General Circulation Model Output Research Articles

Spatio-temporal evaluation of various global circulation models in terms of projection of different meteorological drought indices

During the coming years, a dramatic increase is expected in the number of drought events mainly due to climate change. In this study, the spatio-temporal variations of duration and intensity of drought events during two 30-year periods in the future (2040–2069 and 2070–2099) together with the reference period (1971–2000) were investigated based on the impacts of climate change. Three drought indices including the Standardized Precipitation Index, the China Z Index, and the Statistical Z Score were calculated from the dynamically downscaled precipitation data from the outputs of GFDL-ESM2M, HadGEM2-ES, and MPI-ESM-MR general circulation models (GCMs) under RCP4.5 and RCP8.5 scenarios over the Western Black Sea (WBS) and the Euphrates–Tigris (ET) basins in Turkey. The biases in the GCMs' precipitation were corrected using the linear scaling method. Additionally, the Mann–Kendall trend test was adapted to the values of drought indices to detect the trend. The results of the study showed that there is no meaningful variation between the different drought indices and also the outputs of various GCMs in terms of drought properties. On the other hand, based on the drought indices values calculated from the outputs of all GCMs, it was found that drought duration and intensity will increase during the current century. Additionally, it was concluded that by taking the spatial distribution of drought properties over the basins into the account, there is a slight relationship between the geographical elevation and drought properties over the WBS and the ET basins.

A Deep Neural Network Model of Global Topside Electron Temperature Using Incoherent Scatter Radars and Its Application to GNSS Radio Occultation

AbstractThe goal of this study is to present a new model for global topside electron temperature ( ) using a deep neural network (DNN) that is trained using measurements from incoherent scatter radars (ISRs). This study is also an investigation into whether this model can be used to generate the electron temperature in the topside ionosphere using GNSS ionospheric radio occultation (GNSS‐IRO) data as the input. ISR is one of the most reliable and long‐term sources to measure topside ionospheric electron density and plasma temperature information simultaneously. However, a drawback of ISR databases is the relatively poor spatial coverage due to the low number of ISR stations around the world. In contrast, GNSS‐IRO can be used to measure the global distributed electron density, but information is not directly detected. The relationship between the electron density and the electron temperature has been investigated by many researchers, but these studies have not explicitly considered the parameters that are known to influence the electron temperature, such as solar and geomagnetic activity level, and the features of electron density profile ( , , and scale height). This study uses a DNN technique to create a new global topside electron temperature model from three submodels that have been trained using data from three ISR stations: Arecibo (low latitude), Millstone Hill (midlatitude), and Poker Flat (high latitude). This global model is trained using electron density profile information (e.g., vertical scale height [VSH], 2, and 2) and solar and geomagnetic activity ( and , respectively) in addition to traditional spatial and temporal variables (e.g., local time, month, and latitude) as the independent variables. After the model is developed, information can be generated from the GNSS‐IRO electron density profiles using a newly created model. This model's outputs are assessed with regard to out‐of‐sample ISR data and compared to the latest International Reference Ionosphere model. It is found that the electron temperature profiles from the DNN have a root‐mean‐square deviation of 259 K in the low‐latitude region (i.e., against Arecibo data), 254 K in the midlatitude region (i.e., against Millstone Hill data), and 314 K in high‐latitude region (i.e., against Poker Flat data), and all of them are smaller than the root‐mean‐square deviation from International Reference Ionosphere. An additional comparison between the model results versus the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model outputs is also conducted. A statistical analysis of the diurnal electron temperature profiles obtained from GNSS‐IRO is shown to agree with the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model outputs. The full codes and outputs can be found at the Zenodo (10.5281/zenodo.3637617).

Selection of CMIP5 GCM Ensemble for the Projection of Spatio-Temporal Changes in Precipitation and Temperature over the Niger Delta, Nigeria

Selection of a suitable general circulation model (GCM) ensemble is crucial for effective water resource management and reliable climate studies in developing countries with constraint in human and computational resources. A careful selection of a GCM subset by excluding those with limited similarity to the observed climate from the existing pool of GCMs developed by different modeling centers at various resolutions can ease the task and minimize uncertainties. In this study, a feature selection method known as symmetrical uncertainty (SU) was employed to assess the performance of 26 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCM outputs under Representative Concentration Pathway (RCP) 4.5 and 8.5. The selection was made according to their capability to simulate observed daily precipitation (prcp), maximum and minimum temperature (Tmax and Tmin) over the historical period 1980–2005 in the Niger Delta region, which is highly vulnerable to extreme climate events. The ensemble of the four top-ranked GCMs, namely ACCESS1.3, MIROC-ESM, MIROC-ESM-CHM, and NorESM1-M, were selected for the spatio-temporal projection of prcp, Tmax, and Tmin over the study area. Results from the chosen ensemble predicted an increase in the mean annual prcp between the range of 0.26% to 3.57% under RCP4.5, and 0.7% to 4.94% under RCP 8.5 by the end of the century when compared to the base period. The study also revealed an increase in Tmax in the range of 0 to 0.4 °C under RCP4.5 and 1.25–1.79 °C under RCP8.5 during the periods 2070–2099. Tmin also revealed a significant increase of 0 to 0.52 °C under RCP4.5 and between 1.38–2.02 °C under RCP8.5, which shows that extreme events might threaten the Niger Delta due to climate change. Water resource managers in the region can use these findings for effective water resource planning, management, and adaptation measures.

Mechanisms of Long‐Term Variability and Recent Trend of Salinity Along 137°E

AbstractTo investigate the mechanisms for observed features along 137°E, such as freshening trends since the 1990s and decadal variability, the spatial pattern of salinity anomalies at σθ = 25.4 kg m−3 isopycnal surfaces and their variability were investigated based on ocean general circulation model (OGCM) outputs. While sea surface salinity (SSS) is restored to its climatology in the OGCM, the model captures recent salinity trends and meridionally coherent interannual to decadal variability at 137°E. The trend signal seems to be traced back over the eastern North Pacific (around 30°N, 160°W). Despite the limited SSS variability, the meridional shift of the outcrop line caused by sea surface temperature variation is found to determine the decadal spiciness variability subducting on the isopycnal surface at σθ = 25.4 kg m−3, that is, warm‐saltier anomalies with a southward shift and cold‐fresh anomalies with a northward shift as colder‐fresher water distributes to the north at the sea surface. Furthermore, we conducted tracer experiments with/without mesoscale eddies to examine possible roles of mesoscale eddies in the propagation of spiciness anomalies. Tracers diffuse across the mean stream line by mesoscale eddies and spread from 10°N to 25°N. However, in the tracer experiment with steady mean flow (i.e., without mesoscale eddies), the center of the tracer propagates along the mean stream line and does not diffuse across the mean stream line. This suggests that mesoscale eddies are important for the synchronized salinity trend and variability found from 10°N to 30°N.

Assessment of MC&MCMC uncertainty analysis frameworks on SWAT model by focusing on future runoff prediction in a mountainous watershed via CMIP5 models

AbstractThe river situation in a dry or semi-dry area is extremely affected by climate change and precipitation patterns. In this study, the impact of climate alteration on runoff in Kashafrood River Basin (KRB) in Iran was investigated using the Soil and Water Assessment Tool (SWAT) in historical and three future period times. The runoff was studied by MIROC-ESM and GFDL-ESM2G models as the outputs of general circulation models (GCMs) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) by two representative concentration pathway (RCP) scenarios (RCP2.6 and RCP8.5). The DiffeRential Evolution Adaptive Metropolis (DREAM-ZS) was used to calibrate the hydrological model parameters in different sub-basins. Using DREAM-ZS algorithm, realistic values were obtained for the parameters related to runoff simulation in the SWAT model. In this area, results show that runoff in GFDL-ESM2G in both RCPs (2.6 and 8.5) in comparing future periods with the historical period is increased about 232–383% and in MIROC-ESM tends to increase around 87–292%. Furthermore, GFDL-ESM2G compared to MIROC-ESM in RCP2.6 (RCP8.5) in near, intermediate, and far future periods shows that the value of runoff increases 59.6% (23.0%), 100.2% (35.1%), and 42.5% (65.3%), respectively.

Detecting the impact of climate and reservoirs on extreme floods using nonstationary frequency models

The annual maximum flood records of the Danjiangkou reservoir displayed significant decreasing trends. The upper stream of the reservoir was characterized by minor changes in urbanization in recent decades, while several large reservoirs were built since 1975. To better explain the changes in floods, this paper used the nonstationary generalized extreme value (GEV) distribution by considering the impacts of climate change and human activity. The GEV parameters were expressed as functions of time, climate indices, and climate-reservoir index (CRI). By using CRI as the covariate, the performance of the nonstationary GEV distribution was much better than others. This paper also analyzed the risk of floods using design life level and annual average reliability methods. The reliability obtained from these methods can describe the effects of external indices on floods. Finally, the uncertainty analysis indicated that a nonstationary model might not be practical, and the large confidence intervals of the design flood implied that the results were meaningless. It is essential to build a deterministic relationship between parameters and covariates, and we should be prudent when using general circulation models outputs in extreme statistics.

The effect of climate change on cholera disease: The road ahead using artificial neural network.

Climate change has been described to raise outbreaks of water-born infectious diseases and increases public health concerns. This study aimed at finding out these impacts on cholera infections by using Artificial Neural Networks (ANNs) from 2021 to 2050. Daily data for cholera infection cases in Qom city, which is located in the center of Iran, were analyzed from 1998 to 2016. To determine the best lag time and combination of inputs, Gamma Test (GT) was applied. General circulation model outputs were utilized to project future climate pattern under two scenarios of Representative Concentration Pathway (RCP2.6 and RCP8.5). Statistical downscaling was done to produce high-resolution synthetic time series weather dataset. ANNs were applied for simulating the impact of climate change on cholera. The observed climate variables including maximum and minimum temperatures and precipitation were tagged as predictors in ANNs. Cholera cases were considered as the target outcome variable. Projected future (2020–2050) climate in previous step was carried out to assess future cholera incidence. A seasonal trend in cholera infection was seen. Our results elucidated that the best lag time was 21 days. According to the results of downscaling tool, future climate in the study area by 2050 will be warmer and wetter. Simulation of cholera cases indicated that there is a clear trend of increasing cholera cases under the worst scenario (RCP8.5) by the year 2050 and the highest cholera cases observe in warmer months. The precipitation was recognized as the most effective input variable by sensitivity analysis. We observed a significant correlation between low precipitation and cholera infection. There is a strong evidence to show that cholera disease is correlated with environment variables, as low precipitation and high temperatures in warmer months could provide the swifter bacterial replication. These conditions in Iran, especially in the central parts, may raise the cholera infection rates. Furthermore, ANNs is an executive tool to simulate the impact of climate change on cholera to estimate the future trend of cholera incidence for adopting protective measures in endemic areas.

How climate scenarios alter future predictions of field-scale water and nitrogen dynamics and crop yields

Climate change scenarios are widely used for exploring future changes in environmental systems. However, many aspects of the uncertainties associated with the use of climate change scenarios in environmental systems modeling have not yet been studied sufficiently. We explore how the way that baseline scenarios are defined and general circulation model (GCM) outputs are used affects climate change impact assessments of agricultural systems. Our study builds on a previously validated agricultural systems model, the Root Zone Water Quality Model (RZWQM), coupled with the Decision Support System for Agrotechnology Transfer (DSSAT), which models a tiled-drained field in central Illinois of the United States and uses nine GCM outputs to investigate the effects. Our model simulations demonstrated the following three results. Firstly, the evaluation of climate change impacts presented a significant difference between the types of baseline used. The baseline scenario should be defined using the bias-corrected retrospective GCM outputs. Secondly, once GCM outputs are bias-corrected, the selective use of GCM outputs did not add significant value over using all available GCM outputs to provide more plausible future descriptions of agricultural systems' responses. Notably, however, selective use may have impacts comparable to carbon dioxide (CO2) emission scenarios in the field-scale agricultural climate change impact assessments. Thirdly, raw GCM outputs should be avoided for the predictions of field-scale agricultural systems’ responses to climate change. Our findings can help provide a clearer picture of how GCM outputs should be used in agricultural systems modeling and might enable us to have more plausible descriptions of how future agricultural systems might unfold.

Deriving a long-term pan evaporation reanalysis dataset for two Chinese pan types

A long-term continuous and consistent pan evaporation (Epan) reanalysis dataset will augment the analysis of Epan distributions when the observation network is discontinuous or inconsistent, and assist in the evaluation of the outputs of General Circulation Models (GCMs) and Land Surface Models (LSMs). From the 1950s to early 2000s, China had a continuous observation of the D20 pan, but this was replaced by the 601B pan across China around 2002, and thus the Epan observation network became discontinuous and inconsistent. This study developed a long-term monthly, 0.05°, continuous and consistent reanalysis dataset for both D20 and 601B pans covering mainland China throughout 1960–2014, based on meteorological data homogenization and interpolation and Epan assimilation. The PenPan-V3 model used in Epan assimilation was successfully validated by observations at 767 and 591 stations for D20 and 601B pans, respectively. Comprehensively considering the physical influence of elevation, radiation, wind speed, humidity, and air temperature, the average annual and seasonal gridded Epan reanalyses show significant spatial dependent on proximity to the ocean and latitude, consistent with previous studies. The reanalysis dataset can be used to analyze Epan distributions across China, including the areas without observations, and to estimate the representativeness of Epan to atmospheric evaporative demand. The dataset has been released in two cloud servers in China and the United States, and it will continue to be maintained and updated.

Incorporation of Long Term Climate Changes in Hydrological Modelling

One of climate change's most important concerns at the moment is its impact on hydrology as it has direct links with agriculture, vegetation, and livelihood. This study tries to analyze potential future climate change in the Kumaradhara river basin. This study involved three steps: (1) acquiring and using general circulation model (GCM) to project future global climate scenarios; (2) establishing statistical relationships between GCM data and observed data using Statistical Downscaling Model (SDSM); (3) downscaling the second generation Canadian Earth system Model (CanESM2)GCM output based on the established statistical relationship. The statistical downscaling is carried out for three scenarios used in the fifth evaluation report of the recent Intergovernmental Panel on Climate Change (IPCC) viz., Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5. The statistical downscaling Model (SDSM) results showed that the mean annual daily precipitation is altered in the basin under all the scenarios but it will be different in different time periods depending on scenarios and the basin will experience the reduced precipitation levels in summer. Also the precipitation will marginally rise in all the time slices with reference to baseline data. We can conclude from the results that this region's climate will affect future farming as the availability of water is bound to change. This study should, however, be followed up by a larger study incorporating multiple CMIP5 models such that changes in hydrological-regimes can be examined appropriately.

Using LARS –WG model for prediction of temperature in Columbia City, USA

Climate change has placed considerable pressure on the residential environment in different areas of the world. These issues have increased the motivation of researchers to analyse and forecast the changes in critical climatic factors, such as temperature, in order to offer valuable reference outcomes for management and planning in the future. This study set out to determine to what extent global warming would affect Columbia City, Missouri, USA. The Long Ashton Research Station Weather Generator (LARS-WG) model is used for downscaling daily maximum temperatures based on the SRA1B scenario. Seven General Circulation Models (GCMs) outputs are employed for three selected periods, 2011–2030, 2046–2065 and 2080-2099. The findings show that (1) statistical analysis confirmed the skill and reliability of the LARS-WG model to downscale maximum temperature time series; (2) the ensemble mean of seven GCMs exhibited an increasing based on yearly and monthly data for all periods compared with baseline period 1980-1999. The findings can contribute to a better understanding of the impacts of climate change on the urban environment and encourage planners and stakeholders to find the best solution for mitigation of these impacts.

Sensitivity of Limited Area Atmospheric Simulations to Lateral Boundary Conditions in Idealized Experiments

AbstractThe goal of limited area models (LAMs) is to downscale coarse‐gridded general circulation model output to represent small‐scale features of weather and climate. The LAM needs information from the driving coarse‐gridded model passing through its lateral boundaries. The treatment of this information transfer causes inconsistencies between driving and nested models and, subsequently, issues in regional weather and climate simulations. This work examines errors arising from choices taken by the modeler (temporal update frequency of boundary data, spatial resolution jump, and numerical lateral boundary formulation) systematically in an idealized simulation environment. So‐called Big‐Brother Experiments were performed with the LAM COSMO‐CLM (0.11° grid spacing). A baroclinic wave in a zonal channel was simulated over flat terrain with and without a Gaussian hill. The results reveal that the quality of the driving data, here represented by simulations only differing from the LAM simulations by reduced spatial resolution, dominates the performance of the nested model. Consequently, at the simulated mesoscale, the performance of the nested small‐scale model simulations is weakly sensitive to the numerical lateral boundary formulation (Davies relaxation or the newly implemented, computationally less demanding Mesinger Eta‐model formulation). The performance sensitivity to boundary update frequency and resolution jump is small when at least 6‐hourly updates and a resolution jump factor of maximally six is used. Gaussian hill LAM simulations illustrated the strength of downscaling; they can represent small‐scale features missing in the coarse‐scale driving simulations. In the idealized simulation experiments, spectral nudging is not advisable as it imprints the driving models deficits on the nested simulation.

Hydrological Responses to the Future Climate Change in a Data Scarce Region, Northwest China: Application of Machine Learning Models

Forecasting the potential hydrological response to future climate change is an effective way of assessing the adverse effects of future climate change on water resources. Data-driven models based on machine learning algorithms have great application prospects for hydrological response forecasting as they require less developmental time, minimal input, and are relatively simple compared to dynamic or physical models, especially for data scarce regions. In this study, we employed an ensemble of eight General Circulation Models (GCMs) and two artificial intelligence-based methods (Support Vector Regression, SVR, and Extreme Learning Machine, ELM) to establish the historical streamflow response to climate change and to forecast the future response under Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5 in a mountainous watershed in northwest China. We found that the artificial-intelligence-based SVR and ELM methods showed very good performances in the projection of future hydrological responses. The ensemble of GCM outputs derived very close historical hydrological hindcasts but had great uncertainty in future hydrological projections. Using the variables of GCM outputs as inputs to SVR can reduce intermediate downscaling links between variables and decrease the cumulative effect of bias in projecting future hydrological responses. Future precipitation in the study area will increase in the future under both scenarios, and this increasing trend is more significant under RCP 8.5 than under scenario 4.5. The results also indicate the streamflow change will be more sensitive to temperature (precipitation) under the RCP 8.5 (4.5) scenario. The findings and approach have important implications for hydrological response studies and the evaluation of impacts on localized regions similar to the mountainous watershed in this study.

Connecting Direct Effects of CO2 Radiative Forcing to Ocean Heat Uptake and Circulation

AbstractThe ocean's response to direct atmospheric effects of increased carbon dioxide's (CO2) radiative forcing is examined. These direct effects are defined as the climate changes that result from forcing on a fast time scale of about a year, independent of the slower surface warming that the forcing also provokes. To evaluate how these direct effects impact ocean heat uptake and circulation, output of atmospheric general circulation model (GCM) simulations are used to force an ocean GCM with comprehensive boundary conditions. Perturbation simulations with the prescribed response to a quadrupling of atmospheric CO2 include altered surface winds, freshwater fluxes, downwelling shortwave radiation, and downwelling longwave cloud radiative effect. The perturbation simulations show that the intensification and poleward shift of surface winds, particularly in the Southern Ocean, strengthen the shallow overturning circulation in the tropical Pacific and deep overturning in the Atlantic. This, in turn, has a cooling effect on the global ocean at shallow depths. A two‐layer energy balance model, designed to capture transient global mean climate change, is adapted to account for the altered ocean heat uptake from direct effects. The direct change in global mean ocean heat uptake is a decrease of about 0.3 W/m2 for quadrupling of CO2, offsetting about 5% of the surface longwave forcing.

Stepwise extreme learning machine for statistical downscaling of daily maximum and minimum temperature

Increasing temperature from climate change can bring a number of different risks such as more droughts and heat waves, and increasing sea level rise. Assessment of climate change with future scenarios is essential to adapt these impacts. To provide climate change information through the outputs of general circulation models at finer resolution, a reliable and accurate downscaling model has always been of great interest. Meeting this need, artificial neural network (ANN) has been commonly employed in downscaling for nonlinear models. Extreme learning machine (ELM), a recently developed ANN, is an efficient learning algorithm for generalized single hidden layer feedforward neural networks. In light of its simple learning algorithm, we introduced a useful approach to combine the stepwise feature selection method into ELM for temperature downscaling, as stepwise ELM (SWELM), since model complexity and computational time consumption of a traditional ANN impedes application of stepwise feature selection. This SWELM is able to identify the most influential predictors in a dataset and use them to train a nonlinear model while removing the irrelevant ones. The ELM and SWELM as well as regular ANN were tested in a simulation study. Results indicated that ELM even with randomness of weights and biases in the nodes of input and hidden layers better performed than did ANN. Also, SWELM presents a capability to select the influential predictors and remove the unrelated variables. A case study with downscaling temperature of Wisconsin, USA, showed that ELM was a comparable alternative to ANN. SWELM outperformed the ANN algorithm for temperature downscaling and sometimes predicted the temperature increase larger than did others for future scenarios. The current study of temperature downscaling with the statistical tool allows assessing the possible impacts of climate change in a local scale and some developing countries where sophisticate research cannot be eligible.

Comparison of three multi-site models in stochastic reconstruction of winter daily rainfall over Iran

Spatiotemporal modeling of daily rainfall may establish the spatial relationships between different stations which is important issue in hydro-climatology and has been addressed by various studies. The aim of this study is to compare three multi-site models, i.e. hidden Markov model (HMM), non-homogeneous hidden Markov model (NHMM), and K-nearest neighbor model (KNN) in stochastic generation of rainfall data over a network consisting of 130 synoptic stations across Iran using 20-year (1991–2010) daily dataset. Regarding the NHMM, the hidden layer parameters were considered time dependent, such that a layer including predictors was added to HMM. General circulation model (GCM) outputs could be selected as the elements of this layer or predictors. A principle component analysis (PCA) was implemented on four standardized variables of HADGEM2 model (pressure, geo-potential height, temperature, and specific humidity) for historical period on 29 grids across Iran. Accordingly, pressure variable has been selected as a predictor layer in the NHMM according to Bayesian Information criteria (BIC). Results showed that the NHMM has a significant lower BIC compared to the HMM, which confirms the time-dependence assumption of Markov transition probabilities. In stochastic generation process by NHMM and KNN models, different criteria such as seasonal mean and variance, empirical distribution of daily precipitation data in winter, probabilities of wet and dry spells in various sequences and spatial dependency have been compared. According to the results, the NHMM had a significantly better performance compared to KNN model. Nevertheless, no model was able to precisely generate rainfall based on all the evaluation criteria. Log-odds ratio as the spatial correlation criterion illustrated that both NHMM and KNN have promising results in reconstructing spatial relationships. Therefore, both models could be considered as multi-site approaches with a good skill in keeping spatiotemporally changing parameters. The findings of this study may improve the seasonal predictability of rainfall characteristics across the country.

Time Scale Decomposition of Climate and Correction of Variability Using Synthetic Samples of Stable Distributions

AbstractThe bias correction of the General Circulation Model (GCM) outputs has become a routine step that is taken in climate change impact assessments. To responsibly support the decision‐making processes, the climate‐modeling community has been debating about the conceptual requirements that bias‐correction methods should fulfill. Bearing in mind these requirements, we propose to decompose atmospheric variables into three temporal elements that represent the climate mean state, the interannual variability, and the daily variability. This decomposition is aimed at correcting the biases at one time scale without affecting the simulated climate (mean state) trend or the distributional properties at other time scales. The novelty of the proposed approach is, nevertheless, marked by the adjustment of interannual and daily variability that is made by replacing the GCM‐simulated data with synthetic samples drawn from Stable Distributions (SDs) that are fitted to the observed variability. The replacement prevents the transfer of the sampling variability of the calibration period and gives the corrected data the distributional properties of the observed climate. The employment of SDs was motivated by the fact that the climate‐change‐induced changes in the scale, the symmetry, and the frequency of extremes can be measured and applied to the SDs of the observed data. We correct the biases in the GCM‐simulated temperature and precipitation over northern South America using our proposed approach and two other existing ones. Our proposed method is capable of not only preserving the simulated climate trends but also reproducing the observed extremes as well as a more flexible method based on nonparametric distributions.

Projected changes in modified Thornthwaite climate zones over Southwest Asia using a CMIP5 multi‐model ensemble

AbstractClimate change is known as one of the key challenges of the 21st century for sustainable agricultural development over Southwest Asia. However, not much is known about the way that climate changes might affect precipitation, temperature, and climate zones as their consequence over Southwest Asia. Here we have analysed probable changes in modified Thornthwaite climate zones by using monthly temperature and precipitation data from 320 meteorological stations during the historical period of 1971–2015, as well as the future projections of 17 GCMs (General Circulation Models) outputs under the RCP4.5 and RCP8.5 scenarios in two future periods, 2041–2070 and 2071–2100. The CMIP5 climate projections were bias corrected using the correction factors derived in the historical period and applied to simulate future climatic conditions over Southwest Asia. Results derived from multi model ensemble projections of precipitation and temperature indicated that the regional mean annual temperature by the future period of 2071–2100 is expected to increase by 2.4 and 3.8°C, under RCP4.5 and RCP8.5, respectively. Also, except for the southern and eastern parts of the Arabian Peninsula, by 2071–2100, the mean annual precipitation would significantly decrease by on average 6.2 and 7.5%, under RCP4.5 and RCP8.5, respectively. Computing climate types based on averages from three climatic periods (1971–2015, 2041–2070, and 2071–2100) showed that Southwest Asia tends to become warmer and dryer. For example, by the end of the 21st century, the percentage of the area occupied by the torrid‐arid climate type (which already covers 13.8% of the total area) would be 29.3 and 36.4% under RCP4.5 and RCP8.5, respectively. These changes are important because they would definitely have profound ecological, hydrologic, and social consequences and may affect the basic components of sustainable agricultural systems like crop and livestock productions.

The development of Articulated Weather Generator model and its application in simulating future climate variability

Major areas over global tropics are vulnerable to the effects of climate change, particularly due to the high population and wide agricultural areas. The impacts of climate change have been predicted to affect higher number of hydrometeorological disasters. Over some areas, it is predicted to generate severe droughts and floods. In relation to the above issue, software called ’Articulated Weather Generator for Seasonal Climate Prediction’ (AWGenSCP) has been developed to simulate the daily weather condition for the leading 6-8 months. This paper is intended to briefly explain the internal structure of the AWGenSCP algorithms. Basically, the program is a statistical medium range prediction system which is developed based on markov chain models. In addition to the general model, an articulation scheme was developed and implemented in the systems by utilizing General Circulation Model (GCM) output. This provides ability in simulating the following next seasonal condition, particularly in term of precipitation and temperature. A sample validation of the model is presented in the paper to show the model performance.

Comparing the Performance of Dynamical and Statistical Downscaling on Historical Run Precipitation Data over a Semi-Arid Region

Precise evaluations of climate model precipitation outputs are valuable for making decisions regarding agriculture, water resource, and ecosystem management. Many downscaling techniques have been developed in the past few years for projection of weather variables. We need to apply dynamical and statistical downscaling (DD and SD) to bridge the gap between the coarse resolution general circulation model (GCM) outputs and the need for high-resolution climate information over a semi-arid region. We compare the requirements of DD (RegCM4) and SD (Delta) approaches, evaluate the historical run of NNRP1 data in comparison with station data, and analyze the changes in wet days and precipitation values through both methods during 1990–2010. In this study, we did not want to use prediction data under different scenarios of climate change, and we have just applied observed data to assess the amount of precise of NNRP1 data, over the observed period. SD method requires less time and computing power than DD. The DD approach performs better over the evaluation period according to efficiency criteria. In general, the Pearson correlation in DD with observation data in evaluation period was higher than (r > 0.72 and R2 > 0.52) SD (r > 0.65 and R2 > 0.41) over three study stations. Similarly, MAE and NSE show better results from DD relative to SD. SD underestimates the number annual mean wet-days for all three stations examined. DD overestimates a number of annual mean wet-days, but with less deviation from the observed mean.