scholarly journals Changes in Climate Extremes over North Thailand, 1960–2099

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Mohammad Badrul Masud ◽  
Peeyush Soni ◽  
Sangam Shrestha ◽  
Nitin K. Tripathi
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Annual Rainfall ◽  
Trend Test ◽  
Daily Maximum ◽  
Climate Extreme ◽  
The Future ◽  
North Thailand ◽  
Extreme Indices

This study analyzes 24 climate extreme indices over North Thailand using observed data for daily maximum and minimum temperatures and total daily rainfall for the 1960–2010 period, and HadCM3 Global Climate Model (GCM) and PRECIS Regional Climate Model simulated data for the 1960–2100 period. A statistical downscaling tool is employed to downscale GCM outputs. Variations in and trends of historical and future climates are identified using the nonparametric Mann-Kendall trend test and Sen’s slope. Temperature extreme indices showed a significant rising trend during the observed period and are expected to increase significantly with an increase in summer days and tropical nights in the future. A notable decline in the number of cool days and nights is also expected in the study area while the number of warm days and nights is expected to increase. There was an insignificant decrease in total annual rainfall, number of days with rainfall more than 10 and 20 mm. However, the annual rainfall is projected to increase by 9.65% in the future 2011–2099 period compared to the observed 1960–2010 period.

scholarly journals Projected Changes in Extreme Temperature and Precipitation Indices Over CORDEX-MENA Domain

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 622
Author(s):  
Tugba Ozturk ◽  
F. Sibel Saygili-Araci ◽  
M. Levent Kurnaz
Keyword(s):  
Regional Climate Model ◽  
Radiative Forcing ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Climate Extreme ◽  
Pronounced Increase ◽  
Temperature And Precipitation ◽  
Projected Changes ◽  
Extreme Indices

In this study, projected changes in climate extreme indices defined by the Expert Team on Climate Change Detection and Indices were investigated over Middle East and North Africa. Changes in the daily maximum and minimum temperature- and precipitation- based extreme indices were analyzed for the end of the 21st century compared to the reference period 1971–2000 using regional climate model simulations. Regional climate model, RegCM4.4 was used to downscale two different global climate model outputs to 50 km resolution under RCP4.5 and RCP8.5 scenarios. Results generally indicate an intensification of temperature- and precipitation- based extreme indices with increasing radiative forcing. In particular, an increase in annual minimum of daily minimum temperatures is more pronounced over the northern part of Mediterranean Basin and tropics. High increase in warm nights and warm spell duration all over the region with a pronounced increase in tropics are projected for the period of 2071–2100 together with decrease or no change in cold extremes. According to the results, a decrease in total wet-day precipitation and increase in dry spells are expected for the end of the century.

Statistical Projection of the North Atlantic Storm Tracks

2019 ◽  
Vol 58 (7) ◽  
pp. 1509-1522 ◽  
Author(s):  
Kajsa M. Parding ◽  
Rasmus Benestad ◽  
Abdelkader Mezghani ◽  
Helene B. Erlandsen
Keyword(s):  
North Atlantic ◽  
Geopotential Height ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Storm Tracks ◽  
Cmip5 Models ◽  
The North ◽  
The Future ◽  
The North Atlantic

AbstractA method for empirical–statistical downscaling was adapted to project seasonal cyclone density over the North Atlantic Ocean. To this aim, the seasonal mean cyclone density was derived from instantaneous values of the 6-h mean sea level pressure (SLP) reanalysis fields. The cyclone density was then combined with seasonal mean reanalysis and global climate model projections of SLP or 500-hPa geopotential height to obtain future projections of the North Atlantic storm tracks. The empirical–statistical approach is computationally efficient because it makes use of seasonally aggregated cyclone statistics and allows the future cyclone density to be estimated from the full ensemble of available CMIP5 models rather than from a smaller subset. However, the projected cyclone density in the future differs considerably depending on the choice of predictor, SLP, or 500-hPa geopotential height. This discrepancy suggests that the relationship between the cyclone density and SLP, 500-hPa geopotential height, or both is nonstationary; that is, that the statistical model depends on the calibration period. A stationarity test based on 6-hourly HadGEM2-ES data indicated that the 500-hPa geopotential height was not a robust predictor of cyclone density.

scholarly journals Quantification of the Expected Changes in Annual Maximum Daily Precipitation Quantiles under Climate Change in the Iberian Peninsula

Proceedings ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 23 ◽  
Author(s):  
Carlos Garijo ◽  
Luis Mediero
Keyword(s):  
Climate Change ◽  
Iberian Peninsula ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Control Period ◽  
Regional Climate Models ◽  
The Future

Climate model projections can be used to assess the expected behaviour of extreme precipitations in the future due to climate change. The European part of the Coordinated Regional Climate Downscalling Experiment (EURO-CORDEX) provides precipitation projections for the future under various representative concentration pathways (RCPs) through regionalised Global Climate Model (GCM) outputs by a set of Regional Climate Models (RCMs). In this work, 12 combinations of GCM and RCM under two scenarios (RCP 4.5 and RCP 8.5) supplied by the EURO-CORDEX are analysed for the Iberian Peninsula. Precipitation quantiles for a set of probabilities of non-exceedance are estimated by using the Generalized Extreme Value (GEV) distribution and L-moments. Precipitation quantiles expected in the future are compared with the precipitation quantiles in the control period for each climate model. An approach based on Monte Carlo simulations is developed in order to assess the uncertainty from the climate model projections. Expected changes in the future are compared with the sampling uncertainty in the control period. Thus, statistically significant changes are identified. The higher the significance threshold, the fewer cells with significant changes are identified. Consequently, a set of maps are obtained in order to assist the decision-making process in subsequent climate change studies.

scholarly journals Climate Data to Undertake Hygrothermal and Whole Building Simulations Under Projected Climate Change Influences for 11 Canadian Cities

Data ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 72 ◽  
Author(s):  
Abhishek Gaur ◽  
Michael Lacasse ◽  
Marianne Armstrong
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Internal Variability ◽  
Climatic Conditions ◽  
Climate Data ◽  
Wide Range ◽  
The Future

Buildings and homes in Canada will be exposed to unprecedented climatic conditions in the future as a consequence of global climate change. To improve the climate resiliency of existing and new buildings, it is important to evaluate their performance over current and projected future climates. Hygrothermal and whole building simulation models, which are important tools for assessing performance, require continuous climate records at high temporal frequencies of a wide range of climate variables for input into the kinds of models that relate to solar radiation, cloud-cover, wind, humidity, rainfall, temperature, and snow-cover. In this study, climate data that can be used to assess the performance of building envelopes under current and projected future climates, concurrent with 2 °C and 3.5 °C increases in global temperatures, are generated for 11 major Canadian cities. The datasets capture the internal variability of the climate as they are comprised of 15 realizations of the future climate generated by dynamically downscaling future projections from the CanESM2 global climate model and thereafter bias-corrected with reference to observations. An assessment of the bias-corrected projections suggests, as a consequence of global warming, future increases in the temperatures and precipitation, and decreases in the snow-cover and wind-speed for all cities.

scholarly journals Future ecosystem changes in the Northeast Atlantic: a comparison between a global and a regional model system

2018 ◽  
Vol 75 (7) ◽  
pp. 2355-2369 ◽  
Author(s):  
Morten D Skogen ◽  
Solfrid S Hjøllo ◽  
Anne Britt Sandø ◽  
Jerry Tjiputra
Keyword(s):  
Climate Model ◽  
Spring Bloom ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Scale ◽  
Regional Model ◽  
Global Model ◽  
Future Climate Change ◽  
Co2 Uptake ◽  
The Future

Abstract The biogeochemistry from a global climate model (Norwegian Earth System Model) has been compared with results from a regional model (NORWECOM.E2E), where the regional model is forced by downscaled physics from the global model. The study should both be regarded as a direct comparison between a regional and its driving global model to investigate at what extent a global climate model can be used for regional studies, and a study of the future climate change in the Nordic and Barents Seas. The study concludes that the global and regional model compare well on trends, but many details are lost when a coarse resolution global model is used to assess climate impact on regional scale. The main difference between the two models is the timing of the spring bloom, and a non-exhaustive nutrient consumption in the global model in summer. The global model has a cold (in summer) and saline bias compared with climatology. This is both due to poorly resolved physical processes and oversimplified ecosystem parameterization. Through the downscaling the regional model is to some extent able to alleviate the bias in the physical fields, and the timing of the spring bloom is close to observations. The summer nutrient minimum is one month early. There is no trend in future primary production in any of the models, and the trends in modelled pH and ΩAr are also the same in both models. The largest discrepancy in the future projection is in the development of the CO2 uptake, where the regional suggests a slightly reduced uptake in the future.

scholarly journals How aerosols and greenhouse gases influence the diurnal temperature range

2020 ◽  
Author(s):  
Camilla W. Stjern ◽  
Bjørn H. Samset ◽  
Olivier Boucher ◽  
Trond Iversen ◽  
Jean-François Lamarque ◽  
...  
Keyword(s):  
Global Warming ◽  
Greenhouse Gases ◽  
Greenhouse Gas ◽  
Diurnal Temperature Range ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Diurnal Temperature ◽  
Temperature Range ◽  
The Future

Abstract. The diurnal temperature range (DTR), or difference between the maximum and minimum temperature within one day, is one of many climate parameters that affects health, agriculture and society. Understanding how DTR evolves under global warming is therefore crucial. Since physically different drivers of climate change, such as greenhouse gases and aerosols, have distinct influences on global and regional climate, predicting the future evolution of DTR requires knowledge of the effects of individual climate forcers, as well as of the future emissions mix, in particular in high emission regions. Using global climate model simulations from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP), we investigate how idealized changes in the atmospheric levels of a greenhouse gas (CO2) and aerosols (black carbon and sulfate) influence DTR, globally and in selected regions. We find broad geographical patterns of annual mean change that are similar between climate drivers, pointing to a generalized response to global warming which is not defined by the individual forcing agents. Seasonal and regional differences, however, are substantial, which highlights the potential importance of local background conditions and feedbacks. While differences in DTR responses among drivers are minor in Europe and North America, there are distinctly different DTR responses to aerosols and greenhouse gas perturbations over India and China, where present aerosol emissions are particularly high. BC induces substantial reductions in DTR, which we attribute to strong modelled BC-induced cloud responses in these regions.

scholarly journals Application of transform software for downscaling global climate model EdGCM results in north-eastern Bangladesh

2020 ◽  
Vol 26 (1) ◽  
Author(s):  
Fahmida Ishaque ◽  
Israt Jahan Ripa ◽  
Altaf Hossain ◽  
Abdur Rashid Sarker ◽  
Gazi Tamiz Uddin ◽  
...  
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Surface Air Temperature ◽  
Trend Test ◽  
P Value ◽  
Positive Trend ◽  
Geostatistical Approach ◽  
Slope Estimator ◽  
Fine Resolution

Downscaling is a state-of-the-art technique to generate fine-resolution climate change prediction and an obvious tool for forecasting future climate scenarios for many data-scarce areas like Bangladesh. The Educational Global Climate Model (EdGCM) predicts numerically and its performance was not evaluated for Bangladesh earlier. Due to this reason, an attempt has been made to apply a new geostatistical approach with the help of transform software to downscale EdGCM for identifying the trend of surface air temperature at the Sylhet district. Both Doubled_CO<sup>2</sup> and Global_Warming_01 are simulated from EdGCM and maps are generated to depict global temperature variations. Downscaling is applied to the outputs from Doubled_CO<sup>2</sup> scenario. Percent of bias (PBIAS), Nash-Sutcliffe efficiency (NSE) and the ratio of root mean square error to the standard deviation of measured data (RSR) values are satisfactory and acceptable. The trend analysis was performed using the Mann-Kendall Trend test and Sen’s slope estimator. Temperature changes are significant for both downscaled and observed results of p-value which is less than alpha = 0.05. Mann-Kendall Z tests for annual downscaled and IPCC during (2006-2020) show a positive trend. Downscaled predicted annual average temperature (simulations by Doubled_CO<sup>2</sup>) for 2020 is 21.67˚C for the Sylhet district.

Assessment of future flood intensification in Central Vietnam using a super-high-resolution climate model output

2013 ◽  
Vol 4 (4) ◽  
pp. 373-389 ◽  
Author(s):  
Do Hoai Nam ◽  
Keiko Udo ◽  
Akira Mano
Keyword(s):  
High Resolution ◽  
Bias Correction ◽  
Climate Model ◽  
Global Climate Model ◽  
Annual Rainfall ◽  
Model Output ◽  
Correction Model ◽  
Central Vietnam ◽  
The Future ◽  
Climate Model Output

This paper presents an assessment of the changes in future floods. The ranked area-average heavy daily rainfall amounts simulated by a super-high-resolution (20 km mesh) global climate model output are corrected with consideration of the effects of the topography on heavy rainfall patterns and used as a basis to model design storm hyetographs. The rainfall data are then used as the input for a nearly calibration-free parameter rainfall–runoff model to simulate floods in the future climate (2075–2099) at the Upper Thu Bon River basin in Central Vietnam. The results show that although the future mean annual rainfall will not be considerably different compared to the present-day climate (1979–2003), extreme rainfall is projected to increase vigorously, leading to a similar order of intensification of future floods. It is very likely that the flood peak with a 25-year recurrence will increase approximately 42% relative to the present-day climate. The occurrence of floods with a 10-year recurrence may exceed those with a 25-year recurrence in the present-day climate. The projection results also exhibit insignificant uncertainties caused by an artificial neural network-based bias correction model. Additionally, the presented bias correction model shows advantages over a simple climatology scaling method.

scholarly journals Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping

2012 ◽  
Vol 16 (9) ◽  
pp. 3309-3314 ◽  
Author(s):  
B. Thrasher ◽  
E. P. Maurer ◽  
C. McKellar ◽  
P. B. Duffy
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Strong Relationship ◽  
Technical Note ◽  
Daily Temperature ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Quantile Mapping

Abstract. When applying a quantile mapping-based bias correction to daily temperature extremes simulated by a global climate model (GCM), the transformed values of maximum and minimum temperatures are changed, and the diurnal temperature range (DTR) can become physically unrealistic. While causes are not thoroughly explored, there is a strong relationship between GCM biases in snow albedo feedback during snowmelt and bias correction resulting in unrealistic DTR values. We propose a technique to bias correct DTR, based on comparing observations and GCM historic simulations, and combine that with either bias correcting daily maximum temperatures and calculating daily minimum temperatures or vice versa. By basing the bias correction on a base period of 1961–1980 and validating it during a test period of 1981–1999, we show that bias correcting DTR and maximum daily temperature can produce more accurate estimations of daily temperature extremes while avoiding the pathological cases of unrealistic DTR values.

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