scholarly journals Temporal Patterns of the Two-Dimensional Spatial Trends in Summer Temperature and Monsoon Precipitation of Bangladesh

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Avit Kumar Bhowmik

Two climate indices, TXx and PRCPTOT, representing the summer maximum temperature and annual total monsoon precipitation, respectively, in Bangladesh were computed. The temperature and precipitation measurements from 34 meteorological stations during the temporal extent of 1948–2007 were applied for indices’ computation under thorough quality control. The spatial trends of the indices were analyzed by applying two-dimensional least square approach along latitudes and longitudes of the observation points. The temporal patterns of the spatial trends were identified by temporally interpolating them applying thin plate smoothing spline method. The analyses of TXx identified regional scale spatial trends in the east-west and south-north directions, which were increasing between 1948 and 1980s. After the 1980s the spatial trends started decreasing, and after 2000 the spatial trend along the south-north changed its direction to the north-south and continued until present. The analyses of the PRCPTOT identified spatial trends in the west-east and north-south directions, which were decreasing between 1948 and 1980s and thereafter increasing until present. About half of the spatial trends were significant in F-statistics at or more than 90% confidence level. Thus, the obtained results indicated a significant climatic shift within the regional scale of the country during the study period.

2020 ◽  
Author(s):  
Joanna Struzewska ◽  
Maciej Jefimow ◽  
Paulina Jagiełło ◽  
Maria Kłeczek ◽  
Anahita Sattari ◽  
...  

<p>Regional climate projections are necessary to assess possible changes in the exposure and risk to allow planning the adaptation strategies.</p><p>Projections of temperature and precipitation trends were developed using a consistent methodology and homogeneous datasets to address the needs of up-to-date climate change scenarios for Poland.</p><p>The Euro-Cordex results with the resolution of 0.11deg (about 12.5km) for RCP4.5 and RCP8.5 were downscaled based on various historical gridded datasets (EOBS, ERA5, UERRA and data from IMWM).</p><p>Ensemble analysis was undertaken to assess the projection uncertainty and ensemble mean were calculated for base parameters (daily average, minimum, and maximum temperature and daily precipitation sum) as well as for the number of climate indices.</p><p>We will present spatial and temporal variability of selected climate indices over Poland for subsequent decades. Increase of the annual average temperature is due to the rise in the number of hot days and the reduction of the number of frost days. All temperature indices are characterized by statistically significant trends, strongest for RCP8.5. The most pronounced changes in the frequency and amount of precipitation occur in the north-east of Poland. The total number of days with precipitation increases slightly. The increase in the annual rainfall is due to the increase in the number of days with extreme precipitation.</p><p>Results are presented via an interactive web portal. Further analysis includes the development of projection for solar radiation, wind speed, humidity and snow cover.</p>


Author(s):  
Hareef Ahmed Keerio

The purpose of the study was to investigate the variation in climatic parameters and possible climate effects in the Hyderabad region. The least-square regression method was used to find a linear change in climatic parameters (Temperature and Precipitation). The maximum, minimum, and mean temperatures; annual, and monsoon precipitations were considered under the study. In the last 100 years, the global temperature has been increased by 0.6 or 0.74 0C. In Hyderabad city, we predicted that the minimum temperature (Tmin), maximum temperature (Tmax), and mean temperature (Tmean) are varied in the range of 0.00490C/year to -0.01330C/year. The variability in the precipitation was observed in the last 30 years. Yearly and monsoon precipitation was decreasing with the rate of 1.24mm/year, and 1.34mm/year. The maximum precipitation occurs in July, August, and September; in the rest of the months, no or little precipitation occurred which may lead to a shortage of fresh water.


2020 ◽  
Author(s):  
Luc Yannick Andréas Randriamarolaza ◽  
Enric Aguilar ◽  
Oleg Skrynyk

<p>Madagascar is an Island in Western Indian Ocean Region. It is mainly exposed to the easterly trade winds and has a rugged topography, which promote different local climates and biodiversity. Climate change inflicts a challenge on Madagascar socio-economic activities. However, Madagascar has low density station and sparse networks on observational weather stations to detect changes in climate. On average, one station covers more than 20 000 km<sup>2</sup> and closer neighbor stations are less correlated. Previous studies have demonstrated the changes on Madagascar climate, but this paper contributes and enhances the approach to assess the quality control and homogeneity of Madagascar daily climate data before developing climate indices over 1950 – 2018 on 28 synoptic stations. Daily climate data of minimum and maximum temperature and precipitation are exploited.</p><p>Firstly, the quality of daily climate data is controlled by INQC developed and maintained by Center for Climate Change (C3) of Rovira i Virgili University, Spain. It ascertains and improves error detections by using six flag categories. Most errors detected are due to digitalization and measurement.</p><p>Secondly, daily quality controlled data are homogenized by using CLIMATOL. It uses relative homogenization methods, chooses candidate reference series automatically and infills the missing data in the original data. It has ability to manage low density stations and low inter-station correlations and is tolerable for missing data. Monthly break points are detected by CLIMATOL and used to split daily climate data to be homogenized.</p><p>Finally, climate indices are calculated by using CLIMIND package which is developed by INDECIS<sup>*</sup> project. Compared to previous works done, data period is updated to 10 years before and after and 15 new climate indices mostly related to extremes are computed. On temperature, significant increasing and decreasing decade trends of day-to-day and extreme temperature ranges are important in western and eastern areas respectively. On average decade trends of temperature extremes, significant increasing of daily minimum temperature is greater than daily maximum temperature. Many stations indicate significant decreasing in very cold nights than significant increasing in very warm days. Their trends are almost 1 day per decade over 1950 – 2018. Warming is mainly felt during nighttime and daytime in Oriental and Occidental parts respectively. In contrast, central uplands are warming all the time but tropical nights do not appear yet. On rainfall, no major significant findings are found but intense precipitation might be possible at central uplands due to shortening of longest wet period and occurrence of heavy precipitation. However, no influence detected on total precipitation which is still decreasing over 1950 - 2018. Future works focus on merging of relative homogenization methodologies to ameliorate the results.</p><p>-------------------</p><p>*INDECIS is a part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462).</p>


2009 ◽  
Vol 48 (4) ◽  
pp. 742-757 ◽  
Author(s):  
Shawn P. Serbin ◽  
Christopher J. Kucharik

Abstract Results from the generation of a multidecadal gridded climatic dataset for 57 yr (1950–2006) of daily and monthly precipitation (PTotal), maximum temperature (Tmax), and minimum temperature (Tmin) are presented for the important agricultural and forest products state of Wisconsin. A total of 176 climate stations were used in the final gridded dataset that was constructed at 8-km (5.0′) latitude–longitude resolution using an automated inverse distance weighting interpolation. Accuracy statistics for the interpolated data were based on a rigorous validation step using 104 first- and second-order climate observation stations withheld in the production of the gridded dataset. The mean absolute errors (MAE) for daily minimum and maximum temperatures averaged 1.51° and 1.31°C, respectively. Daily precipitation errors were also reasonable, ranging from −0.04 to 0.08 mm, on average, across all climate divisions in the state with an overall statewide MAE of 1.37 mm day−1. Correlation analysis suggested a high degree of explained variation for daily temperature (R2 ≥ 0.97) and a moderate degree for daily precipitation (R2 = 0.66), whereby the realism improved considerably for monthly precipitation accumulation totals (R2 = 0.87). Precipitation had the best interpolation accuracy during the winter months, related to large-scale, synoptic weather systems, and accuracy was at a minimum in the wetter summer months when more precipitation originates from local-to-regional-scale convective forcing. Overall the grids showed coherent spatial patterns in temperature and precipitation that were expected for this region, such as the latitudinal gradient in temperature and longitudinal gradient in precipitation across the state. The grids will prove useful for a variety of regional-scale research and ecosystem modeling studies.


2011 ◽  
Vol 68 (6) ◽  
pp. 1131-1137 ◽  
Author(s):  
Masa-aki Fukuwaka ◽  
Toshiki Kaga ◽  
Tomonori Azumaya

Abstract Fukuwaka, M., Kaga, T., and Azumaya, T. 2011. Regional differences in climate factors controlling chum and pink salmon abundance. – ICES Journal of Marine Science, 68: 1131–1137. Chum and pink salmon abundances vary on a decadal time-scale. We examined the relationship between large-scale climate indices (CIs), regional climate factors (RFs), and rates of change in regional catches (RCs) of chum and pink salmon in five regions of the North Pacific. Correlation coefficients of RCs with RFs were larger than those of RCs with CIs, although the correlation coefficient of particular variables varied among regions. Climate affected salmon stocks as indicated by significant relationships with various terrestrial and ocean climate factors on a regional scale. These results suggest that no single CI or RF controls salmon abundance in all regions; however, global climate changes could affect regional climate directly and regional salmon abundance indirectly. A warming trend in the North Pacific might affect the long-term change in salmon abundance. The mechanisms controlling regional salmon abundance must be understood better to forecast successfully future conditions for Pacific salmon stocks, because the response of salmon stocks to global climate change varies among regions.


2011 ◽  
Vol 15 (24) ◽  
pp. 1-36 ◽  
Author(s):  
Sarah E. Perkins

Abstract Using the Coupled Model Intercomparison Project phase 3 (CMIP3) general circulation models (GCMs), projections of a range of climate extremes are explored for the western Pacific. These projections include the 1-in-20-yr return levels and a selection of climate indices for minimum temperature, maximum temperature, and precipitation, and they are compared to corresponding mean projections for the Special Report on Emission Scenarios (SRES) A2 scenario during 2081–2100. Models are evaluated per variable based on their ability to simulate current extremes, as well as the overall daily distribution. Using the standardized evaluation scores for each variable, models are divided into four subsets where ensemble variability is calculated to measure model uncertainty and biases are calculated in respect to the multimodel ensemble (MME). Results show that higher uncertainty in projections of climate extremes exists when compared to the mean, even in those subsets consisting of higher-skilled models. Higher uncertainty exists for precipitation projections than for temperature, and biases and uncertainties in the 1-in-20-yr precipitation events are an order of magnitude higher than the corresponding mean. Poorer performing models exhibit a cooler bias in the mean and 1-in-20-yr return levels for maximum and minimum temperature, and ensemble variability is low among all subsets of mean minimum temperature, especially the lower-skilled subsets. Higher-skilled models project 1-in-20-yr precipitation return levels that are more intense than in the MME. The frequency of temperature extremes increase dramatically; however, this is explained by the underpinning small temperature range of the region. Although some systematic biases occur in the higher- and lower-skilled models and omitting the poorer performers is recommended, great care should be exercised when interpreting the reduction of uncertainty because the ensemble variability among the remaining models is comparable and in some cases greater than the MME. Such results should be treated on a case-by-case basis.


2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Heather MacDonald ◽  
Daniel W. McKenney ◽  
Pia Papadopol ◽  
Kevin Lawrence ◽  
John Pedlar ◽  
...  

AbstractWe present historical monthly spatial models of temperature and precipitation generated from the North American dataset version “j” from the National Oceanic and Atmospheric Administration’s (NOAA’s) National Centres for Environmental Information (NCEI). Monthly values of minimum/maximum temperature and precipitation for 1901–2016 were modelled for continental United States and Canada. Compared to similar spatial models published in 2006 by Natural Resources Canada (NRCAN), the current models show less error. The Root Generalized Cross Validation (RTGCV), a measure of the predictive error of the surfaces akin to a spatially averaged standard predictive error estimate, averaged 0.94 °C for maximum temperature models, 1.3 °C for minimum temperature and 25.2% for total precipitation. Mean prediction errors for the temperature variables were less than 0.01 °C, using all stations. In comparison, precipitation models showed a dry bias (compared to recorded values) of 0.5 mm or 0.7% of the surface mean. Mean absolute predictive errors for all stations were 0.7 °C for maximum temperature, 1.02 °C for minimum temperature, and 13.3 mm (19.3% of the surface mean) for monthly precipitation.


2010 ◽  
Vol 19 (3) ◽  
pp. 325 ◽  
Author(s):  
Lara Vilar ◽  
Douglas. G. Woolford ◽  
David L. Martell ◽  
M. Pilar Martín

This paper describes the development and validation of a spatio-temporal model for human-caused wildfire occurrence prediction at a regional scale. The study area is the 8028-km2 region of Madrid, located in central Spain, where more than 90% of wildfires are caused by humans. We construct a logistic generalised additive model to estimate daily fire ignition risk at a 1-km2 grid spatial resolution. Spatially referenced socioeconomic and weather variables appear as covariates in the model. Spatial and temporal effects are also included. The variables in the model were selected using an iterative approach, which we describe. We use the model to predict the expected number of fires in our study area during the 2002–05 period, by aggregating the estimated probabilities over space–time scales of interest. The estimated partial effects of the presence of railways, roads, and wildland–urban interface in forest areas were highly significant, as were the observed daily maximum temperature and precipitation.


2015 ◽  
Vol 42 (3) ◽  
pp. 227-236 ◽  
Author(s):  
GEORG H. ENGELHARD ◽  
CHRISTOPHER P. LYNAM ◽  
BERNARDO GARCÍA-CARRERAS ◽  
PAUL J. DOLDER ◽  
STEVEN MACKINSON

SUMMARYThe large fish indicator (LFI), or ‘proportion of fish greater than 40 cm length in bottom trawl surveys,’ is a frequently debated indicator of Good Environmental Status in European regional seas. How does the LFI respond to changes in fishing pressure? This question is addressed here through analysis of fine-scale spatial trends in the LFI within the North Sea, compared between two periods of contrasting fisheries management: 1983–1999 and 2000–2012, respectively, before and after the onset of the European Union's fleet reduction scheme. Over the entire period, the LFI has decreased in large parts of the North Sea. However, most of the decline was from 1983–1999; since 2000 the LFI has improved in much of the North Sea, especially in UK waters. Comparison with international effort data shows that those western areas where the LFI has improved correspond with regions where otter trawl effort has decreased since 2000 (and previously was highest in the 1990s), and also with decreases in beam trawl effort. This study provides strong support that recent European effort reduction schemes are now beginning to result in an improved ecosystem state as indicated by the regional-scale improvement in the LFI.


1993 ◽  
Vol 27 (7-8) ◽  
pp. 381-385 ◽  
Author(s):  
Y. Oziransky ◽  
B. Shteinman

Data of high spatial and temporal resolution, and a special sampling program are essential for successful application of mathematical models designed to reproduce observed seasonal patterns of temperature, dissolved oxygen, nutrients, pH, and algal biomass for both vertical and longitudinal gradients in a water body. Lake Kinneret suspended solids are of great potential value for estimating transport, exposure to water body elements, and fate of many toxic substances. Therefore the distribution of admixtures in two longitudinal and five vertical segmentation schemes were examined with the two-dimensional water body quality box model “BETTER” (Bender et al, 1990). The transects were taken in the north-western part of Lake Kinneret close to the Jordan River mouth and the National Water Carrier (NWC) head pumping station. The outflow volumes were given according to regular sampling of natural speed of water outflow from different lake layers under calm conditions. Temporal distribution of mixing concentrations as well as turbulent diffusion horizontal coefficients due to the spatial distribution of turbulent scale were obtained during the model's run with the December 1991 data.


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