scholarly journals Statistical Downscaling of Maximum Temperature in Hoshangabad District of India

The Global Climate ModelsCanESM2 and CGCM3 were utilised to downscale the maximum temperature for Hoshangabad district of Madhya Pradesh, India. The area of study comprises to be of 6704 km2 . The predictors employed for CanESM2 were ncepmslpgl, ncepp500gl, ncepp850gl and ncepmslpas, ncepp500gl, ncepp850gl were the predictors fixed for CGCM3. The total duration of the study was from the years 1979 – 2001. The two GCMs, CGCM3 and CanESM2 were checked for their capability in downscaling the maximum temperature climatic parameter. The GCM outputs were evaluated on Nash Sutcliffe Efficiency (NSE) and coefficient of determination (r2 ) criterias. The period of calibration was taken to be 1979-1995 and 1996-2001 was chosen as the period of validation. GCM CanESM2 obtained NSE of 0.77, 0.75 and r2 of 0.79, 0.79 during the periods of calibration and validation respectively. It was concluded that CanESM2 model is found comparatively more suitable for downscaling of maximum temperature for Hoshangabad region. The GCM can be further employed to generate the future scenario of maximum temperature in the region.

2012 ◽  
Vol 9 (8) ◽  
pp. 9847-9884
Author(s):  
N. Guyennon ◽  
E. Romano ◽  
I. Portoghese ◽  
F. Salerno ◽  
S. Calmanti ◽  
...  

Abstract. Various downscaling techniques have been developed to bridge the scale gap between global climate models (GCMs) and finer scales required to assess hydrological impacts of climate change. Such techniques may be grouped into two downscaling approaches: the deterministic dynamical downscaling (DD) and the stochastic statistical downscaling (SD). Although SD has been traditionally seen as an alternative to DD, recent works on statistical downscaling have aimed to combine the benefits of these two approaches. The overall objective of this study is to examine the relative benefits of each downscaling approach and their combination in making the GCM scenarios suitable for basin scale hydrological applications. The case study presented here focuses on the Apulia region (South East of Italy, surface area about 20 000 km2), characterized by a typical Mediterranean climate; the monthly cumulated precipitation and monthly mean of daily minimum and maximum temperature distribution were examined for the period 1953–2000. The fifth-generation ECHAM model from the Max-Planck-Institute for Meteorology was adopted as GCM. The DD was carried out with the Protheus system (ENEA), while the SD was performed through a monthly quantile-quantile transform. The SD resulted efficient in reducing the mean bias in the spatial distribution at both annual and seasonal scales, but it was not able to correct the miss-modeled non-stationary components of the GCM dynamics. The DD provided a partial correction by enhancing the trend spatial heterogeneity and time evolution predicted by the GCM, although the comparison with observations resulted still underperforming. The best results were obtained through the combination of both DD and SD approaches.


2021 ◽  
Author(s):  
Mohamed Sanusi Shiru ◽  
Eun-Sung Chung

Abstract This study assessed the performances of 13 GCMs of the CMIP6 in replicating precipitation and maximum and minimum temperatures over Nigeria during 1984–2014 in order to identify the best GCMs for multi model ensemble aggregation for climate projection. The study uses the monthly full reanalysis precipitation product Version 6 of Global Precipitation Climatology Centre and the maximum and minimum temperature CRU version TS v. 3.23 products of Climatic Research Unit as reference data. The study applied five statistical indices namely, normalized root mean square error, percentage of bias, Nash-Sutcliffe efficiency, and coefficient of determination; and volumetric efficiency. Compromise programming (CP) was then used in the aggregation of the scores of the different GCMs for the variables. Spatial assessment, probability distribution function, Taylor diagram, and mean monthly assessments were used in confirming the findings from the CP. The study revealed that CP was able to uniformly evaluate the GCMs even though there were some contradictory results in the statistical indicators. Spatial assessment of the GCMs in relation to the observed showed the highest ranked GCMs by the CP were able to better reproduce the observed properties. The least ranking GCMs were observed to have both spatially overestimated or underestimated precipitation and temperature over the study area. In combination with the other measures, the GCMs were ranked using the final scores from the CP. IPSL-CM6A-LR, NESM3, CMCC-CM2-SR5, and ACCESS-ESM1-5 were the highest ranking GCMs for precipitation. For maximum temperature, INM.CM4-8, BCC-CSM2-MR, MRI-ESM2-0, and ACCESS-ESM1-5 ranked the highest while AWI-CM-1-1-MR, IPSL-CM6A-LR, INM.CM5-0, and CanESM5 ranked the highest for minimum temperature.


2012 ◽  
Vol 44 (1) ◽  
pp. 147-168 ◽  
Author(s):  
D. I. Jeong ◽  
A. St-Hilaire ◽  
T. B. M. J. Ouarda ◽  
P. Gachon

This study suggested strategies to project future precipitation series based on a multi-site hybrid SDM (statistical downscaling model), which can downscale precipitation series at multiple observation sites simultaneously by combining the multivariate multiple linear regression (MMLR) model and the stochastic randomization procedure. The hybrid SDM and future projection methodologies applied to 10 observation sites located in the great area of Montréal, Québec, Canada. Six future independent precipitation series were projected from six sets of future atmospheric predictors using three AOGCMs (Atmosphere-Ocean Global Climate Models, i.e. CGCM2, CGCM3, HadCM3) and three IPCC SRES emission scenarios (B2, A1B and A2). Downscaled climate change signals on wet/dry sequences and extreme indices of precipitation time series were evaluated over the future period from 2060 to 2099 with respect to the historical period from 1961 to 2000. The future scenarios of all three AOGCMs showed a consistent increase of 7.9–44.6% in winter while only those of HadCM3 and CGCM3 showed a decrease of 2.3–23.0% in summer compared to their historical values. Precipitation series of CGCM2 A2 and CGCM3 A2 scenarios yielded the largest increase in winter, while those of HadCM3 B2 and A2 scenarios yielded the largest decrease in summer for all statistics indices.


Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3254
Author(s):  
Muhammad Yaseen ◽  
Muhammad Waseem ◽  
Yasir Latif ◽  
Muhammad Imran Azam ◽  
Ijaz Ahmad ◽  
...  

The economy of Pakistan relies on the agricultural sector which mainly depends on the irrigation water generating from the upper Indus river basin. Mangla watershed is a trans-boundary basin which shares borders of India and Pakistan, it comprises five major sub-basins, i.e., Jhelum, Poonch, Kanshi, Neelum and Kunhar. The runoff production of this basin is largely controlled by snowmelt in combination with the winter precipitation in the upper part of the basin and summer monsoon. The present study focusses on the application of a statistical downscaling method to generate future climatic scenarios of climatic trends (temperature and precipitation) in Mangla watershed. Statistical Downscaling Model (SDSM) was applied to downscale the Hadley Centre Coupled Model, version 3, Global Climate Model (HadCM3-GCM) predictions of the A2 and B2 emission scenarios. The surface water analyst tool (SWAT) hydrological model was used for the future projected streamflows based on developing climate change scenarios by SDSM. The results revealed an increasing trend of annual maximum temperature (A2) at the rates of 0.4, 0.7 and 1.2 °C for the periods of 2020s, 2050s and 2080s, respectively. However, a consistent decreasing trend of temperature was observed at the high-altitude region. Similarly, the annual minimum temperature exhibited an increasing pattern at the rates of 0.3, 0.5 and 0.9 °C for the periods of 2020s, 2050s and 2080s, respectively. Furthermore, similar increases were observed for annual precipitation at the rates of 6%, 10%, and 19% during 2020, 2050 and 2080, respectively, for the whole watershed. Significant increasing precipitation trends in the future (2080) were observed in Kunhar, Neelum, Poonch and Kanshi sub-basins at the rates of 16%, 11%, 13% and 59%, respectively. Consequently, increased annual streamflow in the future at the rate of 15% was observed attributing to an increased temperature for snow melting in Mangla watershed. The similar increasing streamflow trend is consistent with the seasonal trends in terms of winter (16%), spring (19%) and summer (20%); however, autumn exhibited decreasing trend for all periods.


2013 ◽  
Vol 17 (2) ◽  
pp. 705-720 ◽  
Author(s):  
N. Guyennon ◽  
E. Romano ◽  
I. Portoghese ◽  
F. Salerno ◽  
S. Calmanti ◽  
...  

Abstract. Various downscaling techniques have been developed to bridge the scale gap between global climate models (GCMs) and finer scales required to assess hydrological impacts of climate change. Such techniques may be grouped into two downscaling approaches: the deterministic dynamical downscaling (DD) and the statistical downscaling (SD). Although SD has been traditionally seen as an alternative to DD, recent works on statistical downscaling have aimed to combine the benefits of these two approaches. The overall objective of this study is to assess whether a DD processing performed before the SD permits to obtain more suitable climate scenarios for basin scale hydrological applications starting from GCM simulations. The case study presented here focuses on the Apulia region (South East of Italy, surface area about 20 000 km2), characterised by a typical Mediterranean climate; the monthly cumulated precipitation and monthly mean of daily minimum and maximum temperature distribution were examined for the period 1953–2000. The fifth-generation ECHAM model from the Max-Planck-Institute for Meteorology was adopted as GCM. The DD was carried out with the Protheus system (ENEA), while the SD was performed through a monthly quantile-quantile correction. The SD resulted efficient in reducing the mean bias in the spatial distribution at both annual and seasonal scales, but it was not able to correct the miss-modelled non-stationary components of the GCM dynamics. The DD provided a partial correction by enhancing the spatial heterogeneity of trends and the long-term time evolution predicted by the GCM. The best results were obtained through the combination of both DD and SD approaches.


2021 ◽  
Author(s):  
Mohammed Magdy Hamed ◽  
Mohamed Salem Nashwan ◽  
Shamsuddin Shahid

Abstract The performances of the Global Climate Models (GCMs) of recently released Coupled Model Intercomparison Project phase 6 (CMIP6) compared to its predecessor, CMIP5 are evaluated to anticipate the expected changes in climate over Egypt, globally one of the most environmentally fragile countries due to water insecurity and climate change. Thirteen common GCMs and their multi-model ensemble (MME) of both CMIPs were used for this purpose. The future projections were compared for two radiative concentration pathways (RCP 4.5 and 8.5), and two shared socioeconomic pathways (SSP 2-4.5 and 5-8.5) scenarios. The results revealed improvement in most CMIP6 models in replicating historical rainfall, maximum temperature (Tmax) and minimum temperature (Tmin) climatology over Egypt. The MME of the CMIPs revealed that both could reproduce the spatial distribution and seasonal variability of climate in Egypt. However, the bias in CMIP6 is much less than that for CMIP5. The uncertainty in simulating seasonal variability of rainfall and temperature was lower for CMIP6 compared to CMIP5. The future projection of rainfall using CMIP6 MME revealed a higher reduction of precipitation (4 to 10 mm) in the economically crucial northern region compared to that estimated using CMIP5 (10 to >15 mm). CMIP6 also projected a 1.5 to 2.5ºC more rise in Tmax and Tmin compared to CMIP5. The study indicates more aggravated scenarios of climate changes in Egypt than anticipated earlier, using the CMIP5 model. Therefore, Egypt needs to streamline the existing adaptation measures formulated based on CMIP5 projections.


2021 ◽  
Vol 18 ◽  
pp. 99-114
Author(s):  
M. Bazlur Rashid ◽  
Syed Shahadat Hossain ◽  
M. Abdul Mannan ◽  
Kajsa M. Parding ◽  
Hans Olav Hygen ◽  
...  

Abstract. The climate of Bangladesh is very likely to be influenced by global climate change. To quantify the influence on the climate of Bangladesh, Global Climate Models were downscaled statistically to produce future climate projections of maximum temperature during the pre-monsoon season (March–May) for the 21st century for Bangladesh. The future climate projections are generated based on three emission scenarios (RCP2.6, RCP4.5 and RCP8.5) provided by the fifth Coupled Model Intercomparison Project. The downscaling process is undertaken by relating the large-scale seasonal mean temperature, taken from the ERA5 reanalysis data set, to the leading principal components of the observed maximum temperature at stations under Bangladesh Meteorological Department in Bangladesh, and applying the relationship to the GCM ensemble. The in-situ temperature data has only recently been digitised, and this is the first time they have been used in statistical downscaling of local climate projections for Bangladesh. This analysis also provides an evaluation of the local data, and the local temperatures in Bangladesh show a close match with the ERA5 reanalysis. Compared to the reference period of 1981–2010, the projected maximum pre-monsoon temperature in Bangladesh indicate an increase by 0.7/0.7/0.7 ∘C in the near future (2021–2050) and 2.2/1.2/0.8 ∘C in the far future (2071–2100) assuming the RCP8.5/RCP4.5/RCP2.6 scenario, respectively.


2020 ◽  
Author(s):  
Rubén D. Manzanedo ◽  
Peter Manning

The ongoing COVID-19 outbreak pandemic is now a global crisis. It has caused 1.6+ million confirmed cases and 100 000+ deaths at the time of writing and triggered unprecedented preventative measures that have put a substantial portion of the global population under confinement, imposed isolation, and established ‘social distancing’ as a new global behavioral norm. The COVID-19 crisis has affected all aspects of everyday life and work, while also threatening the health of the global economy. This crisis offers also an unprecedented view of what the global climate crisis may look like. In fact, some of the parallels between the COVID-19 crisis and what we expect from the looming global climate emergency are remarkable. Reflecting upon the most challenging aspects of today’s crisis and how they compare with those expected from the climate change emergency may help us better prepare for the future.


Author(s):  
Laurie Essig

In Love, Inc., Laurie Essig argues that love is not all we need. As the future became less secure—with global climate change and the transfer of wealth to the few—Americans became more romantic. Romance is not just what lovers do but also what lovers learn through ideology. As an ideology, romance allowed us to privatize our futures, to imagine ourselves as safe and secure tomorrow if only we could find our "one true love" today. But the fairy dust of romance blinded us to what we really need: global movements and structural changes. By traveling through dating apps and spectacular engagements, white weddings and Disney honeymoons, Essig shows us how romance was sold to us and why we bought it. Love, Inc. seduced so many of us into a false sense of security, but it also, paradoxically, gives us hope in hopeless times. This book explores the struggle between our inner cynics and our inner romantic.


2020 ◽  
Vol 20 (2) ◽  
pp. 7-13
Author(s):  
G. Stankevych ◽  
L. Dmytrenko ◽  
A. Kats ◽  
V. Shpak

In the future, in Ukraine it is planned to increase the sown area for cereals, legumes and oilseeds, to increase the gross grain harvest to 80 million tons, and its export abroad was increased twice. Intensive construction in the southern ports of Ukraine of grain transshipment terminals with large metal silos will solve the problem of increasing grain export in the future. At these powerful terminals, the bulk of the grain comes mainly by rail, and is shipped to water. The aim of the work was to study the characteristics of the grain receiving from railway transport to the grain transshipment terminal of LLC “Ukrelevatorprom” in order to improve its works efficiency. The object of the study was the development of technology of grain receiving at the grain transshipment terminal; the subject of research is cereals, legume sand oil crops, as well as data from daily volumes of receiving and dispensing operations at the grain transshipment terminal of LLC “Ukrelevatorprom” for 2015-2016. The studies were carried out on the basis of processing data from the consignment notes for 2015-2016, according to which there was a summed amount of grain (net) daily transported by the railway. Further processing of the obtained data was carried out by a combined graphoanalytical method, for which, on the basis of tabular values for each studied year, the corresponding histograms and graphs were built and the necessary indicators were determined. Analysis of the structure of grain crops supplied by railway to LLC “Ukrelevatorprom” in 2015 and 2016 and their ratio showed that the main share was occupied by cereal crops (78.0 % and 73.1 % respectively), which were mainly represented by corn, share which was significantly dominated by other crops (wheat of various classes and barley) and amounted to 45.8 % and 44.5 %, respectively, which can be explained by its high demand in the international grain market, in which Ukraine occupies a leading position. Oilseeds (rapeseed) were taken in accordance with 19.1 % and 14.9 %, and legumes (soybeans) — 2.9 % and 12.0 %. An analysis of the timing of the unloading of grain wagons (hopper cars) showed that the total duration of this process, depending on the crops, averages 37...59 minutes. The longest steps for unloading wagons are to determine the grain quality indicators, especially rapeseed, and to spill grain from the wagons, therefore, to reduce their duration, it is necessary to form feeds of wagons with grain batches of the same quality and use more modern express analyzers to determine grain quality indicators, which will increase the productivity of the grain receiving line from the railway. According to the research results, the enterprise has the potential to increase by about 30 % the volume of grain intake. It was established that the periods of the grain receipt at the enterprise in 2015-2016 amounted to 349 and 353 days, respectively, the actual coefficients of the daily irregularity Kdaily for the grain receipt from the railway in these years are equal to 1.47 and 1.52, and the monthly irregularity Kmonth, respectively 1.33 and 1.21, does not exceed the standard values Kdaily = 2.5 and Kmonth= 2.0. This made it possible to clarify the database from the actual characteristics of the process of grain receiving by railway and can be used in design and verification calculations of equipment in technological lines for receiving grain from railway transport, and will contribute to increasing the efficiency of grain transshipment terminals. 


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