General Circulation Models Models Research Articles

Assessment of CMIP6 GCMs for selecting a suitable climate model for precipitation projections in Southern Thailand

The selection of General Circulation Models (GCMs) is critical due to computational limitations and underlying uncertainties. This study provides a comprehensive assessment of three bias correction (BC) methods, namely the delta change method (DT), quantile mapping (QM), and empirical quantile mapping (EQM). Utilizing precipitation data from 30 observation stations across Southern Thailand, the evaluation encompasses five CMIP6 GCM models (CAMS-CSM1-0, CanESM5, CNRM-CM6-1, CNRM-ESM2-1, IPSL-CM6A-LR). Evaluation metrics, root mean square error (RMSE), mean absolute error (MAE), Pearson's correlation (r), index of agreement (d), and mean bias error (MBE) are employed to assess BC methods. Evaluation measures suggest that the DT method outperforms EQM and QM, with higher accuracy and lower errors (DT: RMSE = 3.61, MAE = 2.32; EQM: RMSE = 3.82, MAE = 3.70). Taylor diagrams show that CNRM-ESM2-1 has the highest correlation across sites (r = 0.36), albeit with a wider dispersion, while CanESM5 has a more balanced performance. Significant annual precipitation increases are projected for different spans 2021-30, 2061-70, and 2091–2100, particularly under SSP585, which will influence flood risk, water management, and climate adaptation. Future projections with SSP585 continuously projecting a larger probability of increased precipitation. The DT method is recommended for the downscaling of CMIP6 GCMs for precipitation projections in Southern Thailand, recognizing its superior performance. The study's findings provide a foundation for informed decision-making and adaptation planning in southern Thailand, urging policymakers to prioritize climate resilience and adaptation strategies while using a multi-model ensemble approach for robust climate forecasts.

SUITABILITY AREAS FOR Candidatus Liberibacter asiaticus UNDER DIFFERENT CLIMATE CHANGE SCENARIOS IN MEXICO

<p><strong>Background.</strong> Climate change models have projected an increase in the distribution of certain crop pests of economic importance by forecasting more favorable future conditions for these organisms. In citrus farming, Huanglongbing is one of the most devastating diseases worldwide, since it has caused the death of millions of trees. <strong>Objetive.</strong> The objective of this study was to estimate the current and future distribution of <em>Candidatus </em>Liberibacter asiaticus in Mexico, under the climate change scenarios SSP2 4.5 and SSP5 8.5, for the years 2050 and 2070. <strong>Methodology.</strong> Distribution models were generated with MaxEnt and R, using uncorrelated bioclimatic variables from eight General Circulation Models (GCM) derived from CMIP6 and disease presence data. <strong>Results.</strong> The results indicate that the current suitability is 44.6 %. The future distribution depended on how model predictions were pooled. An optimistic approach that considered the intersection of all models showed a small reduction of 4.1% while, considering the union of all the GCM models, the increase will vary from 12.3 to 20.1 % of the Mexican territory depending on the particular scenario and time projection. <strong>Implications.</strong> The zones of potential occurrence of <em>Candidatus </em>Liberibacter asiaticus include most of the citrus-growing areas in Mexico. <strong>Conclusion. </strong>In some regions, future scenarios show a reduction in the potential occurrence of the species in citrus plantations. However, the risk remains because its surroundings include suitable areas that can be sources of dissemination of the disease. </p>

Impacts of Climate Variability and Change on Sorghum Crop Yield in the Babile District of Eastern Ethiopia

The impacts of various climatic conditions, such as temperature and rainfall variabilities, are very critical and sensitive to rain-fed crop production, particularly over the water stress arid and semi-arid regions of Ethiopia. This study was designed to evaluate the potential impact of climate variability and change on sorghum grain yield in the Babile district of eastern Ethiopia. The study was conducted based on observed and model-based simulated projected rainfall and temperature obtained from the Ethiopian Meteorological Institute and General Circulation Models (GCM) used by the Intergovernmental Panel on Climate Change (IPCC) of the Fifth Assessment Report CMIP5) and Agricultural Model Inter-comparison and Improvement Project (AgMIP). Three GCM models, namely GFDLESM2M, CanESM2, and HadGEM2-ES under RCP4.5, were considered to generate future climate projections for the near-term 21st century. Various univariate and multivariate statistical techniques were employed to compute and identify whether the impacts of climate variability and change on rain-fed sorghum crop performance were reasonably viable over the districts where grain yield is highly stable and productive under normal climate conditions. Our findings revealed that more stable and better rainfall performance from May to September, the season when sorghum crops are normally planted in the Babile district, was positively correlated, while the maximum and minimum temperatures of the season were negatively correlated with sorghum grain yield. A significant association has been detected between sorghum grain yield and its growing period rainfall, number of rainy days, and maximum and minimum temperature with multi-regression analysis. Thus, the variability of rainfall in August, June temperature, and the number of rainy days in September significantly impacted sorghum crop productivity. As a result, the multi-regression model adjusted R-squared indicated that 77% variance in annual sorghum yield performance was explained by rainfall and temperature conditions that prevailed during the crop growing period. During the past period, there was a significant increase in sorghum yields, which are projected to decline during the near term of the 21st century in the future. This revealed that declining and disturbed rainfall performance and increases in temperature are likely to reduce overall sorghum grain yield in the Babile district. We recommend that there is a need to enhance awareness for smallholder farmers on the adverse impact of climate variability and change on sorghum grain yield. In view of this, the farmers need to be geared toward employing climate-smart agriculture as a possible adaptation measure to reduce the negative impacts of climate variability and change on rain-fed crop production practices in the Babile district and other arid and semi-arid parts of eastern Ethiopia.

A regional scale impact and uncertainty assessment of climate change in the Western Ghats in India.

The general circulation models (GCMs) and emission scenarios (RCP 4.5 and 8.5) have proven to be significantly functional in evaluating the impacts of climate change (CC) on hydrology, although their performance and accuracy varies on a regional scale. The objective of the present study is to evaluate the performance of five CMIP5 GCMs (CanESM2, BNU-ESM, CNRM-CM5, MPI-ESM-LR and MPI-ESM-MR) on a regional scale in the West Flowing River Basins-2 (WFRB-2) in India to model the impact of CC and its scenario uncertainty using reliability ensemble average (REA) method. For quantifying the results, the upper, middle and lower regions of WFRB-2 are separately analysed. The MPIMR and MPILR GCM model shows highest reliability factor range (0.3-0.6) in predicting the annual mean and annual maximum rainfall for most of the grids in the region. The GCM-simulated runoff using VIC (variable infiltration capacity) model is evaluated using statistical parameters such as root mean square error (RMSE), percentage bias (Pbias) and standard deviation (Std). The annual mean (maximum) runoff obtained using REA ensemble shows least RMSE, Pbias and Std values, i.e. 21.08%, 9.10mm and 8.9mm (6%, 39.1mm, 39.1mm), respectively for the middle region, which demonstrates higher reliability of GCM outputs in the flood-prone regions of WFRB-2. Furthermore, the future projection of annual maximum rainfall/runoff shows an increase of 50mm/15mm in the near future (2011-2040) for lower and 20mm/6mm for middle regions, which may cause flooding activities in the lower and middle region of WFRB-2.

A mini-chemical scheme with net reactions for 3D general circulation models

Context. The chemical inventory of hot Jupiter (HJ) exoplanet atmospheres continues to be observed by various ground- and space-based instruments in increasing detail and precision. It is expected for some HJs to exhibit strong non-equilibrium chemistry characteristics in their atmospheres, which might be inferred from spectral observations. Aims. We aim to model the 3D thermochemical non-equilibrium chemistry in the atmospheres of the HJs WASP-39b and HD 189733b. Methods. We coupled a lightweight, reduced chemical network ‘mini-chem’ that utilises net reaction rate tables to the Exo-FMS general circulation model (GCM). We performed GCM models of the exoplanets WASP-39b and HD 189733b as case studies of the coupled mini-chem scheme. The GCM results were then post-processed using the 3D radiative-transfer model gCMCRT to produce transmission and emission spectra to assess the impact of non-equilibrium chemistry on their observable properties. Results. Both simulations show significant departures from chemical equilibrium (CE) due to the dynamical motions of the atmosphere. The spacial distribution of species generally closely follows the dynamical features of the atmosphere rather than the temperature field. Each molecular species exhibits a different quench level in the simulations, which is also dependent on the latitude of the planet. Major differences are seen in the transmission and emission spectral features between the CE and kinetic models. Conclusions. Our simulations indicate that considering the 3D kinetic chemical structures of HJ atmospheres has an important impact on the physical interpretation of observational data. Drawing bulk atmospheric parameters from fitting feature strengths may lead to an inaccurate interpretation of chemical conditions in the atmosphere of HJs. Our open source mini-chem module is simple to couple with contemporary HJ GCM models without substantially increasing required computational resources.

Out-Performing Bias-Corrected GCM Models and CMIP6-Based Precipitation and Temperature Projections for the Bagmati Irrigation Area

The selection of General circulation models (GCMs) and suitable bias correction methods for any particular study area in very crucial for the projection of precipitation and temperature using climate models which can be used for estimating the future crop water requirement. The results of a General Circulation Model (GCM) are being downscaled and compared to a baseline climatology for two IPCC scenarios (ssp245 and ssp585) based on Coupled Model Inter-comparison Project Phase 6 (CMIP6) climate model. We choose four GCMs models out of ten by evaluating their performance to observe historical data. Performance indicators (NSE, PBAIS, and RSR) are computed by comparing bias-corrected historical data with observed historical data. We found that GCM models EC-Earth3, NorESM2-MM, GDFL-ESM4, and IPSL-CM6A-LR showed a higher rating for maximum and minimum temperature, and GCM models EC-Earth3, NorESM2-MM, GDFLESM4, and MPI-ESM2-MM showed a higher rating for precipitation. Among the different bias correction functions power Xo transformation and Power transformed functions, Bernoulli’s Weibull showed the best performance for minimum temperature), maximum temperature, and precipitation, respectively. These models and bias correction could be used to project the climate variables of the surrounding basins.

The Mantis Network II: examining the 3D high-resolution observable properties of the UHJs WASP-121b and WASP-189b through GCM modelling

ABSTRACT The atmospheres of ultra hot Jupiters (UHJs) are prime targets for the detection of molecules and atoms at both low and high spectral resolution. We study the atmospheres of the UHJs WASP-121b and WASP-189b by performing 3D general circulation models (GCMs) of these planets using high temperature correlated-k opacity schemes with ultra-violet (UV) absorbing species included. The GCM results are then post-processed at low and high spectral resolutions and compared to available data. The high resolution results are cross-correlated with molecular and atomic templates to produce mock molecular detections. Our GCM models produce similar temperature-pressure (T-p) structure trends to previous 1D radiative-convective equilibrium models of UHJs. Furthermore, the inclusion of UV opacities greatly shapes the thermal and dynamical properties of the high-altitude, low-pressure regions of the UHJ atmospheres, with sharp T-p inversions due to the absorption of UV light. This suggests that optical wavelength, high-resolution observations probe a dynamically distinct upper atmospheric region, rather than the deeper jet forming layers.

Simulating gas giant exoplanet atmospheres with Exo-FMS: comparing semigrey, picket fence, and correlated-k radiative-transfer schemes

ABSTRACT Radiative-transfer (RT) is a fundamental part of modelling exoplanet atmospheres with general circulation models (GCMs). An accurate RT scheme is required for estimates of the atmospheric energy transport and for gaining physical insight from model spectra. We implement three RT schemes for Exo-FMS: semigrey, non-grey ‘picket fence’, and real gas with correlated-k. We benchmark the Exo-FMS GCM, using these RT schemes to hot Jupiter simulation results from the literature. We perform a HD 209458b-like simulation with the three schemes and compare their results. These simulations are then post-processed to compare their observable differences. The semigrey scheme results show qualitative agreement with previous studies in line with variations seen between GCM models. The real gas model reproduces well the temperature and dynamical structures from other studies. After post-processing our non-grey picket fence scheme compares very favourably with the real gas model, producing similar transmission spectra, emission spectra, and phase curve behaviours. Exo-FMS is able to reliably reproduce the essential features of contemporary GCM models in the hot gas giant regime. Our results suggest the picket fence approach offers a simple way to improve upon RT realism beyond semigrey schemes.

An assessment of LARS-WG’s model to forecast meteorological parameters for climatic zones of cotton-harvested areas over Iran

Background and Objectives: Nowadays, climatologists and researchers are interested in the long-term forecast of climatic variables to be informed about the extent of their variations and to take measures to mitigate the adverse effects of climate change. Accordingly, general circulation models (GCMs) of atmosphere have been developed to predict climatic parameters. LARS-WG is a model to downscale the output of GCMs. It was used in the present research to generate data of daily precipitation, radiation, and minimum and maximum temperature across the cotton cultivation climatic zones (Meteorological stations) in Iran. The objective of this study is to estimate climatic factors (rainfall, radiation and minimum and maximum temperature) in the future period in major climatic cotton cultivation areas in Iran, which may be used for optimized water resource preservation and management in these regions. Also, the 5th IPCC report was used in this study, in contrast with the majority of investigations that use the 4th report. Materials and Methods: In this research, the important areas under cotton cultivation in Iran are the target area. The data of 23 synoptic stations were used in these areas. The required meteorological data recorded in the stations were daily rainfall, minimum temperature, maximum temperature and sunshine. The present study assessed the performance of five different GCM models in simulating the data of precipitation, radiation, minimum temperature, and maximum temperature in nine synoptic stations (SABZEVAR, GHOOCHAN, GHOM, HASHMABAD, LAR, BILESOWAR, EDAREGORGAN, HASANABADEDARAB, and MASHHAD) from 2011 to 2016, firstly. Finally, two GCMs (MIROC5 and GFDL-CM3) were selected for the research purpose based on the results of t-test, F-test, and the Kolmogorov-Smirnov non-parametric test. Then, the parameters were predicted by the selected GCM models for 20 years (2041-2060) under the emission scenarios of RCP4.5 and RCP8.5. Results: According to employing the scenarios of RCP4.5 and RCP8.5, the predicted solar radiation does not show a significant change in the future period versus the base period of 1981-2010 in all studied regions based on both GCM models. The highest change (0.249 MJ / m2 / day) is based on the MIROC5 model and the RCP8.5 scenario in climate zone 6102 (HASHMABAD station). The predictions revealed that the parameters of maximum and minimum temperature would be ascending for all the climatic regions over the future period. The highest variations in the average long-term annual minimum and maximum temperatures versus the base period would be 2.67 and 2.75°C happening in Climatic Region 6002 Includes stations (HASANABADEDARAB, KHAF, HAJI ABAD and GONBAD) over the 2041-2060 period under Scenario RCP8.5. For both scenarios RCP4.5 and RCP8.5, the highest temperature increase was observed in the climatic zones where located in south of Alborz and the east of Zagros mountain chains as well as the Central Iranian Dry Plateau and the lowest increasing was in the climatic regions located around the Caspian Sea, the Persian Gulf and the south of Iran. For the total annual precipitation over the future period (2041-2060), the highest amplification was predicted by MIROC5 to be 98.5 mm under Scenario RCP4.5 in Climatic Region 6202 (EDAREGORGAN station), and the greatest loss of precipitation was predicted by the same model to be -29.8 mm in Scenario RCP8.5 in Climatic Region 6102. Conclusion: Given the prediction of rising average annual minimum and maximum temperature and the decline of precipitation over the future period (2041-2060) in the climatic regions leading to the arid plain of central and southern Iran, it can be concluded that the variations of meteorological parameters induced by climate change will be significant in the cotton-growing climatic regions of Iran over the future decades.

An Efficient Statistical Approach to Develop Intensity-Duration-Frequency Curves for Precipitation and Runoff under Future Climate.

Ongoing and potential future changes in precipitation will affect water management infrastructure. Urban drainage systems are particularly vulnerable. Design standards for many stormwater practices rely on precipitation intensity-duration-frequency (IDF) curves based on extreme value analysis. General Circulation Models (GCMs) project increases in future average temperature but are less clear on changes in precipitation. In many areas, climate projections suggest relatively small changes in total precipitation volume, but also suggest increased magnitude of extreme events. Model skill in predicting extreme precipitation events, however, is limited. We develop an approach for estimating future IDF curves that is efficient, uses widely available statistically downscaled GCM output, and is consistent with published IDF curves for the United States that are often incorporated into local stormwater regulations and design guides (and are GCM model agnostic). The method provides a relatively simple way to develop scenarios in a format directly useful to assessing risk to stormwater management infrastructure. Model biases are addressed through equidistant quantile mapping, in which the modeled change in the cumulative distribution of storm events from historical to future conditions is used to adjust the extreme value fit used for IDF curve development. The approach is efficient because it requires only annual maxima and is readily automated, allowing rapid examination of results across projections. We estimate future IDF curves at locations throughout the United States and link IDF-derived design storms to a rainfall-runoff model to evaluate the potential change in storage volume requirements for capture-based stormwater management practices by 2065.

Intercomparison of Bias-Correction Data Sources and Their Influence on Watershed-Specific Downscaling Climate Projections

HighlightsBias correction from three historical data sources (NCDC, PRISM, and NEXRAD) were assessed.Bias correction removed watershed-average bias in general circulation model (GCM) data for a historical period.Subbasin-specific variability was detected in future projections for each bias-correction data source.Data source had less impact on uncertainty in future projections than GCM or a representative concentration pathway.Uncertainty from bias-correction data sources was higher for precipitation than for temperature.Abstract. Climate projections developed by general circulation models (GCM) are often used in watershed modeling applications to project future hydrologic changes. In many models, the climate projections are downscaled to individual map units represented by grid cells or subbasins. Uncertainty of downscaled climate projections are a product of uncertainties arising mainly from the model itself, from the representative concentration pathway (RCP), and from the downscaling procedure. Other sources of uncertainty may include the historical observations used for GCM bias correction and data aggregation from GCM grids to map (often subbasin) units. This study evaluated effects of three sources of historical data (ground-based weather station network, NCDC, and two gridded datasets, NEXRAD and PRISM) on historical variability, and shifts and uncertainty in precipitation and temperature projections. Climate projections from six GCMs and three RCPs were evaluated in 54 subbasins of the Smoky Hill River watershed in the U.S. Central Great Plains. Bias correction of GCM projections reduced bias of watershed-average annual precipitation in the historical period to near zero, but subbasin-specific variability remained in future projections with little difference among bias-correction data sources. For minimum and maximum temperatures, the GCM ensemble statistics for basin-average and subbasin-specific future projections were similar for all bias-correction data sources. Increase in RCP forcing was found to widen the uncertainty in future projections. Overall, the uncertainty due to data source selection was smaller than the uncertainty due to GCM model and RCP forcing selection. The results demonstrate that statistical downscaling is essential to account for local climate factors within a watershed, and that both weather station-based and gridded bias-correction data sources can be used effectively, but that future climate projections may inherit the historical bias in a selected data source. These inherent uncertainties associated with application of GCMs in hydrological and geospatial modeling should be carefully considered for understanding climate projections when building watershed models and interpreting the results. Keywords: Bias correction, Climate change, Downscaling, GCM, Uncertainty, Watershed.

RANKING OF CMIP5-BASED GENERAL CIRCULATION MODELS USING COMPROMISE PROGRAMMING AND TOPSIS FOR PRECIPITATION: A CASE STUDY OF UPPER GODAVARI BASIN, INDIA

This study determines the suitable general circulation models (GCMs) for the prediction of future precipitation of Upper Godavari sub-basin, India. Five performance indicators (PIs) namely correlation coefficient (CC), normalized root mean square deviation (NRMSD), absolute normalized mean biased deviation (ANMBD), skill score (SS), Nash Sutcliffe efficiency (NSE), and three different combinations (Case 1: all performance indicators, Case 2: CC, SS and ANMBD, and Case 3: CC, SS, and NRMSD) were considered to evaluate the performance of 38 GCM models for the study area. The observed precipitation data for 12 grid points covering the Upper Godavari sub-basin along with eight districts of Maharashtra were used for the selection of the suitable GCMs. The weights of the indicators were determined by the entropy method. Compromise programming (CP) and the technique for order preference to the similarity to ideal solution (TOPSIS) methods were used for ranking the GCMs. The group decision-making approach was employed to make a collective decision about the rank of 38 GCMS considering all the grid points. In view of all the three combinations of PIs, the study suggests that the effect of the performance indicator NSE on the ranking of GCM models is the most significant (weights for the grid points varying in the range 22.75%–78%) followed by ANMBD, CC, NRMSD, and SS. Including the maximum number of PIs and considering their combinations is found to be much helpful to enhance the credibility of the ranking of GCMs. From the group decision-making approach, it was observed that the ensemble of MPI-ESM-P, CNRM-CM5-2, and CNRM-CM5 is suitable for the prediction of precipitation for the study area.

Modeling future irrigation water demands in the context of climate change: a case study of Jayakwadi command area, India

In the present study, the downscaled future climate data from the General Circulation Model (GCM), CanESM2 has been used to calculate the monthly crop water requirements of the major crops cultivated in the Jayakwadi command area, Maharashtra, India. Statistical downscaling was carried out using the statistical downscaling model and the future irrigation demands were estimated using the CROPWAT model. Statistical downscaling of the CanESM2 GCM model and prediction of the future temperature and precipitation was done for two representative concentration pathways (RCP) scenarios namely the RCP 4.5 and RCP 8.5. Further, the future irrigation demands were estimated under the RCP 4.5 and 8.5 scenarios for the period 2011–2100 with three-time spells of 30 years centered on the 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2100). The results indicated an increase in temperature and precipitation over time spells when compared to the base period (1961–2005). The annual average temperature has been projected to increase by 0.306 °C and 0.358 °C by the 2080s when compared to the base period under the RCP 4.5 and RCP 8.5 scenarios, respectively. The annual average precipitation has been projected to increase from 856.58 mm in the base period to 1410.11 mm and 1784.06 mm under RCP 4.5 and RCP 8.5, respectively. The average reference evapotranspiration (ETo) values showed an increase from 5.41 mm/day to 5.45 mm/day, 5.53 mm/day, and 5.57 mm/day for the periods 2020s, 2050s and 2080s respectively in the RCP 8.5 scenario. The average annual irrigation demand showed a reduction of 14.07% and 14.72% for RCP 4.5 and RCP 8.5 scenarios respectively. The estimated variations in demand values can be used for optimal irrigation planning in the culturable command area of the Jayakwadi reservoir.

Statistical downscaling and projection of future temperature change for Tabriz city, Iran.

In the 21st century, Climate change has become one of the prominent global challenges which threats the world, and the changes in climate extremes are estimated to have catastrophic consequences on human society and the natural environment. To overcome the spatial-temporal inadequacy of the GCMs, Linking large-scale General Circulation Model (GCM) data with small-scale local climatic data highly comes to the fore. In this paper, two statistical downscaling techniques encompass LARS-WG and SDSM were employed for assessing the fluctuations of temperature predictand in Tabriz city, Iran. In order to choose the well-response GCMs a Multi-GCM ensemble approach was utilized by EC-EARTH, HadCM2, MIROC5, MPI-ESM GCMs from the CMIP5. To study the impact of climate change over the region, the periods of 1961-1990 and 1991-2005 were used as the baseline and validation period, respectively. Results of evaluation criteria disclosed the superior performance of Multi-GCM ensemble for predicting temperature predictand compared to single GCM models. Furthermore, the result of climate projection for the temperature predictand by both models revealed that the city will experience an increasing trend in temperatures for the horizon of 2021-2080. The average temperature will increase by 2.9 and 3.7 (°C) under Representative Concentration Pathways (RCPs) (i.e., RCP4.5 and 8.5), respectively.

Application of artificial neural network and predictor screening method for downscaling climatic parameters

In this paper, artificial neural network (ANN) was used for downscaling the outputs of general circulation models (GCMs) to evaluate changes in precipitation and mean temperature for a future period in Urmia at the north-west of Iran. MIROC-ESM-CHEM from IPCC AR5 was selected as an acceptable model based on correlation coefficient (CC) values, which is calculated between precipitation of GCM models and precipitation data prepared by Urmia Meteorological Organization for 1951-2000. As a first step, the most important parameters of the MIROC-ESM-CHEM were selected before the downscaling process by ANN in the base period (1951-2000). Afterward, the future projections of precipitation and mean temperature during 2020-2060 were applied using ANN-based simulation according to the CC method. By comparing the results, the MIROC-ESM-CHEM showed a 2.01% increase under RCP4.5 and a 0.16% decrease under RCP8.5 in annual precipitation. Also, the temperature projection outputs showed the annual mean temperature would increase in the future period in this area, and it is likely to get warmer.

Assessing the impact of climate change over the northwest of Iran: an overview of statistical downscaling methods

Due to the spatial-temporal inadequacy of large-scale general circulation models (GCMs), linking large-scale GCM data with small-scale local climatic data has found great interest. In this paper, in order to downscale minimum and maximum temperatures and precipitation predictands, the performance of three statistical downscaling techniques including Long Ashton Research Station-Weather Generator (LARS-WG), statistical downscaling model (SDSM), and artificial neural network (ANN) was compared based on Intergovernmental Panel on Climate Change-Fifth Assessment Report (IPCC-AR5) in northwest Iran. For this purpose, a nonparametric test named Mann-Whitney test, Spearman correlation coefficient, and the root mean square error (RMSE) were utilized to assess the efficiency of downscaling models. To scrutinize the climate change impacts, periods of 1961–1990 and 1991–2005 were considered as the baseline and verification periods, respectively. The findings revealed the superior performance of the ANN model for minimum and maximum temperatures, while for precipitation predictand, the SDSM represented the best performance among the models. Simulation results for future temperature indicated an ascending trend as 0.1–1.3 °C, 0.3–1.7 °C, and 0.5–2.1 °C for LARS-WG, SDSM, and ANN techniques, respectively. On the other hand, simulation outputs for the precipitation indicated a descending trend of 10–30% in future precipitation of the region according to downscaling models under Representative Concentration Pathway 8.5 (RCP8.5) pessimistic scenario of Hadley Center Coupled Model version 3 (HadCM3) GCM model.

Annual and daily ideal periods for deliquescence at the landing site of InSight based on GCM model calculations

Liquid water is one of the key elements in the search for possible life outside of the Earth and has a wide range of consequences on various chemical and geological processes. The InSight probe landed on Mars with a special equipment dedicated to examine the geophysical characteristics and internal heat flow of the planet, and some meteorological instruments were also included in the payload. We examine the annual and daily variations of near-surface relative humidity and surface temperature calculated from the General Circulation Model (GCM) at Elysium Planitia, the landing site of InSight, and search for possible ideal times for deliquescence. We inspect three different hygroscopic salts, but find that out of the three, only calcium perchlorate could liquify at the environment of InSight. We find that nighttime ideal periods could occur in a limited window between approximately Ls 90°and 150°at the late evening hours centered around 9 PM. In our daily studies we find no instances where the whole night could be ideal for deliquescence. This is mostly due to the temperatures dropping below eutectic level, leading to a 0.5 - 2 h long presumed ideal period before midnight. On multiple occasions the temperature is just a few degrees below the necessary limit while relative humidity is high enough, therefore the precise temperature measurements of InSight could be critical in determining ideal periods for deliquescence.

Comparison of multiple downscaling techniques for climate change projections given the different climatic zones in China

General circulation models (GCMs) are important tools for the study of climate change, but their resolutions are too coarse for station-scale impact assessments. Statistical and dynamical downscaling methods are widely used to translate the predictions of GCMs to the finer spatial scale and it is important to understand the difference between statistical and dynamical downscaling methods in different climatic zones and time periods. Moreover, statistical downscaling can be used on both GCM and regional climate model (RCM) outputs. In this study, two sets of GCM precipitations were dynamically and statistically downscaled and their performances were evaluated against the observed precipitation from 308 stations distributed throughout the Yellow, Yangtze, and Pearl River basins. These stations have distinct climatic characteristics from the historical period (1961–2000) and future period (2031–2050). Results suggest dynamically downscaled GCM precipitation does not present lower biases when comparing observed site-specific precipitation to GCM outputs, and biases of the initial dynamically downscaled GCM outputs decreased in areas with higher humidity. This demonstrates that statistical downscaling can improve GCM and RCM outputs, and the statistical downscaling method can reproduce local-scale precipitation satisfactorily without dynamical downscaling. However, statistical downscaling reduced spatial regularity of the biases that exist in GCM and RCM outputs between the observations and simulation. Additionally, the spatial discrepancy between statistically downscaled GCM and RCM precipitations was very small. In the future period, discrepancies between statistically downscaled RCM and GCM precipitations in the two climate scenarios were larger than the historical period for all climate zones.

Climate Change Downscaling Using Stochastic Weather Generator Model in Rift Valley Basins of Ethiopia

Agriculture is the mainstay of Ethiopian economy. Developing country like Ethiopia suffers from climate change, due to their limited economic capability to build irrigation projects to combat the trouble. This study generates climate change in rift valley basins of Ethiopia for three time periods (2020s, 2055s and 2090s) by using two emission scenarios: SRA1B and SRB1 for faster technological and environmental extreme respectively. First, outputs of 15 General Circulation Models (GCMs) under two emission scenarios (SRA1B and SRB1) are statistically downscaled by using LARS-WG software. Probability assessment of bounded range with known distributions is used to deal with the uncertainties of GCMs’ outputs. These GCMs outputs are weighted by considering the ability of each model to simulate historical records. The study result indicates that LARS-WG 5.5 version model is more uncertain to simulate future mean rainfall than generating maximum and minimum mean temperatures. GCMs weight difference for mean rainfall is 0.83 whereas weight difference for minimum and maximum mean temperatures is 0.09 among GCMs models. The study results indicate minimum and maximum temperatures absolute increase in the range of 0.34˚C to 0.58˚C, 0.94˚C to 1.8˚C and 1.42˚C to 3.2˚C and 0.32˚C to 0.56˚C, 0.91˚C to 1.8˚C and 1.34˚C to 3.04˚C respectively in the near-term (2020s), mid-term (2055s) and long-term (2090s) under both emission scenarios. The expected rainfall change percentage during these three time periods considering this GCMs weight difference into account ranges from -2.3% to 7%, 0.375% to 15.83% and 2.625% to 31.1% in the same three time periods. In conclusion, the study results indicate that in coming three time periods, maximum and minimum temperature and rainfall increase is expected in rift valley of basins of Ethiopia.

Evaluation of data-driven models to downscale rainfall parameters from global climate models outputs: the case study of Latyan watershed

Abstract Assessment of climate change in future periods is considered necessary, especially with regard to probable changes to water resources. One of the methods for estimating climate change is the use of the simulation outputs of general circulation models (GCMs). However, due to the low resolution of these models, they are not applicable to regional and local studies and downscaling methods should be applied. The purpose of the present study was to use GCM models' outputs for downscaling precipitation measurements at Amameh station in Latyan dam basin. For this purpose, the observation data from the Amameh station during the 1980–2005 period, 26 output variables from two GCM models, namely, HadCM3 and CanESM2 were used. Downscaling was performed by three data-driven methods, namely, artificial neural network (ANN), nonparametric K-nearest neighborhood (KNN) method, and adaptive network-based fuzzy inference system method (ANFIS). Comparison of the monthly results showed the superiority of KNN compared to the other two methods in simulating precipitation. However, all three, ANN, KNN, and ANFIS methods, showed satisfactory results for both HadDCM3 and CanESM2 GCM models in downscaling precipitation in the study area.