scholarly journals Cenários de Mudanças Climáticas no Estado da Bahia através de Estudos Numéricos e Estatísticos (Climate Change Scenarios in Bahia through Numerical and Statistical Studies)

2013 ◽  
Vol 5 (5) ◽  
pp. 1019 ◽  
Author(s):  
Gildarte Barbosa Silva ◽  
Werônica Meira Souza ◽  
Pedro Vieira Azevedo
Keyword(s):  
Climate Change ◽  
Climatic Variability ◽  
Climatic Changes ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Climate Change Scenarios ◽  
Total Precipitation ◽  
Annual Total Precipitation ◽  
Annual Total ◽  
Statistical Studies

Este trabalho teve como objetivo investigar a ocorrência ou ausência de mudanças climáticas no período de 1970 a 2006, em algumas microrregiões do estado da Bahia: Irecê, Oeste, Sudoeste e Baixo Médio São Francisco, através de índices de tendências de mudanças climáticas obtidos da precipitação pluviométrica e das temperaturas máxima e mínima diárias das estações climatológicas das respectivas regiões e de cenários de mudanças climáticas. Utilizou-se os índices de detecção de mudanças climáticas sugeridos pela OMM calculados a partir dos dados de precipitação e das temperaturas máxima e mínima diárias através do software RClimdex 1.9.0. No estudo numérico foi utilizado o modelo BRAMS. Observou-se que na região de Irecê houve tendência de diminuição da precipitação total anual e aumento da intensidade das chuvas diárias. Na região Oeste houve aumento no número de dias com temperaturas elevadas, aumento nas temperaturas mínimas diárias e aumento na intensidade das chuvas. Na região Sudoeste houve uma  tendência de um pequeno aumento dos totais anuais de chuvas. Na região do Baixo Médio São Francisco houve aumento no número de dias com temperatura máxima diária, diminuição das chuvas diárias e da precipitação total anual. Essa variação na precipitação na região pode ser atribuída à circulação de grande escala, enquanto a intensidade das chuvas pode ter influência na variabilidade climática. Cabe aos gestores desse país encarar essa realidade com muita responsabilidade e, sugira ações e medidas eficazes para combatê-la, capacitando a sociedade como um todo para conviver com essa nova realidade. Palavras-chave: Mudanças climáticas; estudos numéricos; índices de tendências climáticas.   Climate Change Scenarios in Bahia through Numerical and Statistical Studies   ABSTRACT This work had as objective to investigate the occurrence or absence of climatic changes in the period of 1970 the 2006, in some microregions of the state of the Bahia: Irecê, Oeste, Sudoeste and Baixo Médio São Francisco, through indexes of trends of climatic changes with data of daily total precipitation and the daily temperatures maximum and minimum of the climatological stations of the respective regions and climate change scenarios. One used the indexes of detection of climatic changes suggested by WMO calculated from the data of daily precipitation and the daily temperature through software RClimdex 1.9.0. The study used numerical model BRAMS. It was observed that in the region of Irecê it had trend of reduction of the annual total precipitation and increase in the intensity of daily rains. In the region Oeste it had increase in the number of days with raised temperatures, increase in the daily minimum temperatures and increase in the intensity of rains. In the Sudeste region it had a trend of a small increase of the annual rain totals. In the region of the Baixo Médio São Francisco it had increase the number of days with daily maximum temperature, reduction of daily rains and the annual total precipitation. This variation in the precipitation in the region can be attributed to the circulation of great scale, while the intensity of rains can have influence in the climatic variability. It is the managers of this country face that reality as something that must be faced with great responsibility, and suggest actions and effective measures to combat it enabling the society as a whole to deal with this new reality.Keywords: Climatic changes; numerical studies; climate trends. 

scholarly journals Application of Artificial Neural Networks to Project Reference Evapotranspiration under Climate Change Scenarios

2021 ◽  
Author(s):  
Junaid Maqsood ◽  
Aitazaz A. Farooque ◽  
Farhat Abbas ◽  
Travis Esau ◽  
Xander Wang ◽  
...  
Keyword(s):  
Climate Change ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
General Circulation ◽  
Reference Evapotranspiration ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Climate Change Scenarios ◽  
Artificial Neural

Abstract Evapotranspiration, one of the major elements of the water cycle, is sensitive to climate change. The main objective of this study was to examine the response of reference evapotranspiration (ET0) under various climate change scenarios using artificial neural networks and a general circulation model (GCM) - the Canadian Earth System Model Second Generation (CanESM2). The Hargreaves method was used to calculate ET0 for western, central, and eastern parts of Prince Edward Island. The two input parameters of the Hargreaves method; daily maximum temperature (Tmax), and daily minimum temperature (Tmin) were projected using CanESM2. The Tmax and Tmin were downscaled with the help of statistical downscaling and simulation model (SDSM) for three future periods 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2100) under three representative concentration pathways (RCP’s) including RCP 2.6, RCP P4.5, and RCP 8.5, and the. Temporally, there were major changes in Tmax, Tmin, and ET0 for the 2080s under RCP8.5. The temporal variations in ET0 for all RCPs matched the reports in the literature for other similar locations and for RCP8.5 it ranged from 1.63 (2020s) to 2.29 mm/day (2080s). As a next step, a one-dimensional convolutional neural network (1D-CNN), long-short term memory (LSTM), and multilayer perceptron (MLP) were used for estimating ET0 due to the non-linear behavior of ET0 and the limited meteorological input data. High coefficient of correlation (r > 0.95) values for both calibration and validation periods showed the potential of the artificial neural networks in ET0 estimation. The results of this study will help decision makers and water resource managers to quantify the availability of water in future for the island and to optimize the use of island water resources on a sustainable basis.

Underestimation of temperature variability in weather generators and implications for the representation of extreme temperatures in downscaled climate change scenarios

2020 ◽  
Author(s):  
Pierluigi Calanca
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Extreme Values ◽  
Climatic Variability ◽  
Model Performance ◽  
Maximum Temperature ◽  
Climate Change Scenarios ◽  
Modes Of Variability ◽  
Weather Generators ◽  
Total Variability

<p>Stochastic weather generators are still widely used for downscaling climate change scenarios, in particular in the context of agricultural and hydrological impact assessments. Their performance is in many respects satisfactory, except perhaps for the fact that they fail to represent climatic variability in an adequate way. This has implications for the representation of extreme values and their statistics. Concerning precipitation, different approaches for amending this situation have proposed in the past, including using more sophisticated models to better simulate the persistence of wet and dry spells, conditioning rainfall-generating parameters on indices of the large-scale atmospheric circulation, or employing autoregressive models to represent year-to-year variations in annual precipitation amounts. With regard to (minimum and maximum) temperature, efforts to address the question of why weather generators underestimate total variability have been less systematic. Based on results obtained with a well-known weather generator (LARS-WG), this contribution aims to discuss which modes of variability are missing and why, elaborate on the implications of underrepresenting temperature variance for the simulation of temperature extremes in downscaled climate change scenarios, and suggest options to tackle the problem and improve the model performance.</p>

scholarly journals Elevation-Dependent Trends in Precipitation Observed over and around the Tibetan Plateau from 1971 to 2017

Water ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 2848
Author(s):  
Wenfeng Hu ◽  
Junqiang Yao ◽  
Qing He ◽  
Jing Chen
Keyword(s):  
Climate Change ◽  
Tibetan Plateau ◽  
Extreme Precipitation ◽  
Strong Dependence ◽  
The Tibetan Plateau ◽  
Total Precipitation ◽  
Annual Total Precipitation ◽  
Annual Total ◽  
Extreme Precipitation Indices ◽  
Precipitation Indices

The Tibetan Plateau (TP) are regions that are most sensitive to climate change, especially extreme precipitation changes with elevation, may increase the risk of natural disasters and have attracted attention for the study of extreme events in order to identify adaptive actions. Based on daily observed data from 113 meteorological stations in the Tibetan Plateau and the surrounding regions in China during 1971–2017, we calculated the annual total precipitation and extreme precipitation indices using the R ClimDex software package and explored elevation-dependent precipitation trends. The results demonstrate that the annual total precipitation increased at a rate of 6.7 mm/decade, and the contribution of extreme precipitation to total precipitation increased over time, and the climate extremes were enhanced. The annual total, seasonal precipitation, and precipitation extreme trends were observed in terms of elevation dependence in the Tibetan Plateau (TP) and the surrounding area of the Tibetan Plateau (TPS) during 1971–2017. There is growing evidence that the elevation-dependent wetting (EDWE) is complex over the TP. The trends in total precipitation have a strong dependence on elevation, and the EDWE is highlighted by the extreme precipitation indices, for example, the number of heavy precipitation days (R10) and consecutive wet days (CWD). The dependence of extreme precipitation on elevation is heterogeneous, as other extreme indices do not indicate EDWE. These findings highlight the precipitation complexity in the TP. The findings of this study will be helpful for improving our understanding of variabilities in precipitation and extreme precipitation in response to climate change and will provide support for water resource management and disaster prevention in plateaus and mountain ranges.

scholarly journals Observed Trends in Indices of Daily Temperature Extremes in South America 1960–2000

2005 ◽  
Vol 18 (23) ◽  
pp. 5011-5023 ◽  
Author(s):  
L. A. Vincent ◽  
T. C. Peterson ◽  
V. R. Barros ◽  
M. B. Marino ◽  
M. Rusticucci ◽  
...  
Keyword(s):  
Climate Change ◽  
South America ◽  
Climate Extremes ◽  
Daily Temperature ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Increasing Trends

Abstract A workshop on enhancing climate change indices in South America was held in Maceió, Brazil, in August 2004. Scientists from eight southern countries brought daily climatological data from their region for a meticulous assessment of data quality and homogeneity, and for the preparation of climate change indices that can be used for analyses of changes in climate extremes. This study presents an examination of the trends over 1960–2000 in the indices of daily temperature extremes. The results indicate no consistent changes in the indices based on daily maximum temperature while significant trends were found in the indices based on daily minimum temperature. Significant increasing trends in the percentage of warm nights and decreasing trends in the percentage of cold nights were observed at many stations. It seems that this warming is mostly due to more warm nights and fewer cold nights during the summer (December–February) and fall (March–May). The stations with significant trends appear to be located closer to the west and east coasts of South America.

Assessing the contribution of multiple forcings to changes in temperature extremes 1981–2020 using CMIP6 climate models

2021 ◽  
Author(s):  
Mastawesha Misganaw Engdaw ◽  
Andrew Ballinger ◽  
Gabriele Hegerl ◽  
Andrea Steiner
Keyword(s):  
Climate Change ◽  
Greenhouse Gases ◽  
Climate Models ◽  
Temporal Trends ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Cold Temperatures

<p>In this study, we aim at quantifying the contribution of different forcings to changes in temperature extremes over 1981–2020 using CMIP6 climate model simulations. We first assess the changes in extreme hot and cold temperatures defined as days below 10% and above 90% of daily minimum temperature (TN10 and TN90) and daily maximum temperature (TX10 and TX90). We compute the change in percentage of extreme days per season for October-March (ONDJFM) and April-September (AMJJAS). Spatial and temporal trends are quantified using multi-model mean of all-forcings simulations. The same indices will be computed from aerosols-, greenhouse gases- and natural-only forcing simulations. The trends estimated from all-forcings simulations are then attributed to different forcings (aerosols-, greenhouse gases-, and natural-only) by considering uncertainties not only in amplitude but also in response patterns of climate models. The new statistical approach to climate change detection and attribution method by Ribes et al. (2017) is used to quantify the contribution of human-induced climate change. Preliminary results of the attribution analysis show that anthropogenic climate change has the largest contribution to the changes in temperature extremes in different regions of the world.</p><p><strong>Keywords:</strong> climate change, temperature, extreme events, attribution, CMIP6</p><p> </p><p><strong>Acknowledgement:</strong> This work was funded by the Austrian Science Fund (FWF) under Research Grant W1256 (Doctoral Programme Climate Change: Uncertainties, Thresholds and Coping Strategies)</p>

Comparisons of Machine Learning Methods of Statistical Downscaling Method: Case Studies of Daily Climate Anomalies in Thailand

2021 ◽  
Author(s):  
Kanawut Chattrairat ◽  
Waranyu Wongseree ◽  
Adisorn Leelasantitham
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Performance Criteria ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Support Vector ◽  
Seasonal Temperature ◽  
Statistical Downscaling Method ◽  
Downscaling Method

The climate change which is essential for daily life and especially agriculture has been forecasted by global climate models (GCMs) in the past few years. Statistical downscaling method (SD) has been used to improve the GCMs and enables the projection of local climate. Many pieces of research have studied climate change in case of individually seasonal temperature and precipitation for simulation; however, regional difference has not been included in the calculation. In this research, four fundamental SDs, linear regression (LR), Gaussian process (GP), support vector machine (SVM) and deep learning (DL), are studied for daily maximum temperature (TMAX), daily minimum temperature (TMIN), and precipitation (PRCP) based on the statistical relationship between the larger-scale climate predictors and predictands in Thailand. Additionally, the data sets of climate variables from over 45 weather stations overall in Thailand are used to calculate in this calculation. The statistical analysis of two performance criteria (correlation and root mean square error (RMSE)) shows that the DL provides the best performance for simulation. The TMAX and TMIN were calculated and gave a similar trend for all models. PRCP results found that in the North and South are adequate and poor performance due to high and low precipitation, respectively. We illustrate that DL is one of the suitable models for the climate change problem.

scholarly journals Ensemble Projections of Regional Climatic Changes over Ontario, Canada

2015 ◽  
Vol 28 (18) ◽  
pp. 7327-7346 ◽  
Author(s):  
Xiuquan Wang ◽  
Guohe Huang ◽  
Jinliang Liu ◽  
Zhong Li ◽  
Shan Zhao
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Regional Climate ◽  
Climatic Changes ◽  
Climate Projections ◽  
Climate Change Scenarios ◽  
Local Climate ◽  
Ensemble Simulations ◽  
Extreme Precipitation Events ◽  
Data Portal

Abstract In this study, high-resolution climate projections over Ontario, Canada, are developed through an ensemble modeling approach to provide reliable and ready-to-use climate scenarios for assessing plausible effects of future climatic changes at local scales. The Providing Regional Climates for Impacts Studies (PRECIS) regional modeling system is adopted to conduct ensemble simulations in a continuous run from 1950 to 2099, driven by the boundary conditions from a HadCM3-based perturbed physics ensemble. Simulations of temperature and precipitation for the baseline period are first compared to the observed values to validate the performance of the ensemble in capturing the current climatology over Ontario. Future projections for the 2030s, 2050s, and 2080s are then analyzed to help understand plausible changes in its local climate in response to global warming. The analysis indicates that there is likely to be an obvious warming trend with time over the entire province. The increase in average temperature is likely to be varying within [2.6, 2.7]°C in the 2030s, [4.0, 4.7]°C in the 2050s, and [5.9, 7.4]°C in the 2080s. Likewise, the annual total precipitation is projected to increase by [4.5, 7.1]% in the 2030s, [4.6, 10.2]% in the 2050s, and [3.2, 17.5]% in the 2080s. Furthermore, projections of rainfall intensity–duration–frequency (IDF) curves are developed to help understand the effects of global warming on extreme precipitation events. The results suggest that there is likely to be an overall increase in the intensity of rainfall storms. Finally, a data portal named Ontario Climate Change Data Portal (CCDP) is developed to ensure decision-makers and impact researchers have easy and intuitive access to the refined regional climate change scenarios.

scholarly journals Projections of Twenty-First-Century Climate Extremes for Alaska via Dynamical Downscaling and Quantile Mapping

2017 ◽  
Vol 56 (9) ◽  
pp. 2393-2409 ◽  
Author(s):  
Rick Lader ◽  
John E. Walsh ◽  
Uma S. Bhatt ◽  
Peter A. Bieniek
Keyword(s):  
Climate Change ◽  
Dynamical Downscaling ◽  
Climate Extremes ◽  
First Century ◽  
The Arctic ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Quantile Mapping ◽  
Temperature And Precipitation ◽  
Twenty First Century

AbstractClimate change is expected to alter the frequencies and intensities of at least some types of extreme events. Although Alaska is already experiencing an amplified response to climate change, studies of extreme event occurrences have lagged those for other regions. Forced migration due to coastal erosion, failing infrastructure on thawing permafrost, more severe wildfire seasons, altered ocean chemistry, and an ever-shrinking season for snow and ice are among the most devastating effects, many of which are related to extreme climate events. This study uses regional dynamical downscaling with the Weather Research and Forecasting (WRF) Model to investigate projected twenty-first-century changes of daily maximum temperature, minimum temperature, and precipitation over Alaska. The forcing data used for the downscaling simulations include the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim; 1981–2010), Geophysical Fluid Dynamics Laboratory Climate Model, version 3 (GFDL CM3), historical (1976–2005), and GFDL CM3 representative concentration pathway 8.5 (RCP8.5; 2006–2100). Observed trends of temperature and sea ice coverage in the Arctic are large, and the present trajectory of global emissions makes a continuation of these trends plausible. The future scenario is bias adjusted using a quantile-mapping procedure. Results indicate an asymmetric warming of climate extremes; namely, cold extremes rise fastest, and the greatest changes occur in winter. Maximum 1- and 5-day precipitation amounts are projected to increase by 53% and 50%, which is larger than the corresponding increases for the contiguous United States. When compared with the historical period, the shifts in temperature and precipitation indicate unprecedented heat and rainfall across Alaska during this century.

scholarly journals Assessing runoff sensitivities to precipitation and temperature changes under global climate-change scenarios

2018 ◽  
Vol 50 (1) ◽  
pp. 24-42 ◽  
Author(s):  
Lei Chen ◽  
Jianxia Chang ◽  
Yimin Wang ◽  
Yuelu Zhu
Keyword(s):  
Climate Change ◽  
Minimum Temperature ◽  
Yellow River ◽  
Control Variable ◽  
Maximum Temperature ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Temperature Changes ◽  
Runoff Change ◽  
Precipitation And Temperature

Abstract An accurate grasp of the influence of precipitation and temperature changes on the variation in both the magnitude and temporal patterns of runoff is crucial to the prevention of floods and droughts. However, there is a general lack of understanding of the ways in which runoff sensitivities to precipitation and temperature changes are associated with the CMIP5 scenarios. This paper investigates the hydrological response to future climate change under CMIP5 RCP scenarios by using the Variable Infiltration Capacity (VIC) model and then quantitatively assesses runoff sensitivities to precipitation and temperature changes under different scenarios by using a set of simulations with the control variable method. The source region of the Yellow River (SRYR) is an ideal area to study this problem. The results demonstrated that the precipitation effect was the dominant element influencing runoff change (the degree of influence approaching 23%), followed by maximum temperature (approaching 12%). The weakest element was minimum temperature (approaching 3%), despite the fact that the increases in minimum temperature were higher than the increases in maximum temperature. The results also indicated that the degree of runoff sensitivity to precipitation and temperature changes was subject to changing external climatic conditions.

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