Role of Forest Management in Environmental Studies with Reference to the Maintaining of Conservation Values

A forest is a type of ecosystem in which there is high density of trees occupying a relatively large area of land. An ecosystem is an ecological unit consisting of a biotic community together with it’s a biotic environment. In the case of forest, tress dominant the biotic landscape, although there are also other plants and animals. There are many types of forest, such as tropical, evergreen, deciduous and dry forest based on the climatic condition and types of trees present. Forests provide innumerable values to people, provide aspects that address both physical needs as well as the internal nature of people. Forest help cleanse the air by intercepting airborne particles, reducing heat, and absorbing such pollutants as carbon monoxide, sulfur dioxide and nitrogen dioxide. Trees remove this air pollution by lowering air temperature, through respiration, and by retaining particulates. Erosion control has always started with tree and grass planting projects. Tree roots bind the soil and their leaves break the force of wind and rain on soil. Trees fight soil erosion, conserve rainwater and reduce water runoff and sediment deposit after storms. Herbs, shrubs and trees in the forests hold the topmost layer firmly by their roots. This does not allow natural forces like wind and water to carry away the topmost fertile layer of the soil easily. Hence, Forests prevent soil erosion. With forest conservation, animal species, insects and all the biodiversity of natural areas is protected. It is noteworthy that these beings and the local vegetation exert influence on conservation beyond deforestation and the regional climate, even interfering with the health of the local community. Keywords: Forest, Natural Resources, Implementation, Ecological Balance, Significance, Deforestation, Climatic Condition

Atmospheric rivers and associated precipitation patterns during the ACLOUD and PASCAL campaigns near Svalbard (May–June 2017): case studies using observations, reanalyses, and a regional climate model

Recently, a significant increase in the atmospheric moisture content has been documented over the Arctic, where both local contributions and poleward moisture transport from lower latitudes can play a role. This study focuses on the anomalous moisture transport events confined to long and narrow corridors, known as atmospheric rivers (ARs), which are expected to have a strong influence on Arctic moisture amounts, precipitation, and the energy budget. During two concerted intensive measurement campaigns – Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) and the Physical feedbacks of Arctic planetary boundary layer, Sea ice, Cloud and AerosoL (PASCAL) – that took place at and near Svalbard, three high-water-vapour-transport events were identified as ARs, based on two tracking algorithms: the 30 May event, the 6 June event, and the 9 June 2017 event. We explore the temporal and spatial evolution of the events identified as ARs and the associated precipitation patterns in detail using measurements from the French (Polar Institute Paul Emile Victor) and German (Alfred Wegener Institute for Polar and Marine Research) Arctic Research Base (AWIPEV) in Ny-Ålesund, satellite-borne measurements, several reanalysis products (the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA) Interim (ERA-Interim); the ERA5 reanalysis; the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2); the Climate Forecast System version 2 (CFSv2); and the Japanese 55-Year Reanalysis (JRA-55)), and the HIRHAM regional climate model version 5 (HIRHAM5). Results show that the tracking algorithms detected the events differently, which is partly due to differences in the spatial and temporal resolution as well as differences in the criteria used in the tracking algorithms. The first event extended from western Siberia to Svalbard, caused mixed-phase precipitation, and was associated with a retreat of the sea-ice edge. The second event, 1 week later, had a similar trajectory, and most precipitation occurred as rain, although mixed-phase precipitation or only snowfall occurred in some areas, mainly over the coast of north-eastern Greenland and the north-east of Iceland, and no differences were noted in the sea-ice edge. The third event showed a different pathway extending from the north-eastern Atlantic towards Greenland before turning south-eastward and reaching Svalbard. This last AR caused high precipitation amounts on the east coast of Greenland in the form of rain and snow and showed no precipitation in the Svalbard region. The vertical profiles of specific humidity show layers of enhanced moisture that were concurrent with dry layers during the first two events and that were not captured by all of the reanalysis datasets, whereas the HIRHAM5 model misrepresented humidity at all vertical levels. There was an increase in wind speed with height during the first and last events, whereas there were no major changes in the wind speed during the second event. The accuracy of the representation of wind speed by the reanalyses and the model depended on the event. The objective of this paper was to build knowledge from detailed AR case studies, with the purpose of performing long-term analysis. Thus, we adapted a regional AR detection algorithm to the Arctic and analysed how well it identified ARs, we used different datasets (observational, reanalyses, and model) and identified the most suitable dataset, and we analysed the evolution of the ARs and their impacts in terms of precipitation. This study shows the importance of the Atlantic and Siberian pathways of ARs during spring and beginning of summer in the Arctic; the significance of the AR-associated strong heat increase, moisture increase, and precipitation phase transition; and the requirement for high-spatio-temporal-resolution datasets when studying these intense short-duration events.

WRF Sensitivity for Seasonal Climate Simulations of Precipitation Fields on the CORDEX South America Domain

Dynamic numerical models of the atmosphere are the main tools used for weather and climate forecasting as well as climate projections. Thus, this work evaluated the systematic errors and areas with large uncertainties in precipitation over the South American continent (SAC) based on regional climate simulations with the weather research and forecasting (WRF) model. Ten simulations using different convective, radiation, and microphysical schemes, and an ensemble mean among them, were performed with a resolution of 50 km, covering the CORDEX-South America domain. First, the seasonal precipitation variability and its differences were discussed. Then, its annual cycle was investigated through nine sub-domains on the SAC (AMZN, AMZS, NEBN, NEBS, SE, SURU, CHAC, PEQU, and TOTL). The Taylor Diagrams were used to assess the sensitivity of the model to different parameterizations and its ability to reproduce the simulated precipitation patterns. The results showed that the WRF simulations were better than the ERA-interim (ERAI) reanalysis when compared to the TRMM, showing the added value of dynamic downscaling. For all sub-domains the best result was obtained with the ensemble compared to the satellite TRMM. The largest errors were observed in the SURU and CHAC regions, and with the greatest dispersion of members during the rainy season. On the other hand, the best results were found in the AMZS, NEBS, and TOTL regions.

Km-resolution climate models: prospects and challenges

<p>Currently major efforts are underway toward refining the horizontal grid spacing of climate models to about 1 km, using both global and regional climate models. There is the well-founded hope that this increase in resolution will improve climate models, as it enables replacing the parameterizations of moist convection and gravity-wave drag by explicit treatments. Results suggest that this approach has a high potential in improving the representation of the water cycle and extreme events, and in reducing uncertainties in climate change projections. The presentation will provide examples of these developments in the areas of heavy precipitation and severe weather events over Europe. In addition, it will be argued that km-resolution is a promising approach toward constraining uncertainties in global climate change projections, due to improvements in the representation of tropical and subtropical clouds. Work in the latter area has only recently started and results are highly encouraging.</p> <p>For a few years there have also been attempts to make km-resolution available in global climate models for decade-long simulations. Developing this approach requires a concerted effort. Key challenges include the exploitation of the next generation hardware architectures using accelerators (e.g. graphics processing units, GPUs), the development of suitable approaches to overcome the output avalanche, and the maintenance of the rapidly-developing model source codes on a number of different compute architectures. Despite these challenges, it will be argued that km-resolution GCMs with a capacity to run at 1 SYPD (simulated year per day), might be much closer than commonly believed.</p> <p>The presentation is largely based on a recent collaborative paper (Schär et al., 2020, BAMS, https://doi.org/10.1175/BAMS-D-18-0167.1) and ongoing studies. It will also present aspects of a recent Swiss project in this area (EXCLAIM, https://exclaim.ethz.ch/).</p>

Climate Projections over the Great Lakes Region: Using Two-way Coupling of a Regional Climate Model with a 3-D Lake Model

Warming trends of the Laurentian Great Lakes and surrounding areas have been observed in recent decades, and concerns continue to rise about the pace and pattern of future climate change over the world’s largest freshwater system. To date, many regional climate models used for the Great Lakes projection either neglected the lake-atmosphere interactions or only coupled with 1-D column lake models to represent the lake hydrodynamics. The study presents the Great Lakes climate change projection that has employed the two-way coupling of a regional climate model with a 3-D lake model (GLARM) to resolve 3-D hydrodynamics important for large lakes. Using the three carefully selected CMIP5 AOGCMS, we show that the GLARM ensemble average substantially reduces the surface air temperature and precipitation biases of the driving AOGCM ensemble average in present-day climate simulations. The improvements are not only displayed from the atmospheric perspective but also evidenced in accurate simulations of lake surface temperature, and ice coverage and duration. After that, we present the GLARM projected climate change for the mid-21st century (2030–2049) and the late century (2080–2099) for the RCP4.5 and RCP8.5. Under RCP 8.5, the Great Lakes basin is projected to warm by 1.3–2.2 °C by the mid-21st century and 4.0–4.9 °C by the end of the century relative to the early-century (2000–2019). Moderate mitigation (RCP 4.5) reduces the mid-century warming to 0.8–1.9 °C and late-century warming to 1.8–2.7 °C. Annual precipitation in GLARM is projected to increase for the entire basin, varying from −0.4 % to 10.5 % during the mid-century and 1.2 % to 28.5 % during the late-century under different scenarios and simulations. The most significant increases are projected in spring and early summer when current precipitation is highest and little increase in winter when it is lowest. Lake surface temperatures (LSTs) are also projected to increase across the five lakes in all of the simulations, but with strong seasonal and spatial heterogeneities. The most significant LST increase will occur in Lake Superior. The strongest warming was projected in spring, followed by strong summer warming, suggesting earlier and more intense stratification in the future. In contrast, a relatively smaller increase in LSTs during fall and winter are projected with heat transfer to the deepwater due to strong mixing and energy required for ice melting. Correspondingly, the highest monthly mean ice cover is projected to be 3–6 % and 8–20 % across the lakes by the end of the century in RCP 8.5 and RCP 4.5, respectively. In the coastal regions, ice duration will decrease by up to 30–50 days.

Responsibility of major emitters for country-level warming and extreme hot years

The contributions of single greenhouse gas emitters to country-level climate change are generally not disentangled, despite their relevance for climate policy and litigation. Here, we quantify the contributions of the five largest emitters (China, US, EU-27, India, and Russia) to projected 2030 country-level warming and extreme hot years with respect to pre-industrial climate using an innovative suite of Earth System Model emulators. We find that under current pledges, their cumulated 1991–2030 emissions are expected to result in extreme hot years every second year by 2030 in twice as many countries (92%) as without their influence (46%). If all world nations shared the same fossil CO2 per capita emissions as projected for the US from 2016–2030, global warming in 2030 would be 0.4 °C higher than under actual current pledges, and 75% of all countries would exceed 2 °C of regional warming instead of 11%. Our results highlight the responsibility of individual emitters in driving regional climate change and provide additional angles for the climate policy discourse.