NDVI showed negative correlation with precipitation due to the relation of precipitation with sunshine hours available for photosynthesis. The response of vegetation growth to climatic variations varied among vegetation types. Grassland NDVI exhibited positive correlation with precipitation in all time ranges. NDVI of needleleaved forest, broadleaved forest, mixed forest and woodland were positively correlated with temperature in all seasons, while showing significant negative correlation with autumn precipitation. Residual trend analysis revealed that human activities might lead to the vegetation degradation in China farming zone of AHRB. Fires also play an important role in regulating vegetation dynamics in the region. Results of our analysis can be used by national governments from three countries of AHRB in managing and negotiating vegetation resources of the region.

Analysis fire patterns and drivers with a global SEVER-FIRE modelincorporated into Dynamic Global Vegetation Model and satelliteand on-ground observations

Biomass burning is an important environmental process with a strong influence on vegetation and on the atmospheric composition. It competes with microbes and herbivores to convert biomass to CO2 and it is a major contributor of gases and aerosols to the atmosphere. To better understand and predict global fire occurrence, fire models have been developed and coupled to Dynamic Global Vegetation Models (DGVMs) and Earth System Models (ESMs). 15 We present SEVER-FIRE v1.0 (Socio-Economic and natural Vegetation ExpeRimental global fire model version 1.0) which is incorporated into the SEVER-DGVM. One of the major focuses of SEVER-FIRE model is an implementation of pyrogenic behaviour of humans (timing of their activities and their willingness/necessity to ignite or suppress fire), related to socio-economic and demographic conditions in a geographical domain of the model application. Burned areas and emissions from the SEVER model 20 are compared to the Global Fire Emission Database version 2 (GFED), derived from satellite observations, while number of fires are compared with regional historical fire statistics. We focus both on the model output accuracy and on its assumptions regarding fire drivers, and perform: 1An evaluation of the predicted spatial and temporal patterns, focusing on fire incidence, seasonality and inter-annual variability. 25 2Analysis to evaluate the assumptions concerning the etiology, or causation, of fire, including climatic and anthropogenic drivers, as well as the type and amount of vegetation.

Constraints on information measures of embodied solar energy fluxes in the biosphere

The structure of the input-output (I-O) model of the Biosphere is investigated from the viewpoint of the information theory. The model was used to estimate the direct and indirect solar energy cost of different natural and artificial commodities. We showed that the distribution of aggregated fluxes, different types among compartments, can reflect the physical and functional ordering and natural hierarchy in the Biosphere to be found in the values of embodied energy. So, if we divide all the sets of commodities into three groups according to their order of magnitude of energy intensity, and then calculate the information measures of fluxes due to proposed subdivisions, we get the following: (1) that each distribution of aggregated fluxes among the compartments in chosen groups is increasingly far from equi-distribution; and (2) information of one distribution in respect to another is increasingly consequently.