International Journal of Climatology最新文献

Northwest China is much more sensitive to climate warming, and the climate has varied rapidly from warm and drought to warm and humid conditions. In addition, due to the complex terrain of Northwest China, the methods and parameterization schemes of different CMIP6 models, these models are mostly applied to arid areas in Northwest China or Central Asia, lacking climate data for plateau areas and eastern Lanzhou, specifically in filtering CMIP6 models and evaluating applicable models. In this paper, 34 CMIP6 climate models are used to evaluate and forecast future trends in Northwest China under the SSP126, SSP245 and SSP585 scenarios in the short, medium and long term. CMIP6 models of temperature and precipitation are identified by applying the interannual variability skill score (IVS) between CN05.1 datasets and historical CMIP6 models, which are suitable for Northwest China. Then, we assess the characteristics, warming and wetting deviations, and uncertainties in the prediction of climatic change according to CMIP6 models over Northwest China. The results show that CMIP6 models in precipitation and temperature applicable to Northwest China are AWI-CM-1-1-MR, BCC-CSM2-MR, FGOALS-g3, INM-CM4-8, INM-CM5-0 and MRI-ESM2-0. The multi-model ensemble mean (MMEM) has better capability than individual CMIP6 models in precipitation and temperature prediction. Spatiotemporal climatic change over Northwest China shows overall warming and wetting trends. The IVS provides the ability to estimate CMIP6 model simulation performance both temporally and spatially. The temperature simulation is quite good in the Tarim Basin and Hexi Corridor region, and the precipitation simulation is quite good in the plateau region, Altai Mountains, Tianshan Mountains and Hexi Corridor region. Cold and wet deviations occur in Northwest China due to the topography and few stations, which are common reasons. The main sources of uncertainties in temperature prediction during this century are model uncertainty (before the 2090s) and scenario variability (after the 2090s), and model uncertainty in precipitation for CMIP6 becomes the main source of uncertainty.

This study projects the changes in tropical cyclone (TC) landfalls in the western North Pacific under shared socioeconomic pathway (SSPs) scenarios during the TC peak season by using low-resolution global climate models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6). Projections are based on the relationship between mid- and lower-level atmospheric circulation and TC landfall frequency during the historical period from 1985 to 2014 and the future climate period from 2015 to 2100. The landfall areas for TCs are divided into northern East Asia (NEA), middle East Asia (MEA) and southern East Asia (SEA); the TC peak seasons are July–September for NEA and MEA, and July–November for SEA. To evaluate reproducibility, both ensemble and individual model outputs for mid- and lower-level atmospheric circulations associated with TC landfall in each East Asian subregion are compared to the reanalysis. An ensemble of seven models with stable results for all three regions is more reasonable in simulating atmospheric circulation patterns than an ensemble of all CMIP6 models. The findings suggest that TC landfall is projected to increase by about 12% and 32% in NEA and MEA, respectively, in the late 21st century under the SSP5-8.5 scenario compared to the historical period, while decreasing by 13% in SEA. These changes are consistent under both warming scenarios, and are more pronounced in the SSP5-8.5 scenario compared to SSP1-2.6, particularly in the later period of this century. An analysis of future atmospheric circulations suggests that global warming will weaken the western North Pacific subtropical high and cause its boundary to retreat eastward. This will lead to changes in the steering flow, which is closely related to TC tracks, resulting in TC landfalls to increase or decrease depending on the East Asian subregion.

The evaluation of climate models is a crucial step in climate studies. It consists of quantifying the resemblance of model outputs to reference data to identify models with superior capacity to replicate specific climate variables. Clearly, the choice of the evaluation indicator significantly impacts the results, underscoring the importance of selecting an indicator that properly captures the characteristics of a “good model”. This study examines the behaviour of six indicators, considering spatial correlation, distribution mean, variance and shape. Monthly data for precipitation, temperature and teleconnection patterns in Central America were utilized in the analysis. A new multicomponent measure was selected based on these criteria to assess the performance of 32 CMIP6 models in reproducing the annual seasonal cycle of these variables. The top six models were determined using multicriteria methods. It was found that even the best model reproduces one derived climatic variable poorly in this region. The proposed measure and selection method can contribute to enhancing the accuracy of climatological research based on climate models.

Climate change is leading to alterations in the dynamic and thermodynamic climate systems worldwide, including the Indian summer monsoon (ISM), which supports more than a billion population and drives the Indian economy. The anthropogenic climate change induces unprecedented transformations in the natural and ecological systems, such as the increased probability of precipitation extremes, changes in their frequency, duration and spatial variabilities. This current study aims to project the regional landscape-based metric, velocity of climate change (VoCC) and associated climatic exposure regarding precipitation extremes (PEs) for India and its different biogeographic zones. The climate velocities of mean precipitation, 95th, 99.5th and 99.9th percentiles of precipitation for the ISM season are presented for the historical and three projected time slices under the RCP8.5 scenario. ROM, a state-of-the-art regional earth system model over the CORDEX-South Asia domain, was used in the study. It was observed that the intense and very intense rainfall (95th, 99.5th and 99.9th percentiles) was enhanced over most of the study region in the near- and mid-future compared to the far-future. The intense rainfall exhibited higher climate velocity than the mean and very intense precipitation in the near-future. The southern part of the Indian subcontinent usually displayed positive VoCC values for the historical and near-future time slices compared to the northern part of the Indian peninsula, particularly the intense and very intense precipitation. The climatic exposure for all-India was also higher in the near- and mid-future compared to the far-future, especially for the intense rainfall followed by the mean and very intense rainfall. These results suggest the need for focusing the adaptation and mitigation measures towards managing the near-term impacts of PEs in relation to the long-term impacts, especially on the country's diverse flora.

Global climate models (GCMs) are extensively used to calculate standardized drought indices. However, inaccuracies in GCM simulations and uncertainties inherent in the standardization methodology limit the precision of drought evaluations. The objective of this research is to remove bias in GCMs for improving drought monitoring and assessment. Consequently, this article proposes a new framework for drought index under the ensemble of GCMs—Multi-Model Quantile Mapped Standardized Precipitation Index (MMQMSPI). In accordance of Standardized Precipitation Index (SPI), the second stage derives a new index by assessing the feasibility of parametric and nonparametric models during standardization. In the application, we used 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) data of precipitation across 32 grid points within the Tibetan Plateau region. The comparative findings reveal that the integration of KCGMD is the most suitable choice compared to other best-fitted univariate distributions in both features of the proposed framework. In this research, we assess the implications of evaluating future patterns of drought for the years 2015–2100 using seven different time periods and three different future scenarios. Temporal behavior clearly shows monthly variations in the pattern of MMQMSPI, and these variations differ on each time scale, but a drastic change can be seen over the long term, i.e., extreme dry and wet conditions, with a higher probability in all scenarios.

The Köppen-Geiger (K-G) climate classification is the most commonly used climate classification method in the world, and there are many K-G climate classification studies focusing on Türkiye using different datasets. However, the differences in the datasets used in these studies lead to substantial differences and errors in K-G climate zone maps. The differences and disagreements in these maps also cause significant discrepancies in climate studies. In this respect, accurate identification of climate classes and types is very important for understanding the distribution of climate types and for many climate-based studies to achieve accurate results. In this study, the K-G climate types of Türkiye and the regime characteristics of these climate types were determined using the CHELSA dataset corrected based on the measurements of 337 meteorological stations. According to the results that were obtained, 14 climate types were identified in Türkiye. Since the CHELSA dataset reflected topographic conditions well, many microclimates were identified within broad areas of climate types. The distribution of the microclimate types was compared to the distribution of the vegetation, and the accuracy of the results was evaluated. Apart from microclimates, other prominent features of this study were the co-occurrence of multiple climate types in a limited area in the Eastern Black Sea Region and the detection of the EF climate type for the first time at the summit of Mount Ararat. Climate types vary according to altitude conditions, and temperature changes due to altitude are an important factor in the formation of climate sub-types within the same main climate type in Türkiye.

The development of subseasonal forecasts has seen significant advancements, transforming our ability to predict weather patterns and climate variability on intermediate timescales ranging from 2 weeks to 2 months. Motivated by the need to enhance our understanding of subseasonal precipitation forecasts and their applicability to the hydrology forecast, this study retrospectively analysed precipitation ensemble forecasts from subseasonal prediction models in the Uruguay River basin nearby Salto Grande dam. Three models were considered: two from the S2S project (ECMWF and CNRM) and one from the SubX project (GEFS). Model forecasts were analysed on a weekly time scale using both deterministic and probabilistic approaches. Multimodel probabilistic forecasts combining the three different models were built to increase forecast skill. Individual models have a skill larger than or equal to the climatological forecast until 2 weeks in advance. Particularly, ECMWF shows better skill in both ensemble mean and probabilistic forecast. Multimodel probabilistic forecast improves the skill of the forecast throughout the year, with the skill even surpassing the climatological forecast by up to 4 weeks in advance during the summer. In addition, model skill was analysed considering the state of the El Niño–Southern Oscillation (ENSO) on a weekly and monthly basis. On weekly time scales the ENSO state modifies model skill differently depending on the sub-basin and season considered. However, the influence of ENSO on forecast skill is more clearly observed on monthly time scales, with largest improvement in the lower basin during springtime. The results of this work suggest that subseasonal models are a promising tool to bridge the gap between weather and climate forecast in the Uruguay River basin and have the potential to be utilized for hydrological forecasting in the study region.

China's Losses Plateau (LP) is one of the ecologically vulnerable and the most severe soil erosion regions. Thus, knowing spatiotemporal changes in evapotranspiration (ET) and its components (soil evaporation, E; transpiration, T; and vegetation interception evaporation, EI) and revealing the underlying mechanisms are vital for ecosystem and water resources sustainability for this region. Here, we investigate the spatiotemporal changes in ET and its components and then quantify the impacts of climate variables (i.e., precipitation, radiation, temperature, and relative humidity) and vegetation dynamics (e.g., land use/cover changes [LUCC] and changes in leaf area index [LAI]) on their annual trends, by using a process-based terrestrial ecosystem model and a joint-solution method with multiple sensitivity numerical experiments. Results show that over 67% of the study region experienced significant (p < 0.05) increases in annual ET, T, and EI, with regional average rises of 4.05, 3.67, and 0.74 mm·year⁻¹, respectively. However, there are significant (p < 0.05) decreases in regional mean E of 0.38 mm·year⁻¹, and the negative trend covers 35.8% of the study area. E, T, and EI changes dominate the annual ET trends over 11.8%, 87.3%, and 0.9% of the study area, respectively. Attribution analyses highlight the increased LAI as the critical factor governing these trends across most of the LP (>58%). At the same time, precipitation and LUCC play a more dominant role in the remaining areas. This study emphasizes the spatial heterogeneity in the drivers of changes in ET and its components and highlights the critical role of vegetation dynamics. These findings provide valuable insights for understanding the ET processes and guiding sustainable water resource management in the LP.