scholarly journals Future climate projection for mainland Southeast Asia countries: Climate change scenario of 21st century

2011 ◽  
Vol 1 (1) ◽  
pp. 35-38
Author(s):  
Suppakorn Chinvanno ◽  
V. Luang-aram ◽  
C. Sangmanee ◽  
J. Thanakitmetavut
Keyword(s):  
Climate Change ◽  
Southeast Asia ◽  
21St Century ◽  
Climate Change Scenario ◽  
Future Climate ◽  
Climate Projection ◽  
Change Scenario ◽  
Future Climate Projection ◽  
Mainland Southeast Asia

scholarly journals Methods for the Design of Climate Change Scenario in Slovakia for the 21St Century

2009 ◽  
Vol 1 (1) ◽  
pp. 77-90 ◽  
Author(s):  
Marian Melo ◽  
Milan Lapin ◽  
Ingrid Damborska
Keyword(s):  
Climate Change ◽  
Time Series ◽  
21St Century ◽  
Climate Change Scenario ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Precipitation Time Series ◽  
Impact Studies ◽  
Temperature And Precipitation ◽  
Mean Temperature

Abstract In this paper methods of climate-change scenario projection in Slovakia for the 21st century are outlined. Temperature and precipitation time series of the Hurbanovo Observatory in 1871-2007 (Slovak Hydrometeorological Institute) and data from four global GCMs (GISS 1998, CGCM1, CGCM2, HadCM3) are utilized for the design of climate change scenarios. Selected results of different climate change scenarios (based on different methods) for the region of Slovakia (up to 2100) are presented. The increase in annual mean temperature is about 3°C, though the results are ambiguous in the case of precipitation. These scenarios are required by users in impact studies, mainly from the hydrology, agriculture and forestry sectors.

CHILDHOOD ASTHMA PROJECTIONS FOR ATLANTA UNDER A FUTURE CLIMATE CHANGE SCENARIO

Epidemiology ◽  
2004 ◽  
Vol 15 (4) ◽  
pp. S97
Author(s):  
Jonathan Patz ◽  
Howard Frumkin ◽  
Michell Klein ◽  
Michelle Bell ◽  
Hugh Ellis ◽  
...  
Keyword(s):  
Climate Change ◽  
Childhood Asthma ◽  
Climate Change Scenario ◽  
Future Climate ◽  
Future Climate Change ◽  
Change Scenario ◽  
Future Climate Change Scenario

SPAGETTA Weather Generator Linked with Climate Models May Produce Weather Series for Future Climate 

2021 ◽  
Author(s):  
Martin Dubrovsky ◽  
Ondrej Lhotka ◽  
Jiri Miksovsky ◽  
Petr Stepanek ◽  
Jan Meitner
Keyword(s):  
Climate Change ◽  
Climate Sensitivity ◽  
Climate Change Scenario ◽  
Emission Scenario ◽  
Future Climate ◽  
Change Scenario ◽  
Emission Scenarios ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Global Mean

<p>Stochastic weather generators (WGs) are tools for producing weather series, which are statistically similar to the real world weather series. The synthetic series may represent both present and changed (not only the future) climate. In the latter case, WG parameters derived from the observed weather series are modified with climate change scenario, which is typically based on RCM or GCM simulations. As the GCM/RCM simulations are very demanding on computer resources, the numbers of simulations made for individual possible emission scenarios are limited, especially for some (mostly the less probable ones) emission scenarios (e.g. RCP 2.6). Still, many climate change impact studies try to give projections of the CC impacts assuming uncertainties coming from all possible sources, including the modeling uncertainty and  uncertainties in emissions & climate sensitivity. To allow generation of weather series fitting the projection of any GCM forced by any emission scenario, we use a pattern scaling approach, in which the standardized climate change scenario (consisting of changes in climatic characteristic related to 1ºC change in global mean temperature) derived from a given GCM is multiplied by a change in global mean temperature (dTg) projected (for a selected emission scenario and climate sensitivity) by a simple climate model MAGICC.</p><p>In our contribution, we will demonstrate the use of the generator (using SPAGETTA WG, which is our multi-site multi-variate parametric daily WG) in probabilistic projection of future changes in selected climatic characteristics of temperature (T) and precipitation (P); we will focus on spatial hot/cold/dry/wet/hot-dry/hot-wet/cold-dry/cold-wet spells). Standardized climate change scenarios will be derived from multiple GCMs (taken from CMIP5 database) and scaled by dTg projected by MAGICC. Effects of the three above-named sources of uncertainty, as well as the effects of changes in individual statistical characteristics (the means & the site-specific variabilities & the characteristics of the temporal and spatial variability of both T and P) will be assessed.</p><p>Acknowledgements: Projects GRIMASA (Czech Science Foundation, project no. 18-15958S) and SustES (European Structural and Investment Funds, project no. CZ.02.1.01/0.0/0.0/16_019/0000797).</p>

scholarly journals Climate change scenario analysis for Baro-Akobo river basin, Southwestern Ethiopia

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Teressa Negassa Muleta
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Water Resources Management ◽  
Climate Change Scenario ◽  
Emission Scenario ◽  
Future Climate ◽  
Future Climate Change ◽  
Change Scenario ◽  
Resources Management ◽  
Future Climate Change Scenario

Abstract Background Several water resources projects are under planning and implementation in the Baro-Akobo basin. Currently, the planning and management of these projects is relied on historical data. So far, hardly any study has addressed water resources management and adaptation measures in the face of changing water balances due to climate change in the basin. The main bottleneck to this has been lack of future climate change scenario base data over the basin. The current study is aimed at developing future climate change scenario for the basin. To this end, Regional Climate Model (RCM) downscaled data for A1B emission scenario was employed and bias corrected at basin level using observed data. Future climate change scenario was developed using the bias corrected RCM output data with the basic objective of producing baseline data for sustainable water resources development and management in the basin. Result The projected future climate shows an increasing trend for both maximum and minimum temperatures; however, for the case of precipitation it does not manifest a systematic increasing or decreasing trend in the next century. The projected mean annual temperature increases from the baseline period by an amount of 1 °C and 3.5 °C respectively, in 2040s and 2090s. Similarly, evapotranspiration has been found to increase to an extent of 25% over the basin. The precipitation is predicted to experience a mean annual decrease of 1.8% in 2040s and an increase of 1.8% in 2090s over the basin for the A1B emission scenario. Conclusion The study resulted in a considerable future change in climatic variables (temperature, precipitation, and evapotranspiration) on the monthly and seasonal basis. These have an implication on hydrologic extremes-drought and flooding, and demands dynamic water resources management. Hence the study gives a valuable base information for water resources planning and managers, particularly for modeling reservoir inflow-climate change relations, to adapt reservoir operation rules to the real-time changing climate.

scholarly journals Evaluation of long term forest fires in India with respect to state administrative boundary, forest category of LULC and future climate change scenario: A Geospatial Perspective

2018 ◽  
Vol 79 (4) ◽  
pp. 335-343
Author(s):  
Firoz Ahmad ◽  
Md Meraj Uddin ◽  
Laxmi Goparaju
Keyword(s):  
Climate Change ◽  
Forest Fire ◽  
Forest Fires ◽  
Climate Change Scenario ◽  
Fire Frequency ◽  
Future Climate ◽  
Future Climate Change ◽  
Change Scenario ◽  
Future Climate Change Scenario

Abstract Analysing the forest fires events in climate change scenario is essential for protecting the forest from further degradation. Geospatial technology is one of the advanced tools that has enormous capacity to evaluate the number of data sets simultaneously and to analyse the hidden relationships and trends. This study has evaluated the long term forest fire events with respect to India’s state boundary, its seasonal monthly trend, all forest categories of LULC and future climate anomalies datasets over the Indian region. Furthermore, the spatial analysis revealed the trend and their relationship. The state wise evaluation of forest fire events reflects that the state of Mizoram has the highest forest fire frequency percentage (11.33%) followed by Chhattisgarh (9.39%), Orissa (9.18%), Madhya Pradesh (8.56%), Assam (8.45%), Maharashtra (7.35%), Manipur (6.94%), Andhra Pradesh (5.49%), Meghalaya (4.86%) and Telangana (4.23%) when compared to the total country’s forest fire counts. The various LULC categories which represent the forest show some notable forest fire trends. The category ‘Deciduous Broadleaf Forest’ retain the highest fire frequency equivalent to 38.1% followed by ‘Mixed Forest’ (25.6%), ‘Evergreen Broadleaf Forest’ (16.5%), ‘Deciduous Needle leaf Forest’ (11.5%), ‘Shrub land’ (5.5%), ‘Evergreen Needle leaf Forest’ (1.5%) and ‘Plantations’ (1.2%). Monthly seasonal variation of forest fire events reveal the highest forest fire frequency percentage in the month of ‘March’ (55.4%) followed by ‘April’ (28.2%), ‘February’ (8.1%), ‘May’ (6.7%), ‘June’ (0.9%) and ‘January’ (0.7%). The evaluation of future climate data for the year 2030 shows significant increase in forest fire seasonal temperature and abrupt annual rainfall pattern; therefore, future forest fires will be more intensified in large parts of India, whereas it will be more crucial for some of the states such as Orissa, Chhattisgarh, Mizoram, Assam and in the lower Sivalik range of Himalaya. The deciduous forests will further degrade in future. The highlight/results of this study have very high importance because such spatial relationship among the various datasets is analysed at the country level in view of the future climate scenario. Such analysis gives insight to the policymakers to make sustainable future plans for prioritization of the various state forests suffering from forest fire keeping in mind the future climate change scenario.

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