Effects of climate change on daily minimum and maximum temperatures and cloudiness in the Shikoku region: a statistical downscaling model approach

2014 ◽  
Vol 120 (1-2) ◽  
pp. 87-98 ◽  
Author(s):  
Kenichi Tatsumi ◽  
Tsutao Oizumi ◽  
Yosuke Yamashiki
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Statistical Downscaling Model ◽  
Daily Minimum ◽  
Maximum Temperatures ◽  
Model Approach

Prediction of climate change in Brunei Darussalam using statistical downscaling model

2017 ◽  
Vol 133 (1-2) ◽  
pp. 343-360 ◽  
Author(s):  
Dk. Siti Nurul Ain binti Pg. Ali Hasan ◽  
Uditha Ratnayake ◽  
Shahriar Shams ◽  
Zuliana Binti Hj Nayan ◽  
Ena Kartina Abdul Rahman
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Statistical Downscaling Model ◽  
Brunei Darussalam

scholarly journals Aircraft Takeoff Performance in a Changing Climate for Canadian Airports

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 418
Author(s):  
Yijie Zhao ◽  
Laxmi Sushama
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Regional Climate ◽  
Daily Maximum ◽  
Daily Minimum ◽  
Weight Restriction ◽  
South Central ◽  
Aircraft Performance ◽  
Maximum Temperatures ◽  
Projected Changes

Temperature and wind are major meteorological factors that affect the takeoff and landing performance of aircraft. Warmer temperatures and the associated decrease in air density in future climate, and changes to crosswind and tailwind, can potentially impact aircraft performance. This study evaluates projected changes to aircraft takeoff performance, in terms of weight restriction days and strong tailwind and crosswind occurrences, for 13 major airports across Canada, for three categories of aircraft used for long-, medium- and short-haul flights. To this end, two five-member ensembles of transient climate change simulations performed with a regional climate model, for Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios, respectively, are analyzed. Results suggest that the projected increases in weight restriction days associated with the increases in daily maximum temperatures vary with aircraft category and airfield location, with larger increases noted for airfields in the south central regions of Canada. Although avoiding takeoff during the warmest period of the day could be a potential solution, analysis focused on the warmest and coolest periods of the day suggests more weight restriction hours even during the coolest period of the day, for these airfields. Though RCP8.5 in general suggests larger changes to weight restriction hours compared to RCP4.5, the differences between the two scenarios are more prominent for the coolest part of the day, as projected changes to daily minimum temperatures occur at a much faster rate for RCP8.5 compared to RCP4.5, and also due to the higher increases in daily minimum temperatures compared to maximum temperatures. Both increases and decreases to crosswind and tailwind are projected, which suggest the need for detailed case studies, especially for those airfields that suggest increases. This study provides useful preliminary insights related to aircraft performance in a warmer climate, which will be beneficial to the aviation sector in developing additional analysis and to support climate change adaptation-related decision-making.

scholarly journals Prediction of Future Climate Change for Rainfall in the Upper Kurau River Basin, Perak Using Statistical Downscaling Model (SDSM)

2019 ◽  
Vol 7 (6A) ◽  
pp. 33-42
Author(s):  
Nuramidah Hamidon ◽  
Sobri Harun ◽  
Norshuhaila Mohamed Sunar ◽  
Nor Hazren A.Hamid ◽  
Mimi Suliza Muhamad ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Statistical Downscaling ◽  
Future Climate ◽  
Future Climate Change ◽  
Statistical Downscaling Model

scholarly journals Hydrological Implications of Climate Change on River Basin Water Cycle: Case Studies of the Yangtze River and Yellow River Basins, China

2016 ◽  
Author(s):  
Liu Liu ◽  
Zongxue Xu ◽  
Rong Li ◽  
Youzhi Wang
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Statistical Downscaling ◽  
Yangtze River ◽  
Water Cycle ◽  
Yellow River ◽  
River Basins ◽  
The Yangtze River ◽  
Statistical Downscaling Model ◽  
Basin Water

Climate change is a global issue that draws widespread attention from the international society. As an important component of the climate system, the water cycle is directly affected by climate change. Thus, it is very important to study the influences of climate change on the basin water cycle with respect to maintenance of healthy rivers, sustainable use of water resources, and sustainable socioeconomic development in the basin. In this study, by assessing the suitability of multiple General Circulation Models (GCMs) recommended by the Intergovernmental Panel on Climate Change, Statistical Downscaling Model (SDSM) and Automated Statistical Downscaling model (ASD) were used to generate future climate change scenarios. These were then used to drive distributed hydrologic models (Variable Infiltration Capacity, Soil and Water Assessment Tool) for hydrological simulation of the Yangtze River and Yellow River basins, thereby quantifying the effects of climate change on the basin water cycle. The results showed that suitability assessment adopted in this study could effectively reduce the uncertainty of GCMs, and that statistical downscaling was able to greatly improve precipitation and temperature outputs in global climate mode. Compared to a baseline period (1961–1990), projected future periods (2046–2065 and 2081–2100) had a slightly decreasing tendency of runoff in the lower reaches of the Yangtze River basin. In particular, a significant increase in runoff was observed during flood seasons in the southeast part. However, runoff of the upper Yellow River basin decreased continuously. The results provide a reference for studying climate change in major river basins of China.

scholarly journals Stepping inside the niche: microclimate data are critical for accurate assessment of species' vulnerability to climate change

2014 ◽  
Vol 10 (9) ◽  
pp. 20140576 ◽  
Author(s):  
Collin Storlie ◽  
Andres Merino-Viteri ◽  
Ben Phillips ◽  
Jeremy VanDerWal ◽  
Justin Welbergen ◽  
...  
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Environmental Data ◽  
Weather Data ◽  
Daily Maximum ◽  
Biologically Relevant ◽  
Species Vulnerability ◽  
Vulnerability To Climate Change ◽  
Thermal Regimes ◽  
Maximum Temperatures

To assess a species' vulnerability to climate change, we commonly use mapped environmental data that are coarsely resolved in time and space. Coarsely resolved temperature data are typically inaccurate at predicting temperatures in microhabitats used by an organism and may also exhibit spatial bias in topographically complex areas. One consequence of these inaccuracies is that coarsely resolved layers may predict thermal regimes at a site that exceed species' known thermal limits. In this study, we use statistical downscaling to account for environmental factors and develop high-resolution estimates of daily maximum temperatures for a 36 000 km 2 study area over a 38-year period. We then demonstrate that this statistical downscaling provides temperature estimates that consistently place focal species within their fundamental thermal niche, whereas coarsely resolved layers do not. Our results highlight the need for incorporation of fine-scale weather data into species' vulnerability analyses and demonstrate that a statistical downscaling approach can yield biologically relevant estimates of thermal regimes.

scholarly journals Incorporation of Long Term Climate Changes in Hydrological Modelling

2019 ◽  
Vol 8 (10) ◽  
pp. 4521-4526
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
General Circulation ◽  
Global Climate ◽  
Circulation Model ◽  
Cmip5 Models ◽  
Future Climate Change ◽  
Statistical Downscaling Model ◽  
Intergovernmental Panel ◽  
Statistical Relationships

One of climate change's most important concerns at the moment is its impact on hydrology as it has direct links with agriculture, vegetation, and livelihood. This study tries to analyze potential future climate change in the Kumaradhara river basin. This study involved three steps: (1) acquiring and using general circulation model (GCM) to project future global climate scenarios; (2) establishing statistical relationships between GCM data and observed data using Statistical Downscaling Model (SDSM); (3) downscaling the second generation Canadian Earth system Model (CanESM2)GCM output based on the established statistical relationship. The statistical downscaling is carried out for three scenarios used in the fifth evaluation report of the recent Intergovernmental Panel on Climate Change (IPCC) viz., Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5. The statistical downscaling Model (SDSM) results showed that the mean annual daily precipitation is altered in the basin under all the scenarios but it will be different in different time periods depending on scenarios and the basin will experience the reduced precipitation levels in summer. Also the precipitation will marginally rise in all the time slices with reference to baseline data. We can conclude from the results that this region's climate will affect future farming as the availability of water is bound to change. This study should, however, be followed up by a larger study incorporating multiple CMIP5 models such that changes in hydrological-regimes can be examined appropriately.

scholarly journals Projections of Future Climate Change in the Vu Gia Thu Bon River Basin, Vietnam by Using Statistical DownScaling Model (SDSM)

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 755
Author(s):  
Dang Nguyen Dong Phuong ◽  
Trung Q. Duong ◽  
Nguyen Duy Liem ◽  
Vo Ngoc Quynh Tram ◽  
Dang Kien Cuong ◽  
...  
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
River Basin ◽  
Statistical Downscaling ◽  
Minimum Temperature ◽  
Model Performance ◽  
Future Climate Change ◽  
Extreme Temperatures ◽  
Emission Scenarios ◽  
Statistical Downscaling Model

Future projections of anthropogenic climate change play a pivotal role in devising viable countermeasures to address climate-related risks. This study strove to construct future daily rainfall and maximum and minimum temperature scenarios in Vu Gia Thu Bon river basin by employing the Statistical DownScaling Model (SDSM). The model performance was evaluated by utilizing a Taylor diagram with dimensioned and dimensionless statistics. During validation, all model-performance measures show good ability in simulating extreme temperatures and reasonable ability for rainfall. Subsequently, a set of predictors derived from HadCM3 and CanESM2 was selected to generate ensembles of each climatic variables up to the end of 21st century. The generated outcomes exhibit a consistent increase in both extreme temperatures under all emission scenarios. The greatest changes in maximum and minimum temperature were predicted to increase by 2.67–3.9 °C and 1.24–1.96 °C between the 2080s and reference period for the worst-case scenarios. Conversely, there are several discrepancies in the projections of rainfall under different emission scenarios as well as among considered stations. The predicted outcomes indicate a significant decrease in rainfall by approximately 11.57%–17.68% at most stations by 2099. Moreover, all ensemble means were subjected to the overall and partial trend analysis by applying the Innovative-Şen trend analysis method. The results exhibit similar trend patterns, thereby indicating high stability and applicability of the SDSM. Generally, it is expected that these findings will contribute numerous valuable foundations to establish a framework for the assessment of climate change impacts at the river basin scale.

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