Artículos de revistas: "IPSL-CM6A-LR"

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Din, Salah Ud. "Flow prediction in Kabul River: An artificial intelligence based technique." International Journal of Multidisciplinary Research and Growth Evaluation 5, no. 2 (2024): 854–57. http://dx.doi.org/10.54660/.ijmrge.2024.5.2.854-857.

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This study employs random forest regressor to forecast future flow in Kabul River. Utilizing CMIP6 projected climate data for the SSP585 scenario from the IPSL-CM6A-LR climate model. The random forest regressor demonstrate efficacy in predicting flow, achieving an R2 of 0.77. The study highlights the importance of modern artificial intelligence-based techniques for precise flow and flood predictions and suggests an increase in flash flood events in Kabul River in response to a warming climate.

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Zeraatkar, Zahra, Ali Shahidi, and Hadi Memarian. "Assessment and efficiency of CMIP6 models in simulation and prediction of climatic parameters of precipitation and temperature in the Samalghan basin, Iran." Időjárás 128, no. 1 (2024): 59–74. http://dx.doi.org/10.28974/idojaras.2024.1.4.

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In the present study, four global climate models MRI-ESM2-0, IPSL-CM6A-LR, CanESM5, and GFDL-ESM4 from the set of CMIP6 models are assessed to select the best model and determine the effects of climate change on temperature and precipitation parameters under three shared socioeconomic pathway scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for the base period (1988–2017) and a future period (2020–2049) in the Samalghan basin. Statistical measures such as mean absolute error, root mean square error, mean bias error are applied to test the models, and the correlation coefficient is used to compare the results of the historical period of the models with the observational data of the selected stations. Taking the obtained results into account, the global climatic model IPSL-CM6A-LR is chosen to study the trend of temperature and precipitation changes in the future period under scenarios. The results of this study indicate an increasing trend of the average annual precipitation in the desied period compared to the base period for the SSP1-2.6 and SSP2-4.5 scenarios at all stations. Also, it increases in the SSP5-8.5 scenario for all stations except Besh Ghardash, Hesegah and Darkesh stations. The predictions of temperature show an increase in the minimum and maximum temperature values under all scenarios compared to the base period.

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Ndiaye, Cassien Diabe, Elsa Mohino, Juliette Mignot, and Dr MOHAMADOU SAIDOU SALL. "On the Detection of Externally Forced Decadal Modulations of the Sahel Rainfall over the Whole Twentieth Century in the CMIP6 Ensemble." Journal of Climate 35, no. 21 (2022): 6939–54. https://doi.org/10.1175/JCLI-D-21-0585.1.

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Abstract The Sahel semiarid region was marked during the twentieth century by significant modulations of its rainfall regime at the decadal time scale. Part of these modulations have been associated with the internal variability of the climate system, linked to changes in oceanic sea surface temperature. More recently, several studies have highlighted the influence of external forcings during the dry period in the 1980s and the recovery around the 2000s. In this work we evaluate the internally and externally driven decadal modulations of Sahel rainfall during the entire twentieth century using a set of 12 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6). We begin by proposing a physically based definition of Sahel rainfall that takes into account the southward bias in the location of the Sahelian ITCZ simulated by all the models. Our results show that the amplitude of the decadal variability, which is underestimated by most models, is mainly produced by the internally driven component. Conversely, the external forcing tends to enhance the synchrony of the simulated and observed decadal modulations in most models, providing statistically significant correlations of the historical ensemble mean with observations in 1/3 of the models, namely IPSL-CM6A-LR, INM-CM5-0, MRI-ESM2-0, and GISS-E2-1-G. Further analysis of the detection and attribution runs of the IPSL-CM6A-LR shows that anthropogenic aerosol dominate the decadal modulations of Sahel rainfall simulated by this model, suggesting that at least a part of the impact is ocean-mediated and operated through shifts in the ITCZ and the Saharan heat low.

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Andrade-Velázquez, Mercedes, and Martín José Montero-Martínez. "Statistical Downscaling of Precipitation in the South and Southeast of Mexico." Climate 11, no. 9 (2023): 186. http://dx.doi.org/10.3390/cli11090186.

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The advancements in global climate modeling achieved within the CMIP6 framework have led to notable enhancements in model performance, particularly with regard to spatial resolution. However, the persistent requirement for refined techniques, such as dynamically or statistically downscaled methods, remains evident, particularly in the context of precipitation variability. This study centered on the systematic application of a bias-correction technique (quantile mapping) to four designated CMIP6 models: CNRM-ESM2-6A, IPSL-CM6A-LR, MIROC6, and MRI-ESM2-0. The selection of these models was informed by a methodical approach grounded in previous research conducted within the southern–southeastern region of Mexico. Diverse performance evaluation metrics were employed, including root-mean-square difference (rmsd), normalized standard deviation (NSD), bias, and Pearson’s correlation (illustrated by Taylor diagrams). The study area was divided into two distinct domains: southern Mexico and the southeast region covering Tabasco and Chiapas, and the Yucatan Peninsula. The findings underscored the substantial improvement in model performance achieved through bias correction across the entire study area. The outcomes of rmsd and NSD not only exhibited variations among different climate models but also manifested sensitivity to the specific geographical region under examination. In the southern region, CNRM-ESM2-1 emerged as the most adept model following bias correction. In the southeastern domain, including only Tabasco and Chiapas, the optimal model was again CNRM-ESM2-1 after bias-correction. However, for the Yucatan Peninsula, the IPSL-CM6A-LR model yielded the most favorable results. This study emphasizes the significance of tailored bias-correction techniques in refining the performance of climate models and highlights the spatially nuanced responses of different models within the study area’s distinct geographical regions.

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Poletti, Alyssa N., Dargan M. W. Frierson, Travis Aerenson, et al. "Atmosphere and ocean energy transport in extreme warming scenarios." PLOS Climate 3, no. 2 (2024): e0000343. http://dx.doi.org/10.1371/journal.pclm.0000343.

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Extreme scenarios of global warming out to 2300 from the SSP5-8.5 extension scenario are analyzed in three state-of-the-art climate models, including two models with climate sensitivity greater than 4.5°C. The result is some of the largest warming amounts ever seen in simulations run over the historical record and into the future. The simulations exhibit between 9.3 and 17.5°C global mean temperature change between pre-Industrial and the end of the 23rd century. The extremely large changes in global temperature allow exploration of fundamental questions in climate dynamics, such as the determination of moisture and energy transports, and their relation to global atmosphere-ocean circulation. Three models performed simulations of SSP5-8.5 to 2300: MRI-ESM2-0, IPSL-CM6A-LR, and CanESM5. We analyze these simulations to improve understanding of climate dynamics, rather than as plausible futures. In the model with the most warming, CanESM5, the moisture content of the planet more than doubles, and the hydrologic cycle increases in intensity. In CanESM5 and IPSL-CM6A-LR nearly all sea ice is eliminated in both summer and winter in both hemispheres. In all three models, the Hadley circulation weakens, the tropopause height rises, and storm tracks shift poleward, to varying degrees. We analyze the moist static energy transports in the simulations using a diffusive framework. The dry static energy flux decreases to compensate for the increased moisture transport; however the compensation is imperfect. The total atmospheric transport increases but not as quickly as expected with a constant diffusivity. The decrease in eddy intensity plays an important role in determining the energy transports, as do the pattern of cloud feedbacks and the strength of ocean circulations.

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Hamid, H., S. N. Rahmat, H. Kasmin, and N. N. Tukimat. "Rainfall projection using CIMP6 models of extreme area in Johor." IOP Conference Series: Earth and Environmental Science 1347, no. 1 (2024): 012013. http://dx.doi.org/10.1088/1755-1315/1347/1/012013.

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Abstract This paper explores the impact of climate change on rainfall patterns, particularly extreme intensity, in Johor, Malaysia. The study focuses on addressing uncertainties in climate change projections by selecting suitable Global Climate Models (GCMs) based on location and topography. Four CMIP6 models (GFDL-ESM4, IPSL-CM6A-LR, MIROC6, and MRI-ESM2-0) were chosen for analysis. The research employs statistical downscaling, using historical observed data (1988-2020) and GCM output data, with a bias correction through linear scaling. The performance of the GCMs is assessed using various metrics including Root Mean Square Error (RMSE), Coefficient of Determination (R2), Percentage of Bias (Pbias), and Nash-Sutcliffe Efficiency (NSE). The IPSL-CM6A model is identified as the most suitable for rainfall projection in Johor. Under the severe climate scenario (SSP5-8.5), the analysis indicates increasing rainfall intensity in January from 2025 to 2054, notably at the Pusat Pertanian Endau station with a significant 50% increment. However, for the projected period 2055 to 2084, most stations experience a decrease in rainfall from January to June, with the Ladang Sg. Plentong station showing the largest reduction of about 40% in January. Conversely, the latter half of the year shows increased rainfall for all stations. The Mann-Kendall Test method highlights a significant decreasing trend in rainfall across all stations from 2025 to 2084 under the SSP5-8.5 scenario. This suggests that without mitigation efforts, the area will likely experience decreasing rainfall intensity due to the effects of climate change.

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Almeida, Débora de Melo, Sara Sebastiana Nogueira, Emanuel Araújo Silva, João Matheus Ferreira de Souza, Antonio Leandro Chaves Gurgel, and Alex Nascimento de Sousa. "Climate change is expected to reduce the potential distribution of Ceiba glaziovii in Caatinga, the largest area of dry tropical forest in South America." Bioscience Journal 40 (October 30, 2024): e40051. http://dx.doi.org/10.14393/bj-v40n0a2024-72663.

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Ecological niche modeling is a widely used tool to predict species distribution considering current, past, or future climate change scenarios across different geographic areas. Modeling scenarios allow researchers to assess the impacts of climate change on species distribution and identify priority areas for conservation. This study aimed to model the current and future potential distribution of Ceiba glaziovii under different climate change scenarios in Brazil. The MaxEnt algorithm was used to correlate species occurrence points with bioclimatic variables in current and future climate scenarios. Four General Circulation Models (GCMs) from CMIP6 were employed: BCC-CSM2-MR, CNRM-CM6-1, IPSL-CM6A-LR, and MIROC6, considering optimistic and pessimistic projections. The contribution of variables and model accuracy were assessed using the Jackknife statistical test and the Area Under the Curve (AUC) parameter. AUC values for current and future scenarios demonstrated high accuracy. The bioclimatic variables of precipitation and temperature were the main contributors to determining areas with higher habitat suitability. In the future climate scenario, there was a reduction in areas with good climatic suitability for all four GCMs, considering optimistic and pessimistic projections. Among the areas with high habitat suitability, the IPSL-CM6A-1 model in the optimistic projection showed the smallest reduction, while in the pessimistic scenario, all areas with high suitability disappeared. The species' climatic niche is expected to decrease under all tested climate change scenarios. The central areas of the Caatinga and its transition zones exhibit the highest climatic suitability in current and future scenarios and should be prioritized for the species' conservation.

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Üyük, Ayyüce, and Ömer K. Örücü. "Platanus orientalis L. (Doğu Çınarı) günümüz ve gelecek yayılış alanlarının CanESM5ve IPSL-CM6A-LR iklim modellerine göre karşılaştırılması." Ecological Perspective 2, no. 1 (2022): 137–50. http://dx.doi.org/10.53463/ecopers.20220146.

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Peyzaj Mimarlığı meslek disiplininin başlıca tasarım öğesi olan bitki materyalinin iklim değişikliğinden nasıl etkileneceğini analiz etmek, bu türlerin bitkilendirme çalışmalarında gelecekteki kullanımlarının planlanması açısından çok önemlidir. Çalışma için ilk olarak Platanus orientalis L. Türün günümüz koşullarındaki potansiyel yayılış alanı, türün mevcudiyet verileri ve 2,5 dakikalık (yaklaşık 20 km2) uzamsal çözünürlüğe sahip WorldClim 2.1 versiyon 19 biyoiklimsel değişkenler kullanılarak tahmin edilmektedir. Platanus orientalis L. dağıtım alanlarının iklim değişikliğinden nasıl etkilendiğini anlamak için Kanada Modeli olan CanESM5 İklim modeli kullanılarak SSP2 4.5 VE SSP5 8.5 senaryolarına ait 2041-2060 ve 2081-2100 dönemlerine ait potansiyel dağıtım alanları belirlenmiştir. Diğer taraftan türlere ait üretilen günümüz ve gelecekteki dağılım alanlarına ait değişim analizleri oluşturulmuştur. Elde edilen çalışmalara göre şimdiki zaman dağılım alanı için çok uygun ve uygun görülen alanlar 208.092 km2 olarak elde edilmiştir. Sonuç olarak Platanus orientalis L.’in dağılım alanlarının yıllara göre giderek azalacağı ileride ülkemizde türe rastlanmayacağı tahmin edilmektedir.

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Sarikaya, Ayse Gul, and Almira Uzun. "Modeling the Effects of Climate Change on the Current and Future Potential Distribution of Berberis vulgaris L. with Machine Learning." Sustainability 16, no. 3 (2024): 1230. http://dx.doi.org/10.3390/su16031230.

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Species of the Berberis genus, which are widely distributed naturally throughout the world, are cultivated and used for various purposes such as food, medicinal applications, and manufacturing dyes. Model-based machine learning is a language for specifying models, allowing the definition of a model using concise code, and enabling the automatic creation of software that implements the specified model. Maximum entropy (MaxEnt 3.4.1) is an algorithm used to model the appropriate distribution of species across geographical regions and is based on the species distribution model that is frequently also used in modeling the current and future potential distribution areas of plant species. Therefore, this study was conducted to estimate the current and future potential distribution areas of Berberis vulgaris in Türkiye for the periods 2041–2060 and 2081–2100, according to the SSP2 4.5 and SSP5 8.5 scenarios based on the IPSL-CM6A-LR climate change model. For this purpose, the coordinates obtained in the WGS 84 coordinate system were marked using the 5 m high spatial resolution Google Satellite Hybrid base maps, which are readily available in the 3.10.4 QGIS program, the current version of QGIS (Quantum GIS). The CM6A-LR climate model, the latest version of the IPSL climate models, was used to predict the species’ future distribution area. The area showed a high correlation with the points representing B. vulgaris, which is generally distributed in the Mediterranean and the central and eastern Black Sea regions of Türkiye, and the very suitable areas encompassed 45,413.82 km2. However, when the SSP2 4.5 scenario was considered for the period 2041–2060, the areas very suitable for Berberis vulgaris comprised 59,120.05 km2, and in the SSP2 4.5 scenario, very suitable areas were found to encompass 56,730.46 km2 in the 2081–2100 period. Considering the SSP5 8.5 scenario for the period 2041–2060, the area most suitable for the B. vulgaris species is 66,670.39 km2. In the SSP5 8.5 scenario, very suitable areas were found to cover 20,108.29 km2 in the 2081–2100 period. Careful consideration of both the potential positive and negative impacts of climate change is essential, and these should be regarded as opportunities to implement appropriate adaptation strategies. The necessary conditions for the continued existence and sustainability of B. vulgaris—that is, areas with ecological niche potential—have been determined.

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Ruiz-Diaz, Raquel, Mariano Koen-Alonso, Frédéric Cyr, et al. "Climate models drive variation in projections of species distribution on the Grand Banks of Newfoundland." PLOS Climate 3, no. 11 (2024): e0000520. http://dx.doi.org/10.1371/journal.pclm.0000520.

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Species Distribution Models (SDMs) are tools for understanding climate-induced habitat changes, yet their outcomes depend heavily on climate model selection. This study compares biomass projections for three key species on the Grand Banks of Newfoundland that are known to be sensitive to warming—snow crab, yellowtail flounder, and Atlantic cod. We use Earth system models (GFDL-ESM4, IPSL-CM6A-LR) and a regional ocean model system (Atlantic Climate Model (ACM)) under varying climate change emissions scenarios to assess long-term biomass trends and distributional shifts driven by future ocean warming on the Grand Banks. Projections indicate declining biomass for snow crab and yellowtail flounder with rising temperatures, whereas Atlantic cod is anticipated to exhibit biomass gains, particularly in the southern Grand Banks. Variations in biomass projections among climate models were noticeable, with IPSL forecasting the most drastic decline. ACM and GFDL biomass projections were more similar to each other than GFDL and IPSL projections, likely because ACM was downscaled from GFDL. Differences between GFDL and ACM likely arise from the coarse spatial resolution of ESMs, leading to insufficient resolution of the bathymetry and incorrect current patterns, in turn affecting the bottom temperature field. These findings underscore the important role of climate model selection in SDM-derived biomass projections. We partitioned uncertainty by source and found that the relative contribution of variability by component changes by species. As temperatures continue to rise, the urgency of implementing adaptive management strategies to minimize impacts on Newfoundland and Labrador fisheries becomes increasingly evident. SDM outputs can aid in strategic decision making, providing valuable insights for medium and long-term planning in fisheries management.

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Xiao, Heng, Yue Zhuo, Hong Sun, Kaiwen Pang, and Zhijia An. "Evaluation and Projection of Climate Change in the Second Songhua River Basin Using CMIP6 Model Simulations." Atmosphere 14, no. 9 (2023): 1429. http://dx.doi.org/10.3390/atmos14091429.

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The aim of this study is to evaluate the performance of the Global Climate Model (GCM) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in historical simulations of temperature and precipitation. The goal is to select the best performing GCMs for future projection of temperature and precipitation in the Second Songhua River Basin under multiple shared socioeconomic pathways (SSPs). Interannual variability skill (IVS) and Taylor diagrams are used to evaluate the spatiotemporal performance of GCMs against temperature and precipitation data published by the China Meteorological Science Commons during 1956–2016. In addition, five relatively independent models are selected to simulate the temperature and precipitation for 2021–2050 using Hierarchical Clustering. The selected models are CMCC-ESM2, EC-Earth3-Veg-LR, IPSL-CM6A-LR, MIROC-ES2L, and MPI-ESM1-2-HR. The projected results find that SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios show an increasing trend of future annual mean temperature and precipitation. However, for annual precipitation, there is a mixed state of increase and decrease among different models on the seasonal scale. In general, future temperature and precipitation changes still show a trend of growth and uneven distribution in the Second Songhua River Basin, which may be further accelerated by human activities.

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Raila, Shiva Nath, Raju Acharya, Sudan Ghimire, et al. "Out-Performing Bias-Corrected GCM Models and CMIP6-Based Precipitation and Temperature Projections for the Bagmati Irrigation Area." Journal of Advanced College of Engineering and Management 7, no. 01 (2022): 165–72. http://dx.doi.org/10.3126/jacem.v7i01.47342.

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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.

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Babaousmail, Hassen, Rongtao Hou, Brian Ayugi, et al. "Evaluation of the Performance of CMIP6 Models in Reproducing Rainfall Patterns over North Africa." Atmosphere 12, no. 4 (2021): 475. http://dx.doi.org/10.3390/atmos12040475.

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This study assesses the performance of historical rainfall data from the Coupled Model Intercomparison Project phase 6 (CMIP6) in reproducing the spatial and temporal rainfall variability over North Africa. Datasets from Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) are used as proxy to observational datasets to examine the capability of 15 CMIP6 models’ and their ensemble in simulating rainfall during 1951–2014. In addition, robust statistical metrics, empirical cumulative distribution function (ECDF), Taylor diagram (TD), and Taylor skill score (TSS) are utilized to assess models’ performance in reproducing annual and seasonal and monthly rainfall over the study domain. Results show that CMIP6 models satisfactorily reproduce mean annual climatology of dry/wet months. However, some models show a slight over/under estimation across dry/wet months. The models’ overall top ranking from all the performance analyses ranging from mean cycle simulation, trend analysis, inter-annual variability, ECDFs, and statistical metrics are as follows: EC-Earth3-Veg, UKESM1-0-LL, GFDL-CM4, NorESM2-LM, IPSL-CM6A-LR, and GFDL-ESM4. The mean model ensemble outperformed the individual CMIP6 models resulting in a TSS ratio (0.79). For future impact studies over the study domain, it is advisable to employ the multi-model ensemble of the best performing models.

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Smith, Christopher J., Ryan J. Kramer, and Adriana Sima. "The HadGEM3-GA7.1 radiative kernel: the importance of a well-resolved stratosphere." Earth System Science Data 12, no. 3 (2020): 2157–68. http://dx.doi.org/10.5194/essd-12-2157-2020.

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Abstract. We present top-of-atmosphere and surface radiative kernels based on the atmospheric component (GA7.1) of the HadGEM3 general circulation model developed by the UK Met Office. We show that the utility of radiative kernels for forcing adjustments in idealised CO2 perturbation experiments is greatest where there is sufficiently high resolution in the stratosphere in both the target climate model and the radiative kernel. This is because stratospheric cooling to a CO2 perturbation continues to increase with height, and low-resolution or low-top kernels or climate model output are unable to fully resolve the full stratospheric temperature adjustment. In the sixth phase of the Coupled Model Intercomparison Project (CMIP6), standard atmospheric model data are available up to 1 hPa on 19 pressure levels, which is a substantial advantage compared to CMIP5. We show in the IPSL-CM6A-LR model where a full set of climate diagnostics are available that the HadGEM3-GA7.1 kernel exhibits linear behaviour and the residual error term is small, as well as from a survey of kernels available in the literature that in general low-top radiative kernels underestimate the stratospheric temperature response. The HadGEM3-GA7.1 radiative kernels are available at https://doi.org/10.5281/zenodo.3594673 (Smith, 2019).

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Ragab, Sanad H., Shatha I. Alqurashi, Mohammad M. Aljameeli, Michael G. Tyshenko, Ahmed H. Abdelwahab, and Tharwat A. Selim. "Predicting the Global Distribution of Gryllus bimaculatus Under Climate Change: Implications for Biodiversity and Animal Feed Production." Sustainability 16, no. 23 (2024): 10278. http://dx.doi.org/10.3390/su162310278.

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The potential range and distribution of insects are greatly impacted by climate change. This study evaluates the potential global shifts in the range of Gryllus bimaculatus (Orthoptera: Gryllidae) under several climate change scenarios. The Global Biodiversity Information Facility provided the location data for G. bimaculatus, which included nineteen bioclimatic layers (bio01–bio19), elevation data from the WorldClim database, and land cover data. For the near future (2021–2040) and far future (2081–2100) under low (SSP1-2.6) and high (SSP5-8.5) emission scenarios, the Beijing Climate Center Climate System Model (BCC-CSM2-MR) and the Institute Pierre-Simon Laplace Coupled Model Intercomparison Project (IPSL-CM6A-LR) were used. Assessing habitat gain, loss, and stability for G. bimaculatus under potential scenarios was part of the evaluation analysis. The results showed that the main environmental parameters affecting the distribution of G. bimaculatus were mean temperature of the driest quarter, mean diurnal temperature range, isothermality, and seasonal precipitation. Since birds, small mammals, and other insectivorous insects rely on G. bimaculatus and other cricket species as their primary food supply, habitat loss necessitates management attention to the effects on the food web. The spread of G. bimaculatus as a sentinel species in the food chain and its use in animal feeds are both impacted by habitat loss and gain.

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Acarer, Ahmet. "Role of climate change on Oriental spruce (Picea orientalis L.): Modeling and mapping." BioResources 19, no. 2 (2024): 3845–56. http://dx.doi.org/10.15376/biores.19.2.3845-3856.

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Global climate change is a process with dramatic consequences for ecosystems, and changes that may occur in the potential distribution of plant communities especially draw attention. This study aimed to reveal the potential distribution modeling and mapping of the Oriental spruce (Picea orientalis L.), distributed in a limited area, using current and future (year 2100) climate scenarios in Turkey. The maximum entropy method for potential distribution and Chelsa V2.1 technical specification IPSL-CM6A-LR scenarios (SSP126-SSP370-SSP585) were preferred to reveal the effect of climate change. Results for the current were in the “excellent” category with training and test data AUC 0.981 and 0.977, respectively. The variables contributing to the model were the precipitation amount of the driest month, mean diurnal air temperature range, annual precipitation amount, and mean annual air temperature. Variables contributing to the current model were analysed using the SSP126, SSP370, and SSP585 scenarios of the year 2100. It was assessed that the potential distribution for 2100 decreases according to SSP126, was fragmented according to SSP370, and decreased according to the SSP585 scenario. As a result, the authors determined that the high potential distribution is reduced 61% when the current mapping of Oriental spruce is compared with the SSP585 mapping.

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Ragab, Sanad H., and Michael G. Tyshenko. "Predicting the potential worldwide distribution of Aedes aegypti under climate change scenarios." International Journal of Scientific Reports 9, no. 11 (2023): 344–52. http://dx.doi.org/10.18203/issn.2454-2156.intjscirep20233163.

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Background: Climate change is one of the most important factors associated with medically important insect pests such as mosquitoes (Diptera: Culicidae). Diseases spread by mosquitoes are increasing due to changes in global temperature and weather patterns that are altering vector host ranges allowing spread into new regions. Zika, dengue fever, chikungunya and yellow fever are arboviral infections that are spread by Aedes aegypti (Culicidae). The objective of the current research is to study the potential geographic distribution habitats of Ae. aegypti in the world under current and future climate conditions. Methods: Data of Ae. aegypti was obtained from the global biodiversity information facility and used 19 bioclimatic layers (bio01-bio19) and elevation from the WorldClim database. The scenarios used are the Beijing climate center climate system model (BCC-CSM2-MR) and the institute Pierre-Simon Laplace, coupled model intercomparison project (IPSL-CM6A-LR) with two shared socio-economic pathways (SSPs) for each of the general circulation model (GCMs): SSP126 and SSP585. Results: The results revealed that altitude, temperature, seasonality (standard deviation *100) (bio4), and annual precipitation (bio12) were the most important environmental variables that affect the distribution of Ae. aegypti. Conclusions: The models showed that Africa and South America maintained very high and excellent habitat suitability for Ae. Aegypti under the current potential distribution map.

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Casagrande, Fernanda, Ronald Buss de Souza, Paulo Nobre, and Andre Lanfer Marquez. "An inter-hemispheric seasonal comparison of polar amplification using radiative forcing of a quadrupling CO<sub>2</sub> experiment." Annales Geophysicae 38, no. 5 (2020): 1123–38. http://dx.doi.org/10.5194/angeo-38-1123-2020.

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Abstract. The numerical climate simulations from the Brazilian Earth System Model (BESM) are used here to investigate the response of the polar regions to a forced increase in CO2 (Abrupt-4×CO2) and compared with Coupled Model Intercomparison Project phase 5 (CMIP5) and 6 (CMIP6) simulations. The main objective here is to investigate the seasonality of the surface and vertical warming as well as the coupled processes underlying the polar amplification, such as changes in sea ice cover. Polar regions are described as the most climatically sensitive areas of the globe, with an enhanced warming occurring during the cold seasons. The asymmetry between the two poles is related to the thermal inertia and the coupled ocean–atmosphere processes involved. While at the northern high latitudes the amplified warming signal is associated with a positive snow– and sea ice–albedo feedback, for southern high latitudes the warming is related to a combination of ozone depletion and changes in the wind pattern. The numerical experiments conducted here demonstrated very clear evidence of seasonality in the polar amplification response as well as linkage with sea ice changes. In winter, for the northern high latitudes (southern high latitudes), the range of simulated polar warming varied from 10 to 39 K (−0.5 to 13 K). In summer, for northern high latitudes (southern high latitudes), the simulated warming varies from 0 to 23 K (0.5 to 14 K). The vertical profiles of air temperature indicated stronger warming at the surface, particularly for the Arctic region, suggesting that the albedo–sea ice feedback overlaps with the warming caused by meridional transport of heat in the atmosphere. The latitude of the maximum warming was inversely correlated with changes in the sea ice within the model's control run. Three climate models were identified as having high polar amplification for the Arctic cold season (DJF): IPSL-CM6A-LR (CMIP6), HadGEM2-ES (CMIP5) and CanESM5 (CMIP6). For the Antarctic, in the cold season (JJA), the climate models identified as having high polar amplification were IPSL-CM6A-LR (CMIP6), CanESM5(CMIP6) and FGOALS-s2 (CMIP5). The large decrease in sea ice concentration is more evident in models with great polar amplification and for the same range of latitude (75–90∘ N). Also, we found, for models with enhanced warming, expressive changes in the sea ice annual amplitude with outstanding ice-free conditions from May to December (EC-Earth3-Veg) and June to December (HadGEM2-ES). We suggest that the large bias found among models can be related to the differences in each model to represent the feedback process and also as a consequence of each distinct sea ice initial condition. The polar amplification phenomenon has been observed previously and is expected to become stronger in the coming decades. The consequences for the atmospheric and ocean circulation are still subject to intense debate in the scientific community.

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Anil, Suram, and P. Anand Raj. "Deciphering the projected changes in CMIP-6 based precipitation simulations over the Krishna River Basin." Journal of Water and Climate Change 13, no. 3 (2022): 1389–407. http://dx.doi.org/10.2166/wcc.2022.399.

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Abstract The impact of climate change on the Krishna River Basin (KRB) is significant due to the semi-arid nature of the basin. Herein, 21 global climate models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) were examined to simulate the historical monthly precipitation over the 1951–2014 period in the KRB. The symmetrical uncertainty (SU) method and the multi-criteria decision method (MCDM) were employed to select the suitable GCMs for projecting possible changes in precipitation over the KRB. The biases in the climate projections were removed by using the empirical quantile mapping method. The reliability ensemble averaging (REA) method was used to generate the multi-model ensemble (MME) mean of projections and to analyse the spatio-temporal changes of precipitation under different shared socioeconomic pathways (SSPs). BCC-CSM2-MR, IPSL-CM6A-LR, MIROC6, INM-CM5-0, and MPI-ESM1-2-HR were found to be the most suitable GCMs for the KRB. The MME mean of the chosen GCMs showed significant changes in precipitation projection that occurs for a far future period (2071–2100) over the KRB. The projection changes of precipitation range from −36.72 to 83.05% and −37.68 to 95.75% for the annual and monsoon periods, respectively, for various SSPs. Monsoon climate projections show higher changes compared with the annual climate projections, which reveals that precipitation concentration is more during the monsoon period over the KRB.

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20

Cruz-Baltuano, Ana, Raúl Huarahuara-Toma, Arlette Silva-Borda, et al. "Assessment of Observed and Projected Extreme Droughts in Perú—Case Study: Candarave, Tacna." Atmosphere 16, no. 1 (2024): 18. https://doi.org/10.3390/atmos16010018.

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Droughts have always been one of the most dangerous hazards for civilizations, especially when they impact the headwaters of a watershed, as their effects can spread downstream. In this context, observed droughts (1981–2015) and projected droughts (2016–2100) were assessed in Candarave, the headwaters of the Locumba basin. Regarding observed droughts, SPI-3 and SPEI-3 detected seven extreme droughts (1983, 1992, 1996, 1998, 2010, 2011, and 2012), with the most intense occurring in 1992 and 1998. SPI-6 and SPEI-6 identified the same extreme drought events, highlighting 1992 as the most intense. Additionally, it was concluded that the VCI also detected the droughts identified by the SPEI; however, a more detailed analysis of its use is necessary due to the limited availability of suitable satellite images in the area. On the other hand, a high-resolution dataset of climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) under the SSP3-7.0 scenario was used to project future droughts. Of the models in that dataset, CanESM5, IPSL–CM6A–LR, and UKESM1–0–LL did not perform well in the study area. SPI and SPEI projected more than ten episodes of extreme drought, indicating that extreme droughts will become more frequent, severe, and intense in the last 30 years of this century.

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21

Lézine, Anne-Marie, Maé Catrain, Julián Villamayor, and Myriam Khodri. "Using data and models to infer climate and environmental changes during the Little Ice Age in tropical West Africa." Climate of the Past 19, no. 1 (2023): 277–92. http://dx.doi.org/10.5194/cp-19-277-2023.

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Abstract. Here we present hydrological and vegetation paleo-data extracted from 28 sites in West Africa from 5∘ S to 19∘ N and the past1000/PMIP4 IPSL-CM6A-LR climate model simulations covering the 850–1850 CE period to document the environmental and climatic changes that occurred during the Little Ice Age (LIA). The comparison between paleo-data and model simulations shows a clear contrast between the area spanning the Sahel and the savannah in the north, characterized by widespread drought, and the equatorial sites in the south, where humid conditions prevailed. Particular attention was paid to the Sahel, whose climatic evolution was characterized by a progressive drying trend between 1250 and 1850 CE. Three major features are highlighted: (1) the detection of two early warning signals around 1170 and 1240 CE preceding the onset of the LIA drying trend; (2) a tipping point at 1800–1850 CE characterized by a rainfall drop and an environmental degradation in the Sahel; and (3) a succession of drying events punctuating the LIA, the major one of which was dated to around 1600 CE. The climatic long-term evolution of the Sahel is associated with a gradual southward displacement of the Inter-Tropical Convergence Zone induced by the radiative cooling impacts of major volcanic eruptions that have punctuated the last millennium.

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22

Andrade-Velázquez, Mercedes, and Martín José Montero-Martínez. "Historical and Projected Trends of the Mean Surface Temperature in South-Southeast Mexico Using ERA5 and CMIP6." Climate 11, no. 5 (2023): 111. http://dx.doi.org/10.3390/cli11050111.

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This study aimed to determine the mean temperature trends in the south-southeast region of Mexico during the historical period of 1980–2014, as well as during the future periods of 2021–2040, 2041–2060, and 2081–2100, as recommended by the IPCC. Additionally, the study sought to identify the climate change scenario that is most closely aligned with the socio-environmental conditions of the south-southeast zone of Mexico and that has the greatest impact on the region’s average temperature. The downscaling method of bias correction was conducted at a spatial resolution of 0.25° × 0.25°, and an analysis of historical trends was performed for the period 1980–2014 with ERA5 and four CMIP6 models (CNRM-ESM2-1, IPSL-CM6A-LR, MIROC6, and MRI-ESM2-0). This process was extended to future projections. The models indicated temperature differences of less than 0.5 °C with respect to ERA5, in agreement with other studies. Additionally, the current study calculated future trends for the south-southeast region using three of the CMIP6 scenarios (SSP2-4.5, SSP4-6.0, and SSP5-8.5). The z-eq proposal was used to compare the slopes, enabling us to determine which of the three scenarios corresponded to the historical trend, assuming identical socio-environmental conditions. The SSP4-6.0 scenario was found to correspond to the historical trend.

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23

Flack, David L. A., Gwendal Rivière, Ionela Musat, et al. "Representation by two climate models of the dynamical and diabatic processes involved in the development of an explosively deepening cyclone during NAWDEX." Weather and Climate Dynamics 2, no. 1 (2021): 233–53. http://dx.doi.org/10.5194/wcd-2-233-2021.

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Abstract. The dynamical and microphysical properties of a well-observed cyclone from the North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX), called the Stalactite cyclone and corresponding to intensive observation period 6, is examined using two atmospheric components (ARPEGE-Climat 6.3 and LMDZ6A) of the global climate models CNRM-CM6-1 and IPSL-CM6A, respectively. The hindcasts are performed in “weather forecast mode”, run at approximately 150–200 km (low resolution, LR) and approximately 50 km (high resolution, HR) grid spacings, and initialised during the initiation stage of the cyclone. Cyclogenesis results from the merging of two relative vorticity maxima at low levels: one associated with a diabatic Rossby vortex (DRV) and the other initiated by baroclinic interaction with a pre-existing upper-level potential vorticity (PV) cut-off. All hindcasts produce (to some extent) a DRV. However, the second vorticity maximum is almost absent in LR hindcasts because of an underestimated upper-level PV cut-off. The evolution of the cyclone is examined via the quasi-geostrophic ω equation which separates the diabatic heating component from the dynamical one. In contrast to some previous studies, there is no change in the relative importance of diabatic heating with increased resolution. The analysis shows that LMDZ6A produces stronger diabatic heating compared to ARPEGE-Climat 6.3. Hindcasts initialised during the mature stage of the cyclone are compared with airborne remote-sensing measurements. There is an underestimation of the ice water content in the model compared to the one retrieved from radar-lidar measurements. Consistent with the increased heating rate in LMDZ6A compared to ARPEGE-Climat 6.3, the sum of liquid and ice water contents is higher in LMDZ6A than ARPEGE-Climat 6.3 and, in that sense, LMDZ6A is closer to the observations. However, LMDZ6A strongly overestimates the fraction of super-cooled liquid compared to the observations by a factor of approximately 50.

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24

Zanchettin, Davide, Claudia Timmreck, Myriam Khodri, et al. "Effects of forcing differences and initial conditions on inter-model agreement in the VolMIP volc-pinatubo-full experiment." Geoscientific Model Development 15, no. 5 (2022): 2265–92. http://dx.doi.org/10.5194/gmd-15-2265-2022.

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Abstract. This paper provides initial results from a multi-model ensemble analysis based on the volc-pinatubo-full experiment performed within the Model Intercomparison Project on the climatic response to Volcanic forcing (VolMIP) as part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The volc-pinatubo-full experiment is based on an ensemble of volcanic forcing-only climate simulations with the same volcanic aerosol dataset across the participating models (the 1991–1993 Pinatubo period from the CMIP6-GloSSAC dataset). The simulations are conducted within an idealized experimental design where initial states are sampled consistently across models from the CMIP6-piControl simulation providing unperturbed preindustrial background conditions. The multi-model ensemble includes output from an initial set of six participating Earth system models (CanESM5, GISS-E2.1-G, IPSL-CM6A-LR, MIROC-E2SL, MPI-ESM1.2-LR and UKESM1). The results show overall good agreement between the different models on the global and hemispheric scales concerning the surface climate responses, thus demonstrating the overall effectiveness of VolMIP's experimental design. However, small yet significant inter-model discrepancies are found in radiative fluxes, especially in the tropics, that preliminary analyses link with minor differences in forcing implementation; model physics, notably aerosol–radiation interactions; the simulation and sampling of El Niño–Southern Oscillation (ENSO); and, possibly, the simulation of climate feedbacks operating in the tropics. We discuss the volc-pinatubo-full protocol and highlight the advantages of volcanic forcing experiments defined within a carefully designed protocol with respect to emerging modelling approaches based on large ensemble transient simulations. We identify how the VolMIP strategy could be improved in future phases of the initiative to ensure a cleaner sampling protocol with greater focus on the evolving state of ENSO in the pre-eruption period.

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25

Dunkl, István, Nicole Lovenduski, Alessio Collalti, Vivek K. Arora, Tatiana Ilyina, and Victor Brovkin. "Gross primary productivity and the predictability of CO₂: more uncertainty in what we predict than how well we predict it." Biogeosciences 20, no. 16 (2023): 3523–38. http://dx.doi.org/10.5194/bg-20-3523-2023.

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Abstract. The prediction of atmospheric CO2 concentrations is limited by the high interannual variability (IAV) in terrestrial gross primary productivity (GPP). However, there are large uncertainties in the drivers of GPP IAV among Earth system models (ESMs). Here, we evaluate the impact of these uncertainties on the predictability of atmospheric CO2 in six ESMs. We use regression analysis to determine the role of environmental drivers in (i) the patterns of GPP IAV and (ii) the predictability of GPP. There are large uncertainties in the spatial distribution of GPP IAV. Although all ESMs agree on the high IAV in the tropics, several ESMs have unique hotspots of GPP IAV. The main driver of GPP IAV is temperature in the ESMs using the Community Land Model, whereas it is soil moisture in the ESM developed by the Institute Pierre Simon Laplace (IPSL-CM6A-LR) and in the low-resolution configuration of the Max Planck Earth System Model (MPI-ESM-LR), revealing underlying differences in the source of GPP IAV among ESMs. Between 13 % and 24 % of the GPP IAV is predictable 1 year ahead, with four out of six ESMs showing values of between 19 % and 24 %. Up to 32 % of the GPP IAV induced by soil moisture is predictable, whereas only 7 % to 13 % of the GPP IAV induced by radiation is predictable. The results show that, while ESMs are fairly similar in their ability to predict their own carbon flux variability, these predicted contributions to the atmospheric CO2 variability originate from different regions and are caused by different drivers. A higher coherence in atmospheric CO2 predictability could be achieved by reducing uncertainties in the GPP sensitivity to soil moisture and by accurate observational products for GPP IAV.

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Ferreiro-Lera, Giovanni-Breogán, Ángel Penas, and Sara del Río. "Unveiling Deviations from IPCC Temperature Projections through Bayesian Downscaling and Assessment of CMIP6 General Circulation Models in a Climate-Vulnerable Region." Remote Sensing 16, no. 11 (2024): 1831. http://dx.doi.org/10.3390/rs16111831.

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The European Mediterranean Basin (Euro-Med), a region particularly vulnerable to global warming, notably lacks research aimed at assessing and enhancing the widely used remote climate detection products known as General Circulation Models (GCMs). In this study, the proficiency of GCMs in replicating reanalyzed 1981–2010 temperature data sourced from the ERA5 Land was assessed. Initially, the least data-modifying interpolation method for achieving a resolution match of 0.1° was ascertained. Subsequently, a pixel-by-pixel evaluation was conducted, employing five goodness-of-fit metrics. From these metrics, we compiled a Comprehensive Rating Index (CRI). A Multi-Model Ensemble using Random Forest was constructed and projected across three emission scenarios (SSP1-RCP2.6, SSP2-RCP4.5, and SSP5-RCP8.5) and timeframes (2026–2050, 2051–2075, and 2076–2100). Empirical Bayesian Kriging, selected for its minimal data alteration, supersedes the commonly employed Bilinear Interpolation. The evaluation results underscore MPI-ESM1-2-HR, GFDL-ESM4, CNRM-CM6-1, MRI-ESM2-0, CNRM-ESM2-1, and IPSL-CM6A-LR as top-performing models. Noteworthy geospatial disparities in model performance were observed. The projection outcomes, notably divergent from IPCC forecasts, revealed a warming trend of 1 to over 2 °C less than anticipated for spring and winter over the medium–long term, juxtaposed with heightened warming in mountainous/elevated regions. These findings could substantially refine temperature projections for the Euro-Med, facilitating the implementation of policy strategies to mitigate the effects of global warming in vulnerable regions worldwide.

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27

Simon, Amélie, Guillaume Gastineau, Claude Frankignoul, Vladimir Lapin, and Pablo Ortega. "Pacific Decadal Oscillation modulates the Arctic sea-ice loss influence on the midlatitude atmospheric circulation in winter." Weather and Climate Dynamics 3, no. 3 (2022): 845–61. http://dx.doi.org/10.5194/wcd-3-845-2022.

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Abstract. The modulation of the winter impacts of Arctic sea-ice loss by the Pacific Decadal Oscillation (PDO) is investigated in the IPSL-CM6A-LR ocean–atmosphere general circulation model. Ensembles of simulations are performed with constrained sea-ice concentration following the Polar Amplification Model Intercomparison Project (PAMIP) and initial conditions sampling warm and cold phases of the PDO. Using a general linear model, we estimate the simulated winter impact of sea-ice loss, PDO and their combined effects. On the one hand, a negative North Atlantic Oscillation (NAO)-like pattern appears in response to sea-ice loss together with a modest deepening of the Aleutian Low. On the other hand, a warm PDO phase induces a large positive Pacific–North America pattern, as well as a small negative Arctic Oscillation pattern. Both sea-ice loss and warm PDO responses are associated with a weakening of the poleward flank of the eddy-driven jet, an intensification of the subtropical jet and a weakening of the stratospheric polar vortex. These effects are partly additive; the warm PDO phase therefore enhances the response to sea-ice loss, while the cold PDO phase reduces it. However, the effects of PDO and sea-ice loss are also partly non-additive, with the interaction between both signals being slightly destructive. This results in small damping of the PDO teleconnections under sea-ice loss conditions, especially in the stratosphere. The sea-ice loss responses are compared to those obtained with the same model in atmosphere-only simulations, where sea-ice loss does not significantly alter the stratospheric polar vortex.

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28

Dufatanye Umwali, Edovia, Xi Chen, Brian Odhiambo Ayugi, et al. "Estimating the Effects of Climate Fluctuations on Precipitation and Temperature in East Africa." Atmosphere 15, no. 12 (2024): 1455. https://doi.org/10.3390/atmos15121455.

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This study evaluated the effectiveness of the NASA Earth Exchange Global Daily Downscaled models from CMIP6 experiments (hereafter; NEX-GDDP-CMIP6) in reproducing observed precipitation and temperature across East Africa (EA) from 1981 to 2014. Additionally, climate changes were estimated under various emission scenarios, namely low (SSP1-2.6), medium (SSP2-4.5), and high (SSP5-8.5) scenarios. Multiple robust statistics metrics, the Taylor diagram, and interannual variability skill (IVS) were employed to identify the best-performing models. Significant trends in future precipitation and temperature are evaluated using the Mann-Kendall and Sen’s slope estimator tests. The results highlighted IPSL-CM6A-LR, EC-Earth3, CanESM5, and INM-CM4-8 as the best-performing models for annual and March to May (MAM) precipitation and temperature respectively. By the end of this century, MAM precipitation and temperature are projected to increase by 40% and 4.5 °C, respectively, under SSP5-8.5. Conversely, a decrease in MAM precipitation and temperature of 5% and 0.8 °C was projected under SSP2-4.5 and SSP1-2.6, respectively. Long-term mean precipitation increased in all climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), with near-term MAM precipitation showing a 5% decrease in Rwanda, Burundi, Uganda, and some parts of Tanzania. Under the SSP5-8.5 scenario, temperature rise exceeded 2–6 °C in most regions across the area, with the fastest warming trend of over 6 °C observed in diverse areas. Thus, high greenhouse gas (GHG) emission scenarios can be very harmful to EA and further GHG control is needed.

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Melnikova, Irina, Philippe Ciais, Katsumasa Tanaka, et al. "Carbon cycle and climate feedbacks under CO₂ and non-CO₂ overshoot pathways." Earth System Dynamics 16, no. 1 (2025): 257–73. https://doi.org/10.5194/esd-16-257-2025.

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Abstract. Reducing emissions of non-carbon dioxide (CO2) greenhouse gases (GHGs), such as methane (CH4) and nitrous oxide (N2O), complements CO2 mitigation in limiting global warming. However, estimating carbon–climate feedback for these gases remains fraught with uncertainties, especially under overshoot scenarios. This study investigates the impact of CO2 and non-CO2 gases with nearly equal levels of effective radiative forcing on the climate and carbon cycle, using the Earth system model (ESM) IPSL-CM6A-LR. We first present a method to recalibrate methane and nitrous oxide concentrations to align with published radiative forcings, ensuring accurate model performance. Next, we carry out a series of idealised ramp-up and ramp-down concentration-driven experiments and show that, while the impacts of increasing and decreasing CO2 and non-CO2 gases on the surface climate are nearly equivalent (when their radiative forcing magnitudes are set to be the same), regional differences emerge. We further explore the carbon cycle feedbacks and demonstrate that they differ under CO2 and non-CO2 forcing. CO2 forcing leads to both carbon–climate and carbon–concentration feedbacks, whereas non-CO2 gases give rise to the carbon–climate feedback only. We introduce a framework, building on previous studies that addressed CO2 forcing, to separate the carbon–climate feedback into a temperature term and a temperature–CO2 cross-term. Our findings reveal that these feedback terms are comparable in magnitude for the global ocean. This underscores the importance of considering both terms in carbon cycle feedback framework and climate change mitigation strategies.

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30

Rivera, Paris, Eduardo Herrera, and Werner Ochoa. "Comparación de series mensuales de precipitación y temperaturas de los Modelos CMIP6 para Guatemala." Ciencia, Tecnología y Salud 9, no. 2 (2022): 132–49. http://dx.doi.org/10.36829/63cts.v9i2.1285.

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Se comparan las métricas de 37 modelos climáticos globales (GCMs, por sus siglas en inglés) de la Fase 6 del Proyecto de Intercomparación de Modelos Acoplados (CMIP6) con el objetivo de simular el clima de Guatemala del periodo de 1971 al 2014. La temperatura y precipitación mensual fue comparada con los datos de observación de la Unidad De Investigación Climática de la Universidad del este de Anglia (CRU). Se generó un ranquin de modelos basado en la menor distancia entre tres dimisiones basado en tres métricas; Coeficiente de Correlación de Pearson (CCP), Error medio cuadrático (RMSE) y Desviación estándar (DS). Este ordenamiento coincide con los mejores valores de eficiencia Nash-Sutcliffe (NSE) para temperatura y eficiencia Kling-Gupta (KGE) para la precipitación, demás se calculan las métricas; coeficiente de correlación de Spearman (CCS), errores de sesgo medio (MBE) y el absoluto medio (MAE). Para precipitación los primeros 5 modelos presentan valores KGE de entre 0.5 y 0.7, el CCP y CCS entre 0.7 a 0.8 comparados con CRU. Para temperatura los primeros 5 modelos presenta valores de NSE de entre 0.5 a 0.6, CCP y CCS de 0.8. Los modelos sobreestiman levemente la temperatura y subestiman la precipitación. Los modelos con mejor habilidad fueron CIESM para temperatura y el modelo IPSL-CM6A-LR para precipitación. Adicionalmente se compara el promedio de 66 estaciones locales con CRU, presentando un KGE 0.51, CCP 0.77 para precipitación y NSE -0.17 y un CCP 0.20 para temperatura. Finalmente, se presenta una tabla con los 10 primeros modelos para cada variable.

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Tian, Peizhi, Binyang Jian, Jianrui Li, Xitian Cai, Jiangfeng Wei, and Guo Zhang. "Land-Use-Change-Induced Cooling and Precipitation Reduction in China: Insights from CMIP6 Models." Sustainability 15, no. 16 (2023): 12191. http://dx.doi.org/10.3390/su151612191.

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In the 21st century, the effect of land use/land cover change (LULCC) on climate has become an area of active research. To explore the effects of LULCC on temperature and precipitation in China, we used outputs from the BCC-CSM2-MR, CESM2, IPSL-CM6A-LR, and UKESM1 models, which participated in the Land Use Model Intercomparison Project (LUMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Based on these models, we identified temporal variations in precipitation and near-surface air temperature (hereinafter temperature) with and without historical land use changes and their relation with LULCC in China during 1850–2014. We then determined the significant changing period (1972–2012) and revealed the relation between the spatial distribution of historical change in vegetation cover types, precipitation, and temperature. The results showed that annual historical precipitation decreased faster (132.23 mm/(1000 a) faster), while annual historical temperature increased slower (2.70 °C/(1000 a) slower) than that without LULCC during 1850–2014. LULCC not only influenced surface properties to change local precipitation and temperature distributions and mean values, but also affected other components through atmospheric circulations due to typical monsoon characteristics in China. The relative contribution of grassland change to precipitation variation was the largest, while relatively, cropland change contributed the most to temperature variation. Our study innovatively used new model outputs from LUMIP to analyze the impacts of LULCC on precipitation and temperature, which can help to guide and improve future land use management and predictions of precipitation and temperature.

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32

Zhao, Siyi, Jiankai Zhang, Chongyang Zhang, et al. "Evaluating Long-Term Variability of the Arctic Stratospheric Polar Vortex Simulated by CMIP6 Models." Remote Sensing 14, no. 19 (2022): 4701. http://dx.doi.org/10.3390/rs14194701.

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The Arctic stratospheric polar vortex is a key component of the climate system, which has significant impacts on surface temperatures in the mid-latitudes and polar regions. Therefore, understanding polar vortex variability is helpful for extended-range weather forecasting. The present study evaluates long-term changes in the position and strength of the polar vortex in the Arctic lower stratosphere during the winters from 1980/81 to 2013/14. Simulations of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models are compared with Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA2) reanalysis dataset. Overall, the CMIP6 models well capture the spatial characteristics of the polar vortex with spatial correlation coefficients between the potential vorticity (PV) in the lower stratosphere from simulations and MERRA2 products generally greater than 0.85 for all CMIP6 models during winter. There is a good agreement in the position and shape of the polar vortex between the CMIP6 multi-model mean and MERRA2, although there exist differences between simulations of individual CMIP6 models. However, most CMIP6 models underestimate the strength of polar vortex in the lower stratosphere, with the largest negative bias up to about −20%. The present study further reveals that there is an anticorrelation between the polar vortex strength bias and area bias simulated by CMIP6 models. In addition, there is a positive correlation between the trend of EP-flux divergence for wavenumber one accumulated in early winter and the trend in zonal mean zonal wind averaged in late winter. As for the long-term change in polar vortex position, CanESM5, IPSL-CM5A2-INCA, UKESM1-0-LL, and IPSL-CM6A-LR well capture the persistent shift of polar vortex towards the Eurasian continent and away from North America in February, which has been reported in observations. These models reproduce the positive trend of wavenumber-1 planetary waves since the 1980s seen in the MERRA2 dataset. This suggests that realistic wave activity processes in CMIP6 models play a key role not only in the simulation of the strength of the stratospheric polar vortex but also in the simulation of the polar vortex position shift.

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Li, Xiuping, Peiqing Xiao, Shilong Hao, and Zhihui Wang. "Rainfall Erosivity Characteristics during 1961–2100 in the Loess Plateau, China." Remote Sensing 16, no. 4 (2024): 661. http://dx.doi.org/10.3390/rs16040661.

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Rainfall erosivity, which signifies the inherent susceptibility of soil erosion induced by precipitation, plays a fundamental role in formulating a comprehensive soil loss equation (RUSLE). It stands as a crucial determinant among the foundational factors considered in a comprehensive soil loss equation’s establishment. Nonetheless, the prediction and quantification of future alterations in rainfall erosivity under the influence of global warming have been relatively limited. In this study, climate change was widely evaluated and 10 preferred global climate models in the Loess Plateau were selected by using the data sets of 27 models simulating climate change and the CN05.1 data set provided by the latest CMIP6. The monthly precipitation forecast data were obtained by using the delta downscaling method. Combined with trend analysis, significance test, and coefficient of variation, the annual rainfall erosivity during 1961–2100 under four SSP scenarios was analyzed and predicted. Among the 27 GCM models used in this paper, the most suitable climate models for simulating monthly precipitation in the Loess Plateau were CMCC-CM2-SR5, CMCC-ESM2, TaiESM1, EC-Earth3, EC-Earth-Veg-LR, INM-CM4-8, CAS-ESM2-0, EC-Earth-Veg, ACCESS-ESM1-5, and IPSL-CM6A-LR. In comparison to the base period (1961–1990), during the historical period (1961–2014), the average annual rainfall erosivity on the Loess Plateau amounted to 1259.64 MJ·mm·hm−2·h−1·a−1, showing an insignificant downward trend. In the northwest of Ningxia, Yulin City and Yanan City showed a significant upward trend. In the future period (2015–2100), the annual rainfall erosivity continues to constantly change and increase. The potential average increase in rainfall erosivity is about 13.48–25.86%. In terms of spatial distribution, most areas showed an increasing trend. Among these regions, the majority of encompassed areas within Shanxi Province, central Shaanxi, and Inner Mongolia increased greatly, which was not conducive to soil and water conservation and ecological environment construction. This study offers a scientific reference for the projected future erosivity characteristics of the Loess Plateau.

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Li, Hui, Hongxu Mu, Shengqi Jian, and Xinan Li. "Assessment of Rainfall and Temperature Trends in the Yellow River Basin, China from 2023 to 2100." Water 16, no. 10 (2024): 1441. http://dx.doi.org/10.3390/w16101441.

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China’s Yellow River Basin (YRB) is sensitive to climate change due to its delicate ecosystem and complex geography. Water scarcity, soil erosion, and desertification are major challenges. To mitigate the YRB’s ecological difficulties, climate change must be predicted. Based on the analysis of the evolution features of hydro-meteorological elements, the CMIP6 climate model dataset with Delta downscaling and the Empirical Orthogonal Function (EOF) is utilized to quantitatively explore the future variations in precipitation and temperature in the YRB. The following results are drawn: The spatial resolution of the CMIP6 climate model is less than 0.5° × 0.5° (i.e., about 55 km × 55 km), which is improved to 1 km × 1 km by the downscaling of Delta and has outstanding applicability to precipitation and temperature in the YRB. The most accurate models for monthly mean temperature are CESM2-WACCM, NorESM2-LM, and ACCESS-CM2, and for precipitation are ACCESS-ESM1-5, CESM2-WACCM, and IPSL-CM6A-LR. Between 2023 and 2100, annual precipitation increases by 6.89, 5.31, 7.02, and 10.18 mm/10a under the ssp126, ssp245, ssp370, and ssp585 climate scenarios, respectively, with considerable variability in precipitation in the YRB. The annual temperature shows a significant upward trend, and the change rates under the different climate scenarios are, respectively, 0.1 °C/10a, 0.3 °C/10a, 0.5 °C/10a, and 0.7 °C/10a. The increase is positively correlated with emission intensity. Based on the EOF analysis, temperature and precipitation mainly exhibit a consistent regional trend from 2023 to 2100, with the primary modal EOF1 of precipitation for each scenario exhibiting a clear spatial distribution in the southeast–northwest.

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35

Silvy, Yona, Clément Rousset, Eric Guilyardi, et al. "A modeling framework to understand historical and projected ocean climate change in large coupled ensembles." Geoscientific Model Development 15, no. 20 (2022): 7683–713. http://dx.doi.org/10.5194/gmd-15-7683-2022.

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Abstract. The ocean responds to climate change through modifications of heat, freshwater and momentum fluxes at its boundaries. Disentangling the specific role of each of these contributors in shaping the changes of the thermohaline structure of the ocean is central for our process understanding of climate change and requires the design of specific numerical experiments. While it has been partly addressed by modeling studies using idealized CO2 forcings, the time evolution of these individual contributions during historical and projected climate change is however lacking. Here, we propose a novel modeling framework to isolate these contributions in coupled climate models for which large ensembles of historical and scenario simulations are available. The first step consists in reproducing a coupled pre-industrial control simulation with an ocean-only configuration, forced by prescribed fluxes at its interface, diagnosed from the coupled model. In a second step, we extract the external forcing perturbations from the historical+scenario ensemble of coupled simulations, and we add them to the prescribed fluxes of the ocean-only configuration. We then successfully replicate the ocean's response to historical and projected climate change in the coupled model during 1850–2100. In a third step, this full response is decomposed in sensitivity experiments in which the forcing perturbations are applied individually to the heat, freshwater and momentum fluxes. Passive tracers of temperature and salinity are implemented to discriminate the addition of heat and freshwater flux anomalies from the redistribution of pre-industrial heat and salt content in response to ocean circulation changes. Here, we first present this general framework and then apply it to the IPSL-CM6A-LR model and its ocean component NEMO3.6. This framework brings new opportunities to precisely explore the mechanisms driving historical and projected ocean changes within single climate models.

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36

Prathom, Chotirose, and Paskorn Champrasert. "General Circulation Model Downscaling Using Interpolation—Machine Learning Model Combination—Case Study: Thailand." Sustainability 15, no. 12 (2023): 9668. http://dx.doi.org/10.3390/su15129668.

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Climate change, a global problem, is now impacting human life and nature in many sectors. To reduce the severity of the impacts, General Circulation Models (GCMs) are used for predicting future climate. The prediction output of a GCM requires a downscaling process to increase its spatial resolution before projecting on local area. In order to downscale the output to a higher spatial resolution (less than 20 km), a statistical method is typically considered. By using this method, a large amount of historical observed data, up to 30 years, is essential. In some areas, the historical data is insufficient. Hence, the statistical method may not be suitable to downscale the output on the area which lacks the required data. Hence, this research aims to explore a high spatial resolution downscaling process that is able to provide a valid and high accuracy result in the Thailand area with a limitation in quantity of historical data. In this research, a combination of an interpolation and machine learning model called `IDW-ANN’ is proposed for downscaling the data under the condition. The prediction of temperature and precipitation from a GCM, IPSL-CM6A-LR in CMIP6 is downscaled by the proposed combination into a 1 km spatial resolution. After the performance evaluation, the IDW-ANN downscaling process showed good accuracy (RMSE, MAE, and R2) and valid downscaled results. The future climate situation in Thailand, in particular temperature, and precipitation level, in 2040 and 2100 under two scenarios of SSPs (SSP1-2.6 and SSP3-7.0) is also projected at 1 km resolution by using IDW-ANN. From the projection, the level of precipitation sums, and temperature seem to be increased in most of Thailand in all future scenarios.

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37

Sicard, Marie, Masa Kageyama, Sylvie Charbit, Pascale Braconnot, and Jean-Baptiste Madeleine. "An energy budget approach to understand the Arctic warming during the Last Interglacial." Climate of the Past 18, no. 3 (2022): 607–29. http://dx.doi.org/10.5194/cp-18-607-2022.

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Abstract. The Last Interglacial period (129–116 ka) is characterised by a strong orbital forcing which leads to a different seasonal and latitudinal distribution of insolation compared to the pre-industrial period. In particular, these changes amplify the seasonality of the insolation in the high latitudes of the Northern Hemisphere. Here, we investigate the Arctic climate response to this forcing by comparing the CMIP6 lig127k and piControl simulations performed with the IPSL-CM6A-LR (the global climate model developed at Institut Pierre-Simon Laplace) model. Using an energy budget framework, we analyse the interactions between the atmosphere, ocean, sea ice and continents. In summer, the insolation anomaly reaches its maximum and causes a rise in near-surface air temperature of 3.1 ∘C over the Arctic region. This warming is primarily due to a strong positive anomaly of surface downwelling shortwave radiation over continental surfaces, followed by large heat transfer from the continents to the atmosphere. The surface layers of the Arctic Ocean also receive more energy but in smaller quantity than the continents due to a cloud negative feedback. Furthermore, while heat exchange from the continental surfaces towards the atmosphere is strengthened, the ocean absorbs and stores the heat excess due to a decline in sea ice cover. However, the maximum near-surface air temperature anomaly does not peak in summer like insolation but occurs in autumn with a temperature increase of 4.2 ∘C relative to the pre-industrial period. This strong warming is driven by a positive anomaly of longwave radiation over the Arctic Ocean enhanced by a positive cloud feedback. It is also favoured by the summer and autumn Arctic sea ice retreat (-1.9×106 and -3.4×106 km2, respectively), which exposes the warm oceanic surface and thus allows oceanic heat storage and release of water vapour in summer. This study highlights the crucial role of sea ice cover variations, Arctic Ocean, as well as changes in polar cloud optical properties on the Last Interglacial Arctic warming.

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38

Liu, Tao, Zhenjiang Si, Yan Liu, Longfei Wang, Yusu Zhao, and Jing Wang. "Runoff and Drought Responses to Land Use Change and CMIP6 Climate Projections." Water 17, no. 11 (2025): 1696. https://doi.org/10.3390/w17111696.

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Climate and land use changes significantly affect runoff and hydrological drought, presenting challenges for water resource management. This study focuses on the Naoli River Basin, utilizing the SWAT model integrated with PLUS land use projections under the CMIP6 SSP245 and SSP585 scenarios to assess trends in runoff and drought characteristics from 2025 to 2100. The Standardized Runoff Index (SRI) and run theory are applied to analyze drought frequency and duration. Key findings include the following: (1) Under the SSP585 scenario (2061–2100), land use changes—specifically, a reduction in cropland and an increase in forest cover—resulted in a 12.59% decrease in runoff compared to the baseline period (1970–2014), with notable differences when considering climate-only scenarios. (2) The SSP585 scenario exhibits a significant rise in drought frequency and duration, particularly during summer, whereas SSP245 shows milder trends. (3) Based on the Taylor plot evaluation, the ensemble average MMM-Best (r = 0.80, RMSE = 26.15) has been identified as the optimal prediction model for the 2025–2100 period. Deviation analysis revealed that NorESM2-MM and IPSL-CM6A-LR demonstrated the greatest stability, while EC-Earth3 exhibited the largest deviation and highest uncertainty. (4) Land use changes under the SSP245 scenario help mitigate drought by enhancing water retention, although their effectiveness diminishes under SSP585 due to the dominant influence of climate factors, including increased temperature and precipitation variability. And (5) SRI-3 mutation analysis indicated that the mutation point occurred in July 2074 under the SSP245 scenario and in April 2060 under the SSP585 scenario (p < 0.05). The trend for SSP245 revealed significant fluctuations, with the number of crossover points rising to 40 following land use changes; conversely, the SSP585 trend remained stable with only seven crossover points, as high-emission scenarios predominantly influenced early mutations. These findings illuminate the interactive effects of land use and climate change, providing a scientific foundation for optimizing water resource management and developing effective drought mitigation strategies.

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39

Beridze, Berika, Katarzyna Sękiewicz, Łukasz Walas, et al. "Niche modelling suggests low feasibility of assisted gene flow for a Neogene relict tree, Castanea sativa Mill." Dendrobiology 90 (October 11, 2023): 58–75. http://dx.doi.org/10.12657/denbio.090.005.

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Abstract: As many tree species populations are being degraded by climate change, adaptive conservation, and forest management, such as assisted gene flow (AGF), can provide the genetic variation needed to adapt to climate change. The core of this strategy is to assist the adaptation process in populations at risk of climate maladaptation by introducing individuals with beneficial alleles to cope with expected climate changes. Castanea sativa Mill. (sweet chestnut) is an essential component of natural forests in the Mediterranean and Caucasian regions, with a long history of cultivation. Current climate change may seriously threaten the long-term persistence of the species, particularly in the Caucasus region, where the largest range reductions are predicted. Here, we used Species Distribution Models (SDMs) to assess the feasibility of AGF in European and Caucasian populations of Castanea sativa. Bioclimatic variables for present (1981–2010) and future (2071–2100) conditions were obtained from the CHELSA climate database. The final models of future species ranges were averaged across three climate models (IPSL-CM6A-LR, MPIESM1-2-HR and UKESM1-0-L) and three climate change scenarios – SSP1-2.6, SSP3-7.0 and SSP5-8.5. There are marked differences in the climatic niches of the Iberian, Alpine-Apennine, Balkan, and Caucasian populations, with significant implications for AGF. The most suitable European areas for the Caucasian populations were found only in the Adriatic region. The Iberian populations were not compatible with the predicted future climate in the Caucasus in any of the scenarios tested. Suitable areas for Alpine-Apennine populations within the AGF strategy were predicted in the Colchic lowlands, the eastern Pontic mountains and the Hyrcanian forests in the SSP1-2.6 and SSP3-7.0 climate change scenarios. In contrast, the Balkan populations would be compatible at most with the western Pontic mountains and, to a lesser extent, with the Hyrcanian forests. According to the most damaging climate scenario SSP5-8.5, the potential of AGF in the Caucasus with Alpine-Apennine and Balkan populations could be very limited. Our study showed limited applicability of AGF for Castanea sativa between the European and Caucasian populations due to low climate match. Genomic modelling is needed to fully assess the feasibility of this strategy in the species.

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40

Caillet, Justine, Nicolas C. Jourdain, Pierre Mathiot, et al. "Uncertainty in the projected Antarctic contribution to sea level due to internal climate variability." Earth System Dynamics 16, no. 1 (2025): 293–315. https://doi.org/10.5194/esd-16-293-2025.

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Abstract. Identifying and quantifying irreducible and reducible uncertainties in the Antarctic Ice Sheet (AIS) response to future climate change is essential for guiding mitigation and adaptation policy decision. However, the impact of the irreducible internal climate variability, resulting from processes intrinsic to the climate system, remains poorly understood and quantified. Here, we characterise both the atmospheric and oceanic internal climate variability in a selection of three Coupled Model Intercomparison Project Phase 6 (CMIP6) models (UKESM1-0-LL, IPSL-CM6A-LR, and MPI-ESM1.2-HR) and estimate their impact on the Antarctic contribution to sea-level change over the 21st century under the SSP2-4.5 scenario. To achieve this, we use a standalone ice-sheet model driven by the ocean through parameterised basal melting and by the atmosphere through emulated surface mass balance estimates. The atmospheric component of internal climate variability in Antarctica has a similar amplitude in the three CMIP6 models. In contrast, the amplitude of the oceanic component strongly depends on the climate model and its representation of convective mixing in the ocean. A low bias in sea-ice production and an overly stratified ocean lead to a lack of deep convective mixing which results in weak ocean variability near the entrance of ice-shelf cavities. Internal climate variability affects the Antarctic contribution to sea-level change until 2100 by 45 % to 93 % depending on the CMIP6 model. This may be a low estimate, as the internal climate variability in the CMIP models is likely underestimated. The effect of atmospheric internal climate variability on the surface mass balance overwhelms the effect of oceanic internal climate variability on the dynamical ice-sheet mass loss by a factor of 2 to 5, except in the Dronning Maud area and the Amundsen, Getz, and Aurora basins, where both contributions may be similar depending on the CMIP model. Based on these results, we recommend that ice-sheet model projections consider (i) several climate models and several members of a single climate model to account for the impact of internal climate variability and (ii) a longer temporal period when correcting historical climate forcing to match present-day observations.

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41

Li, Yan, Bo Huang, Chunping Tan, Xia Zhang, Francesco Cherubini, and Henning W. Rust. "Investigating the global and regional response of drought to idealized deforestation using multiple global climate models." Hydrology and Earth System Sciences 29, no. 6 (2025): 1637–58. https://doi.org/10.5194/hess-29-1637-2025.

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Abstract. Land use change, particularly deforestation, significantly influences the global climate system. While various studies have explored how deforestation affects temperature and precipitation, its impact on drought remains less explored. Understanding these effects across different climate zones and timescales is crucial for crafting effective land use policies aimed at mitigating climate change. This study investigates how changes in forest cover affect drought across different timescales and climate zones using simulated deforestation scenarios, where forests are converted to grasslands. The study utilizes data from nine global climate models, including BCC-CSM2-MR, CMCC-ESM2, CNRM-ESM2-1, CanESM5, EC-Earth3-Veg, GISS-E2-1-G, IPSL-CM6A-LR, MIROC-ES2L, and UKESM1-0-LL, which contribute to the Land Use Model Intercomparison Project (LUMIP). Drought effects are assessed by examining the Standardized Precipitation Evapotranspiration Index (SPEI) in the idealized global deforestation experiment (deforest-global) using the pre-industrial control simulation (piControl) as the reference. At the 3-month scale (SPEI03), global SPEI responses to deforestation are negative overall, indicating increased dryness conditions, particularly in tropical regions, while causing wetter conditions in dry regions. The multi-model ensemble mean (MME) of SPEI03 is -0.19±0.05 (mean ± standard deviation) in tropical regions and 0.07±0.05 in dry regions. The impact on drought conditions becomes more significant over longer timescales. In tropical regions, the MME of SPEI at the 24-month scale is -0.39±0.07, while it is 0.19±0.08 in dry regions, highlighting the lasting effects of deforestation on drought conditions. Seasonal responses of SPEI03 to deforestation are more pronounced during autumn and winter, with especially significant effects observed in tropical and northern polar regions. For the MME of SPEI03, the values in tropical regions are -0.24±0.08 and -0.18±0.07, while, in northern polar regions, they are -0.16±0.07 and -0.20±0.08, respectively. Continental zones experience significant seasonal changes, becoming drier in winter and wetter in summer due to global deforestation, while the Northern Hemisphere's dry regions see increased wetter conditions, particularly in autumn. Deforestation alters surface albedo by changing surface land cover structure, which affects the surface energy and water balance by modifying net solar radiation, evapotranspiration, and precipitation patterns. These changes affect water deficits, leading to varying drought responses to deforestation. The findings deepen our understanding of the relationship between vegetation change and climate change, offering valuable insights for better resource management and mitigation strategies against future climate change impacts.

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42

Li, Zhenjie, Hui Tao, Heike Hartmann, Buda Su, Yanjun Wang, and Tong Jiang. "Variation of Projected Atmospheric Water Vapor in Central Asia Using Multi-Models from CMIP6." Atmosphere 11, no. 9 (2020): 909. http://dx.doi.org/10.3390/atmos11090909.

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Using data from the Integrated Global Radiosonde Archive Version 2 (IGRA2) and the Multi Model Ensemble (MME) of four global climate models (GCMs), named CanESM5, IPSL-CM6A-LR, MIROC6, and MRI-ESM2-0, within the framework of phase 6 of the Coupled Model Intercomparison Project (CMIP6), we analyzed the changes in atmospheric total column water vapor (TCWV) over Central Asia in the future (2021–2100) under SSP-RCPs scenarios: SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, and SSP5-8.5, relative to baseline period (1986–2005). Results showed that the annual mean TCWV from IGRA2 was consistent with the model output from 1979 to 2014 in Central Asia. Besides, the spatial distribution of TCWV in Central Asia during the baseline period was consistent between the models. The regional average value of Central Asia was between 10.8 mm and 12.4 mm, and decreased with elevation. TCWV will increase under different SSP-RCPs from 2021 to 2040, but showed different trends after 2040. It will increase under SSP1-1.9 and SSP1-2.6 scenarios from 2021 to 2050, and decrease after that. It will grow from 2021 to 2055 under SSP4-3.4 scenario, and then stay essentially constant. Under SSP2-4.5 and SSP4-6.0 scenarios, TCWV will rise rapidly during 2021–2065, but the growth will decline from 2065 to 2100. TCWV will continue to increase under SSP3-7.0 and SSP5-8.5 scenarios, and the largest increase is projected under SSP5-8.5 scenario. Change in near-surface temperature (Ts) matched the change in TCWV, but changes in precipitation and evapotranspiration are not significant during 2021–2100. In spite of the large variations in TCWV under different SSP-RCPs, the dominant characteristic in all scenarios shows that a large TCWV increase is demonstrated over areas with small TCWV amounts during the baseline period. On the contrary, increases will be small where the TCWV amounts had been large during the baseline period. The change in TCWV is highly correlated to the increase in Ts in Central Asia. Under SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, and SSP5-8.5 scenarios, the higher the temperature due to higher radiative forcing, the steeper the regression slope between TCWV and Ts change. It is closest to the theoretical value of the Clausius-Clapeyron equation under SSP3-7.0 and SSP5-8.5 scenarios, but not presented under other scenarios. Spatially, steeper regression slopes during 2021–2100 have been found around the Caspian Sea in the southwest and in the high-elevation areas in the southeast of Central Asia, which is likely related to the abundant local water supply for evaporation.

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Mignot, Juliette, Frédéric Hourdin, Julie Deshayes, et al. "The Tuning Strategy of IPSL‐CM6A‐LR." Journal of Advances in Modeling Earth Systems 13, no. 5 (2021). http://dx.doi.org/10.1029/2020ms002340.

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44

Lézine, Anne-Marie, Maé Catrain, Julián Villamayor, and Myriam Khodri. "Using data and model to infer climate and environmental changes during the Little Ice Age in tropical West Africa." August 17, 2022. https://doi.org/10.5281/zenodo.7003853.

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Datasets and processed datasets of the publication  "Using data and model to infer climate and environmental changes during the Little Ice Age in tropical West Africa": IPSL-CM6A-LR model rainfall (pr) raw dataset for the past millennium (3 members) over the 10ºS-35ºN and 50ºW-50ºE region, pre-processed IPSL-CM6A-LR model rainfall and pre-processed pollen and hydroclimate proxies datasets.

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45

Boucher, Olivier, Jérôme Servonnat, Anna Lea Albright, et al. "Presentation and Evaluation of the IPSL‐CM6A‐LR Climate Model." Journal of Advances in Modeling Earth Systems 12, no. 7 (2020). http://dx.doi.org/10.1029/2019ms002010.

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46

UZUN, Almira, and Ömer K. ÖRÜCÜ. "Modeling of potential distribution areas of Spartium junceum L. (Spanish broom) under the impact of global climate change." Ağaç ve Orman, December 11, 2023. http://dx.doi.org/10.59751/agacorman.1383004.

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İklimin canlılar üzerindeki yaşamsal etkileri ve bu etkilerin sebepleri yaşamın varlığı boyunca bilinmekte ve araştırılmaya devam etmektedir. İklim değişikliğinin bitkiler üzerinde de birçok farklı etkisi bulunmakta ve çoğu zaman da bu etkiler olumsuz sonuçlar doğurmaktadır. Bu çalışmada, parlak sarı çiçekleri ile dikkat çeken ve özellikle toprak tutma kabiliyeti olan Spartium junceum L.’nin günümüz yayılış alanı ve gelecekte iklim değişikliği etkisi altında potansiyel yayılış alanları MaxEnt algoritması ile modellenmiştir. Modelde, örnek noktalar ve biyoklimatik değişkenlerle birlikte IPSL CM6A-LR iklim değişikliği modelindeki SSP2 4.5 ve SSP5 8.5 senaryolarının 2041-2060 (~2050) ve 2081-2100 (~2090) periyotları kullanılmıştır. Çalışmada model çıktılarına göre Katırtırnağı’nın günümüzdeki tahmini potansiyel uygun ve çok uygun yayılış alanlarının tahmini olarak 52270 km2 olduğu görülmüştür. IPSL CM6A-LR iklim değişimi modeline göre ise gelecekte yayılış alanlarında ciddi kayıplar yaşayacağı ve SSP5 8.5 senaryosu 2081-2100 periyotlarında çok uygun yayılış alanlarının sadece 17 km2 olarak kalacağı, yani bir çok açıdan ekonomik ve ekolojik değere sahip bu türün neslinin ülkemiz koşullarında tehlikeye gireceği görülmektedir.

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47

Langehaug, Helene Reinertsen, Hanne Sagen, A. Stallemo, et al. "Constraining CMIP6 estimates of Arctic Ocean temperature and salinity in 2025-2055." Frontiers in Marine Science 10 (October 24, 2023). http://dx.doi.org/10.3389/fmars.2023.1211562.

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Global climate models (CMIP6 models) are the basis for future predictions and projections, but these models typically have large biases in their mean state of the Arctic Ocean. Considering a transect across the Arctic Ocean, with a focus on the depths between 100-700m, we show that the model spread for temperature and salinity anomalies increases significantly during the period 2025-2045. The maximum model spread is reached in the period 2045-2055 with a standard deviation 10 times higher than in 1993-2010. The CMIP6 models agree that there will be warming, but do not agree on the degree of warming. This aspect is important for long-term management of societal and ecological perspectives in the Arctic region. We therefore test a new approach to find models with good performance. We assess how CMIP6 models represent the horizontal patterns of temperature and salinity in the period 1993-2010. Based on this, we find four models with relatively good performance (MPI-ESM1-2-HR, IPSL-CM6A-LR, CESM2-WACCM, MRI-ESM2-0). For a more robust model evaluation, we consider additional metrics (e.g., climate sensitivity, ocean heat transport) and also compare our results with other recent CMIP6 studies in the Arctic Ocean. Based on this, we find that two of the models have an overall better performance (MPI-ESM1-2-HR, IPSL-CM6A-LR). Considering projected changes for temperature for the period 2045-2055 in the high end ssp585 scenario, these two models show a similar warming in the Mid Layer (300-700m; 1.1-1.5°C). However, in the low end ssp126 scenario, IPSL-CM6A-LR shows a considerably higher warming than MPI-ESM1-2-HR. In contrast to the projected warming by both models, the projected salinity changes for the period 2045-2055 are very different; MPI-ESM1-2-HR shows a freshening in the Upper Layer (100-300m), whereas IPSL-CM6A-LR shows a salinification in this layer. This is the case for both scenarios. The source of the model spread appears to be in the Eurasian Basin, where warm waters enter the Arctic. Finally, we recommend being cautious when using the CMIP6 ensemble to assess the future Arctic Ocean, because of the large spread both in performance and the extent of future changes.

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CONSTRAIN, archive. "Journal Article: Presentation and Evaluation of the IPSL‐CM6A‐LR Climate Model." May 28, 2020. https://doi.org/10.1029/2019MS002010.

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Abstract: This study presents the global climate model IPSL‐CM6A‐LR developed at Institut Pierre‐Simon Laplace (IPSL) to study natural climate variability and climate response to natural and anthropogenic forcings as part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). This article describes the different model components, their coupling, and the simulated climate in comparison to previous model versions. We focus here on the representation of the physical climate along with the main characteristics of the global carbon cycle. The model's climatology, as assessed from a range of metrics (related in particular to radiation, temperature, precipitation, and wind), is strongly improved in comparison to previous model versions. Although they are reduced, a number of known biases and shortcomings (e.g., double Intertropical Convergence Zone [ITCZ], frequency of midlatitude wintertime blockings, and El Niño–Southern Oscillation [ENSO] dynamics) persist. The equilibrium climate sensitivity and transient climate response have both increased from the previous climate model IPSL‐CM5A‐LR used in CMIP5. A large ensemble of more than 30 members for the historical period (1850–2018) and a smaller ensemble for a range of emissions scenarios (until 2100 and 2300) are also presented and discussed.

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49

Sicard, Marie, Masa Kageyama, Sylvie Charbit, Pascale Braconnot, and Jean-Baptiste Madeleine. "An energy budget approach to understand the Arctic warming during the Last Interglacial." Climate of the Past, December 13, 2021. https://doi.org/10.5281/zenodo.5777277.

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This dataset contains the IPSL-CM6A-LR model outputs (piControl and lig127k simulations) used to draw the figures in Sicard et al. manuscript "An energy budget approach to understand the Arctic warmingduring the Last Interglacial" submitted to Climate of the Past. We have adjusted the monthly outputs from the lig127k simulation thanks to the PaleoCalAdjust algorithm (Bartlein and Shafer, 2019).

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Lurton, Thibaut, Yves Balkanski, Vladislav Bastrikov, et al. "Implementation of the CMIP6 Forcing Data in the IPSL‐CM6A‐LR Model." Journal of Advances in Modeling Earth Systems 12, no. 4 (2020). http://dx.doi.org/10.1029/2019ms001940.

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