Defining Climate Zone of Borneo based on Cluster Analysis

2021 ◽  
Author(s):  
Zulfaqar Sa'adi ◽  
Shamsuddin Shahid ◽  
Mohammed Sanusi Shiru
Keyword(s):  
Regional Climate ◽  
Development Planning ◽  
Climate Classification ◽  
Climate Zones ◽  
Local Climate ◽  
Climate Conditions ◽  
Tropical Regions ◽  
Related Development ◽  
Climatic Characteristic

Abstract Although Borneo Island is one of the most vulnerable tropical regions to climate change, maps depicting the local climate conditions employing climate classification are still not well defined. The present study attempted regional climate classification to divide the Borneo region into several homogenous groups based on long-term average climate behavior. Daily gridded rainfall and temperature (Tavg, Tmax, and Tmin) data at 0.25o resolution spanning 56-years (1960−2016) was used. The classification was done using non-hierarchical k-mean and several hierarchical methods, namely, Single, Complete, McQuitty, Average, Centroid, Median, and two algorithms of Ward's method, wardD, and wardD2. The results showed that k-mean, wardD, and wardD2 were able to classify the climate of Borneo into four zones, namely 'Dry and hot' (DH), 'Wet and hot' (WH), 'Wet' (W), and 'Wet and cold' (WC) with a considerable difference at the boundaries. Spatial relevancy, stability, and variability of the clusters based on correlation and compromise programming showed that the wardD method was the most likely to yield acceptable results with optimum 4-cluster to partition the area into four principal climate zones. The constructed cluster plot, centroid plot, and probability distribution function (PDF) showed a distinct climatic characteristic between the climate zones in terms of rainfall, temperature, and seasonality. The proposed climate zonation for Borneo can help in better understanding climate regionality and climate-related development planning.

scholarly journals Using a Statistical Preanalysis Approach as an Ensemble Technique for the Unbiased Mapping of GCM Changes to Local Stations

2018 ◽  
Vol 19 (9) ◽  
pp. 1447-1465 ◽  
Author(s):  
Cristián Chadwick ◽  
Jorge Gironás ◽  
Sebastián Vicuña ◽  
Francisco Meza ◽  
James McPhee
Keyword(s):  
Climatic Variability ◽  
Alternative Methods ◽  
Absolute Difference ◽  
Climate Projections ◽  
Mean Absolute Difference ◽  
Local Climate ◽  
Climate Conditions ◽  
Ensemble Technique ◽  
Climatic Scenarios

Abstract Accounting for climate change, GCM-based projections and their uncertainty are relevant to study potential impacts on hydrological regimes as well as to analyze, operate, and design water infrastructure. Traditionally, several downscaled and/or bias-corrected GCM projections are individually or jointly used to map the raw GCMs’ changes to local stations and evaluate uncertainty. However, the preservation of GCMs’ statistical attributes is by no means guaranteed, and thus alternative methods to cope with this issue are needed. This work develops an ensemble technique for the unbiased mapping of GCM changes to local stations, which preserves local climate variability and the GCMs’ statistics. In the approach, trend percentiles are extracted from the GCMs to represent the range of future long-term climate conditions to which local climatic variability is added. The approach is compared against a method in which each GCM is individually used to build future climatic scenarios from which percentiles are computed. Both approaches were compared to study future precipitation conditions in three Chilean basins under future climate projections based on 45 GCM runs under the RCP8.5 scenario. Overall, the approaches produce very similar results, even if a few trend percentiles are adopted in the GCM preanalysis. In fact, using 5–10 percentiles produces a mean absolute difference of 0.4% in the estimation of the probabilities of consecutive years under different precipitation thresholds, which is ~60% less than the error obtained using the median trend. Thus, the approach successfully preserves the GCM’s statistical attributes while incorporating the range of projected climates.

Analysis of Mean Climate Conditions in Senegal (1971–98)

2006 ◽  
Vol 10 (5) ◽  
pp. 1-40 ◽  
Author(s):  
Souleymane Fall ◽  
Dev Niyogi ◽  
Fredrick H. M. Semazzi
Keyword(s):  
West Africa ◽  
Large Scale ◽  
Regional Climate ◽  
Linear Regression Analysis ◽  
Regional Scale ◽  
Regional Climate Change ◽  
Warming Trend ◽  
Positive Trend ◽  
Local Climate ◽  
Climate Conditions

Abstract This paper presents a GIS-based analysis of climate variability over Senegal, West Africa. It responds to the need for developing a climate atlas that uses local observations instead of gridded global analyses. Monthly readings of observed rainfall (20 stations) and mean temperature (12 stations) were compiled, digitized, and quality assured for a period from 1971 to 1998. The monthly, seasonal, and annual temperature and precipitation distributions were mapped and analyzed using ArcGIS Spatial Analyst. A north–south gradient in rainfall and an east–west gradient in temperature variations were observed. June exhibits the greatest variability for both quantity of rainfall and number of rainy days, especially in the western and northern parts of the country. Trends in precipitation and temperature were studied using a linear regression analysis and interpolation maps. Air temperature showed a positive and significant warming trend throughout the country, except in the southeast. A significant correlation is found between the temperature index for Senegal and the Pacific sea surface temperatures during the January–April period, especially in the El Niño zone. In contrast to earlier regional-scale studies, precipitation does not show a negative trend and has remained largely unchanged, with a few locations showing a positive trend, particularly in the northeastern and southwestern regions. This study reveals a need for more localized climate analyses of the West Africa region because local climate variations are not always captured by large-scale analysis, and such variations can alter conclusions related to regional climate change.

scholarly journals SSD Analyze for the Assessment of Long Term Thermal Effect of Urbanization on the Izmir City’s Local Climate Change

2021 ◽  
Author(s):  
OZSEN CORUMLUOGLU
Keyword(s):  
Side Effects ◽  
Urban Land ◽  
Thermal Pollution ◽  
Comfort Level ◽  
Study Region ◽  
Local Climate ◽  
Climate Conditions ◽  
Natural Climate ◽  
The City

Abstract Background: Urbanization provides several opportunities to human being to live better and comfortable life. On the other hand, it also comes with some costs and side effects like worsen climate conditions. In local concept, pre-climate conditions in rural area can be called as natural when they are compared against post-climate conditions after urbanization expands over and swallows these natural areas. So, these natural conditions are changed to worsen conditions by some civic activities in cities through urbanization. One of the urbanization side effects is thermal pollution caused by specific urban activities and patterns on land surfaces in cities. Thus, thermal pollution changes city’s local climate and negatively affects the city’s comfort level at least locally. There are several researches focusing on that issue in cities. Each one made its contribution to the area to build up a strong knowledge. One great contribution comes from the researches focusing on analyzing time serious thermal data with continuous distribution over cities.Method: Here in this research is introduced and suggested a Simulated Single Data (SSD) statistical analyze method for the studies based on time serious data. Therefore the method was applied to Remote Sensing (RS) LANDSAT satellites’ bands especially to time series’ thermal bands of Izmir city to reveal where generally Urban Hot Spots (UHS) appear and Urban Heat Islands (UHI) develop in the city w.r.t. this SSD image from long period of time. Stereo representation of the study region is also used to visually examine the topographical effect on UHI distribution.Conclusions: The study clearly demonstrated that industrial regions and roads with large surfaces, somehow bare lands even with spare bushes or grassy lands and more significantly the slope urban land parts within special aspects are the main contributors of UHSs and UHI developments in the city even w.r.t. long term data. Thus those contributors affect the city pre-natural climate conditions negatively and then let UHSs to appear and UHIs to develop at and around where these urban land cover structures are located or seen in the city. Those city parts are the most risky zones that city authorities take serious actions for caring their city chronical climate (thermal) conditions and to focus on for returning these zones back to their pre-natural climate environmental conditions. There are also some nature based solutions that are given and suggested in the conclusion section of the paper for compensation of the effects caused by those contributors in the city.

scholarly journals A 1-km global dataset of historical (1979–2017) and future (2020–2100) Köppen-Geiger climate classification and bioclimatic variables

2021 ◽  
Author(s):  
Diyang Cui ◽  
Shunlin Liang ◽  
Dongdong Wang ◽  
Zheng Liu
Keyword(s):  
Climate Change ◽  
Regional Scale ◽  
Climatic Conditions ◽  
Future Climate ◽  
Distribution Models ◽  
Climate Classification ◽  
Climate Change Research ◽  
Climate Zones ◽  
Bioclimatic Variables

Abstract. The Köppen-Geiger classification scheme provides an effective and ecologically meaningful way to characterize climatic conditions and has been widely applied in climate change studies. Significant changes in Köppen climates have been observed and projected in the recent two centuries. Current accuracy, temporal coverage, spatial and temporal resolution of historical and future climate classification maps cannot sufficiently fulfil the current needs of climate change research. Comprehensive assessment of climate change impacts requires a more accurate depiction of fine-grained climatic conditions and continuous long-term time coverage. Here, we present a series of improved 1-km Köppen-Geiger climate classification maps for ten historical periods in 1979–2017 and four future periods in 2020–2099 under RCP2.6, 4.5, 6.0, and 8.5. The historical maps are derived from multiple downscaled observational datasets and the future maps are derived from an ensemble of bias-corrected downscaled CMIP5 projections. In addition to climate classification maps, we calculate 12 bioclimatic variables at 1-km resolution, providing detailed descriptions of annual averages, seasonality, and stressful conditions of climates. The new maps offer higher classification accuracy and demonstrate the ability to capture recent and future projected changes in spatial distributions of climate zones. On regional and continental scales, the new maps show accurate depictions of topographic features and correspond closely with vegetation distributions. We also provide a heuristic application example to detect long-term global-scale area changes of climate zones. This high-resolution dataset of Köppen-Geiger climate classification and bioclimatic variables can be used in conjunction with species distribution models to promote biodiversity conservation and to analyze and identify recent and future interannual or interdecadal changes in climate zones on a global or regional scale. The dataset referred to as KGClim, is publicly available at http://doi.org/10.5281/zenodo.4546140 for historical climate and http://doi.org/10.5281/zenodo.4542076 for future climate.

scholarly journals A 1-km global dataset of historical (1979–2017) and future (2020–2100) Köppen-Geiger climate classification and bioclimatic variables

2021 ◽  
Author(s):  
Diyang Cui ◽  
Shunlin Liang ◽  
Dongdong Wang ◽  
Zheng Liu
Keyword(s):  
Climate Change ◽  
Regional Scale ◽  
Climate Change Impacts ◽  
Global Scale ◽  
Climatic Conditions ◽  
Distribution Models ◽  
Climate Classification ◽  
Climate Zones ◽  
Bioclimatic Variables

Abstract. The Köppen-Geiger climate classification scheme provides an effective and ecologically meaningful way to characterize climatic conditions and has been widely applied in climate change studies. The Köppen-Geiger climate maps currently available are limited by relatively low spatial resolution, poor accuracy, and noncomparable time periods. Comprehensive assessment of climate change impacts requires a more accurate depiction of fine-grained climatic conditions and continuous long-term time coverage. Here, we present a series of improved 1-km Köppen-Geiger climate classification maps for ten historical periods in 1979–2017 and four future periods in 2020–2099 under RCP2.6, 4.5, 6.0, and 8.5. The historical maps are derived from multiple downscaled observational datasets and the future maps are derived from an ensemble of bias-corrected downscaled CMIP5 projections. In addition to climate classification maps, we calculate 12 bioclimatic variables at 1-km resolution, providing detailed descriptions of annual averages, seasonality, and stressful conditions of climates. The new maps offer higher classification accuracy and demonstrate the ability to capture recent and future projected changes in spatial distributions of climate zones. On regional and continental scales, the new maps show accurate depictions of topographic features and correspond closely with vegetation distributions. We also provide a heuristic application example to detect long-term global-scale area changes of climate zones. This high-resolution dataset of Köppen-Geiger climate classification and bioclimatic variables can be used in conjunction with species distribution models to promote biodiversity conservation and to analyze and identify recent and future interannual or interdecadal changes in climate zones on a global or regional scale. The dataset referred to as KGClim, is publicly available at http://doi.org/10.5281/zenodo.4546140 for historical climate and http://doi.org/10.5281/zenodo.4542076 for future climate.

scholarly journals A 1 km global dataset of historical (1979–2013) and future (2020–2100) Köppen–Geiger climate classification and bioclimatic variables

2021 ◽  
Vol 13 (11) ◽  
pp. 5087-5114
Author(s):  
Diyang Cui ◽  
Shunlin Liang ◽  
Dongdong Wang ◽  
Zheng Liu
Keyword(s):  
Climate Change ◽  
Regional Scale ◽  
Climatic Conditions ◽  
Future Climate ◽  
Distribution Models ◽  
Climate Classification ◽  
Climate Change Research ◽  
Climate Zones ◽  
Bioclimatic Variables

Abstract. The Köppen–Geiger classification scheme provides an effective and ecologically meaningful way to characterize climatic conditions and has been widely applied in climate change studies. Significant changes in the Köppen climates have been observed and projected in the last 2 centuries. Current accuracy, temporal coverage and spatial and temporal resolution of historical and future climate classification maps cannot sufficiently fulfill the current needs of climate change research. Comprehensive assessment of climate change impacts requires a more accurate depiction of fine-grained climatic conditions and continuous long-term time coverage. Here, we present a series of improved 1 km Köppen–Geiger climate classification maps for six historical periods in 1979–2013 and four future periods in 2020–2099 under RCP2.6, 4.5, 6.0, and 8.5. The historical maps are derived from multiple downscaled observational datasets, and the future maps are derived from an ensemble of bias-corrected downscaled CMIP5 projections. In addition to climate classification maps, we calculate 12 bioclimatic variables at 1 km resolution, providing detailed descriptions of annual averages, seasonality, and stressful conditions of climates. The new maps offer higher classification accuracy than existing climate map products and demonstrate the ability to capture recent and future projected changes in spatial distributions of climate zones. On regional and continental scales, the new maps show accurate depictions of topographic features and correspond closely with vegetation distributions. We also provide a heuristic application example to detect long-term global-scale area changes of climate zones. This high-resolution dataset of the Köppen–Geiger climate classification and bioclimatic variables can be used in conjunction with species distribution models to promote biodiversity conservation and to analyze and identify recent and future interannual or interdecadal changes in climate zones on a global or regional scale. The dataset referred to as KGClim is publicly available via http://glass.umd.edu/KGClim (Cui et al., 2021d)​​​​​​​ and can also be downloaded at https://doi.org/10.5281/zenodo.5347837 (Cui et al., 2021c) for historical climate and https://doi.org/10.5281/zenodo.4542076 (Cui et al., 2021b) for future climate.

scholarly journals Climate controlled root zone parameters show potential to improve water flux simulations by land surface models

2021 ◽  
Author(s):  
Fransje van Oorschot ◽  
Ruud J. van der Ent ◽  
Markus Hrachowitz ◽  
Andrea Alessandri
Keyword(s):  
Land Surface ◽  
Root Zone ◽  
Surface Model ◽  
Land Surface Models ◽  
Climate Control ◽  
Local Climate ◽  
Climate Conditions ◽  
Surface Models ◽  
Zone Parameters

Abstract. The root zone storage capacity Sr is the maximum volume of water in the subsurface that can potentially be accessed by vegetation for transpiration. It influences the seasonality of transpiration as well as fast and slow runoff processes. Many studies have shown that Sr is heterogeneous as controlled by local climate conditions, which affect vegetation strategies in sizing their root system able to support plant growth and to prevent water shortages. Root zone parameterization in most land surface models does not account for this climate control on root development, being based on look-up tables that prescribe worldwide the same root zone parameters for each vegetation class. These look-up tables are obtained from measurements of rooting structure that are scarce and hardly representative of the ecosystem scale. The objective of this research is to quantify and evaluate the effects of a climate controlled representation of Sr on the water fluxes modeled by the HTESSEL land surface model. Climate controlled Sr is here estimated with the memory method (MM) in which Sr is derived from the vegetation's memory of past root zone water storage deficits. Sr,MM is estimated for 15 river catchments over Australia across three contrasting climate regions: tropical, temperate and Mediterranean. Suitable representations of Sr,MM are implemented in an improved version of HTESSEL (MD) by accordingly modifying the soil depths to obtain a model Sr-MD that matches Sr,MM in the 15 catchments. In the control version of HTESSEL (CTR), Sr,CTR is larger than Sr,MM in 14 out of 15 catchments. Furthermore, the variability among the individual catchments of Sr,MM (117–722 mm) is considerably larger than of Sr,CTR (491–725 mm) The climate controlled representation of Sr in the MD version results in a significant and consistent improvement of the modeled monthly seasonal climatology (1975–2010) and inter-annual anomalies of river discharge compared with observations. However, the effects on biases in long-term annual mean fluxes are small and mixed. The modeled monthly seasonal climatology of the catchment discharge improved in MD compared to CTR: the correlation with observations increased significantly from 0.84 to 0.90 in tropical catchments, from 0.74 to 0.86 in temperate catchments and from 0.86 to 0.96 in Mediterranean catchments. Correspondingly, the correlations of the inter-annual discharge anomalies improve significantly in MD from 0.74 to 0.78 in tropical catchments, from 0.80 to 0.85 in temperate catchments and from 0.71 to 0.79 in Mediterranean catchments. The results indicate that the use of climate controlled Sr,MM can significantly improve the timing of modeled discharge and, by extension, also evaporation fluxes in land surface models. On the other hand, the method has not shown to significantly reduce long-term climatological model biases over the catchments considered for this study.

Technological Analysis of the Orientation of the Pitched Icelandic Turf Roof to the Cardinal Points and their Effect on the Stability of the Vegetative Layers

2014 ◽  
Vol 1041 ◽  
pp. 19-22 ◽  
Author(s):  
Petr Selnik ◽  
Klara Necadova ◽  
Martin Mohapl
Keyword(s):  
Functional Condition ◽  
Local Climate ◽  
Climate Conditions ◽  
Long Term Stability ◽  
The Stability ◽  
The Right ◽  
Weather Impacts ◽  
Technological Analysis ◽  
Made In

This article is focused on the assessment of the pitched turf green roof to the cardinal points, its permanent sustainable functional condition and investigation of a possible damage caused by local climate. The solution of these problems is the crucial factor in next development, use and expansion of this type of roofing in European broader range. This study of the roof construction that was made in Iceland offers conclusion based on quick changes of the local climate conditions and demanding extreme weather impacts. The main aim of this research is proposed the right possible solution and design of this construction including dealing with the details, select the convenient orientation to the cardinal points and surroundings to provide long-term stability of this construction.

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