Coupled Model Intercomparison Project Phase 5 Models Research Articles

Aerosol impacts on the East Asian winter monsoon: Insights from TaiESM1 and CMIP6 simulations

AbstractThe East Asian winter monsoon (EAWM) is an important climate pattern in Southeast Asia. This study delved into the aerosol effects on the EAWM by analysing simulations conducted with the Taiwan Earth System Model version 1 (TaiESM1) model, part of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Our assessment, validated against observational and reanalysis data, revealed that TaiESM1 effectively replicates the robust circulation of the EAWM and aerosol concentrations, placing its performance above average level among CMIP6 models. Notably, the simulated global mean aerosol optical thickness exhibited an approximately 20% increase from 1850 to 2014. The increased aerosols corresponded to a weakened Aleutian Low and an intensified Siberian High, leading to a weakened EAWM in the lower atmosphere over extratropical areas. In addition, the cooling across the Northern Hemisphere might affect enhanced northerly winds at approximately 10°N, which are situated in the southern part of the EAWM. An analysis of the near‐surface temperature budget indicated that the aerosol‐induced cooling in East China and India was primarily driven by changes in shortwave and longwave radiation fluxes, albeit slightly offset by sensible heat and latent heat fluxes. Furthermore, our study also reveals that while the mechanisms through which anthropogenic aerosols affect the northern portion of the EAWM vary across most CMIP6 models, their impacts on the tropical region remain consistent.

Enhanced performance of CMIP6 climate models in simulating historical precipitation in the Florida Peninsula

AbstractAssessing the performance of General Circulation Models (GCMs) in simulating historical regional precipitation is essential for climate assessments. This study analysed 18 GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and 27 GCMs from Phase 6 (CMIP6) for the Florida Peninsula. Naïve combinations formed the CMIP5‐Ensemble and CMIP6‐Ensemble. The reference dataset consisted of global gridded precipitation data from National Oceanic and Atmospheric Administration. GCM‐simulated precipitation data were compared to the reference dataset across multiple time scales between 1951 and 2005. Most CMIP5 and CMIP6 GCMs exhibited a negative bias at the daily time scale, with an absolute value ranging between 0 and 2 mm day−1 at the median level, except for INM‐CM4 and MRI‐CGCM3 (CMIP5) and CNRM‐ESM2‐1 (CMIP6). Two performance metrics, that is, Pearson correlation and mean absolute error (MAE), were used to evaluate the GCMs. A correlation value above 0.3 is statistically significant at the significance level (α = 0.05). For the Pearson correlation between simulated and observed monthly precipitation, only 15% of CMIP5 GCMs (3 out of 19) had a median value above 0.3, whilst this increased to approximately 39% (11 out of 28) for CMIP6 GCMs. For monthly climatological precipitation, only 21% (4 out of 21) of GCMs in CMIP5 showed a correlation with a median value of 0.8 or higher. This percentage rose to 50% (14 out of 28) for CMIP6. A notable difference was observed in the MAE between CMIP5 and CMIP6 climate models. Lastly, GCM raw outputs were evaluated based on rainy season characteristics (onset, demise and duration). Significant improvements from the CMIP5 to the CMIP6 models were observed in capturing the rainy season in the Florida Peninsula. This study thoroughly compares CMIP5 and CMIP6 climate models in simulating historical precipitation at various time scales for the study region. Although the results are specific to the study area, the methods can be applied more broadly.

Assessment of precipitation and near-surface temperature simulation by CMIP6 models in South America

This study evaluated the performance of 50 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating the statistical features of precipitation and air temperature in five subdomains of South America during the historical period (1995–2014). Monthly precipitation and temperature simulations were validated with data from the Climate Prediction Center Merged Analysis of Precipitation, the Global Precipitation Climatology Project, and the ERA5 reanalysis. The models’ performance was evaluated using a ranking analysis with statistical metrics such as mean, standard deviation, Pearson’s spatial correlation, annual cycle amplitude, and linear trend. The analyses considered the representation of precipitation and air temperature separately for each subdomain, the representation for all five regions together, and the joint representation of precipitation and air temperature for all five subdomains. In the Brazilian Amazon, the best-performing models were EC-Earth3-Veg, INM-CM4-8, and INMCM5-0 (precipitation), and IPSL-CM6A-LR, MPI-ESM2-0, and IITM-ESM (temperature). In the La Plata Basin, KACE-1-0-G, ACCESS-CM2, and IPSL-CM6A-LR (precipitation), and GFDL-ESM4, TaiESM1, and EC-Earth3-Veg (temperature) yielded the best simulations. In Northeast Brazil, SAM0-UNICON, CESM2, and MCM-UA-1-0 (precipitation), BCC-CSM2-MR, KACE-1-0-G, and CESM2 (temperature) showed the best results. In Argentine Patagonia, the GCMs ACCESS-CM2, ACCESS-ESM1-5 and EC-Earth3-Veg-LR (precipitation), and CAMS-CSM1-0, CMCC-CM2-HR4, and GFDL-ESM4 (temperature) outperformed. Finally, for Southeast Brazil, the models ACCESS-CM2, ACCESS-ESM1-5, and EC-Earth3-Veg-LR (precipitation), and CAMS-CSM1-0, CMCC-CM2-HR4, and GFDL-ESM4 (temperature) yielded the best simulations. The joint evaluation of the regions and variables indicated that the best models are CESM2, TaiESM1, CMCC-CM2-HR4, FIO-ESM-2-0, and MRI-ESM2-0.

Anthropogenic influence on seasonal extreme temperatures in eastern China at century scale

Due to the scarcity of observational data in the early 20th century, very limited research has explored the impact of human activities on temperature extremes at the regional scale. Here we used a newly developed homogenized near-surface air temperature dataset from the beginning of the 20th century to estimate the frequency and intensity of extreme temperatures in eastern China and evaluate their anthropogenic influence based on models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found clear increases in warm extremes and decreases in cold extremes since 1901 for both annual and seasonal mean temperatures, with more pronounced changes in recent decades. The most significant warming occurred in spring and winter, approximately double the smallest warming observed in autumn. The CMIP6 models generally replicated the century-scale warming in annual and seasonal temperature extremes, showing increases in the frequency and intensity of warm extremes and corresponding decreases in cold extremes. The optimal fingerprinting detections suggest that the century-scale warming can be clearly attributed to anthropogenic forcing, including changes in seasonal extreme temperatures. Most observed changes in extreme temperatures were attributable to anthropogenic greenhouse gas emissions, partially offset by a smaller negative impact from anthropogenic aerosol forcing, whereas natural forcing has played a minor role. These results provide important information for accurately projecting future changes in temperature extremes.

Interannual synchronization of the North American summer monsoon and the North Atlantic tropical cyclone genesis frequency

Variations of the North American summer monsoon (NASM) and North Atlantic tropical cyclone (NATC) activities strongly influence climate anomalies in North America, with serious potential risk to life and property. Despite the scientific importance of this topic, the possible linkage between the NASM and the NATC genesis frequency remains unexplored. Here, we aim to examine the relationship between interannual variations of the NASM intensity and the NATC genesis frequency based on observations and Coupled Model Intercomparison Project Phase 6 (CMIP6) models. Our results show a strong association between the NASM intensity and the NATC genesis frequency during the extended boreal summer, with a good synchronization between their interannual variations. In years with stronger (weaker) NASM intensity, the NATC genesis frequency tends to be higher (lower). The observed NASM–NATC synchronization may be explained by two pathways: tropical-ocean-driven pathway and monsoon-heating-driven pathway. In the tropical-ocean-driven pathway, the tropical Pacific and Atlantic interbasin sea surface temperature (SST) anomalies play a critical role in bridging the NASM and NATC, by modulating the cross-Central American wind. Simulations of the tropical Pacific–Atlantic interbasin SST anomalies are critical for CMIP6 models to capture the observed linkage between the NASM and the vertical wind shear over the NATC main development region (MDR). In the monsoon-heating-driven pathway, the heating source due to the rainfall anomalies associated with the NASM can trigger atmospheric circulation anomalies through the Gill-type response, thereby affecting the NATC by changing the vertical wind shear over the MDR. This study demonstrates a connection between interannual variations of the NASM and the NATC genesis frequency, results of which can be used to advance our understanding of the monsoon–TC relationship and increase research focus on the interannual NASM–NATC synchronization in climate prediction.

Outlook for Offshore Wind Energy Development in Mexico from WRF Simulations and CMIP6 Projections

This study presents a comprehensive assessment of the offshore wind energy potential in Mexico across 40 years (1979–2018) of numerical simulations using the Weather Research and Forecasting (WRF) model and data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The WRF identifies three regions with moderate to good wind potential: off the north coast of Tamaulipas (Zone I), the northwest coast of Yucatan (Zone II), and the Gulf of Tehuantepec (Zone III). The analysis involves comparing 47 CMIP6 climate models with the WRF results and selecting the best performing models to obtain future projections for the short term (2040–2069) and the long term (2070–2099). Two ensemble-based strategies were implemented. The first one, which uses an intersection approach from which four CMIP6 models were considered, reveals positive percentage differences in Zone II for both future projections, especially for the long-term one. In Zones I and III, positive values are also observed near the coast, mainly for the long-term projection, but they are considerably lower compared to those in Zone II. The second ensemble strategy uses weight assignment through the Mean Absolute Percentage Error, so that a greater weight is given to the model that performed better in each particular zone, potentially providing more accurate results. The findings suggest the likelihood of increased offshore wind energy in these three zones of Mexico, for both short- and long-term future projections, with positive percentage differences of up to 10% in certain areas.

Projected changes in extreme daily precipitation linked to changes in precipitable water and vertical velocity in CMIP6 models

Understanding the drivers of precipitation and their changes in a non-stationary climate is crucial for effective climate adaptation and water resource management, as it helps us anticipate and respond to shifting precipitation patterns and their impacts. Here, analysing simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) we show that the conditional probability of extreme daily precipitation given joint extremes of two drivers (precipitable water and vertical velocity) will be stable in a 3 °C warmer future. Consistent with earlier work, we find that the near-global increase in precipitable water (thermodynamic influence) is the baseline for changes in extreme precipitation, which are modulated by changes in vertical velocity (dynamic influence). Thus, in regions where vertical velocity increases, the effect of the two drivers is additive and their changes contribute to an increase in extreme precipitation. The changes of the two drivers are opposite where vertical velocity decreases, resulting in only small increases in extreme precipitation or even a decrease. Furthermore, we reveal that there are moderate changes in the dependence between the drivers, which are larger over the ocean than over landmasses, but they contribute only little to the overall changes in extreme precipitation. We conclude that the use of two very simple drivers that are readily available from climate models can be of great utility for evaluating precipitation extremes in models and understanding their projected changes.

Projected changes in rainfall and temperature using CMIP6 models over the Okavango River basin, southern Africa

AbstractThe Okavango River Basin (ORB) which includes a renowned World Heritage site Okavango Delta, contains highly biodiverse ecosystems. Most of its rural population relies on rain-fed subsistence farming. Limited research has been done on future changes in rainfall and temperature in this region. Here, such changes are analysed for the periods 2030–2059 (near term) and 2070–2099 (long term), relative to the historical period 1985–2014. The analysis is based on the Coupled Model Intercomparison Project Phase 6 (CMIP6), under the shared socioeconomic pathways (SSPs) SSP245 and SSP585. The projected changes vary over the ORB. For rainfall, the models project some significant decreasing trends over Ngamiland in the central/southern ORB, but none are significant over the northern ORB. The significant trends during the near term period include those projected by the model average (-30.60 mm/decade) in March–April (MA) under the SSP585 scenario. Some significant decreasing rainfall trends have also been found in December-February (DJF), but none are significant in October–November (ON). DJF is the main growing season whereas ON (MA) is the onset (end) of the rainy season. For temperature, significant warming trends have been found over both Ngamiland and the northern ORB, with the strongest warming in ON particularly during the near term period. For this ON season over the northern ORB, under the SSP585 (SSP245) scenario, the model average projects a warming trend of 0.56 °C/decade (0.67 °C/decade). These findings may help with the management of agricultural activities, water resources and the highly biodiverse ecosystems in the ORB.

Improving Earth System Model Selection Methodologies for Projecting Hydroclimatic Change: Case Study in the Pacific Northwest

AbstractThe rapid expansion of Earth system model (ESM) data available from the Coupled Model Intercomparison Project Phase 6 (CMIP6) necessitates new methods to evaluate the performance and suitability of ESMs used for hydroclimate applications as these extremely large data volumes complicate stakeholder efforts to use new ESM outputs in updated climate vulnerability and impact assessments. We develop an analysis framework to inform ESM sub‐selection based on process‐oriented considerations and demonstrate its performance for a regional application in the US Pacific Northwest. First, a suite of global and regional metrics is calculated, using multiple historical observation datasets to assess ESM performance. These metrics are then used to rank CMIP6 models, and a culled ensemble of models is selected using a trend‐related diagnostics approach. This culling strategy does not dramatically change climate scenario trend projections in this region, despite retaining only 20% of the CMIP6 ESMs in the final model ensemble. The reliability of the culled trend projection envelope and model response similarity is also assessed using a perfect model framework. The absolute difference in temperature trend projections is reduced relative to the full ensemble compared to the model for each SSP scenario, while precipitation trend errors are largely unaffected. In addition, we find that the spread of the culled ensemble temperature and precipitation trends includes the trend of the “truth” model ∼83%‐92% of the time. This analysis demonstrates a reliable method to reduce ESM ensemble size that can ease use of ESMs for creating and understanding climate vulnerability and impact assessments.

Projections of tropical cyclones over the north Indian Ocean using different tracking schemes under CMIP5 models

This research compares two different methods of tracing cyclones in the North Indian Ocean (NIO)- (i) Commonwealth Scientific and Industrial Research Organisation (CSIRO) Direct Detection (CDD) and Okubo-Weiss-Zeta parameter (OWZ) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) model data. Many CMIP5 models are evaluated against TC observations from the International Best Track Archive for Climate Stewardship (IBTrACS) and a statistical Generalised Additive Model for climate change projections in the past (1970–2000). Estimates of TCs' potential future occurrence in the NIO are evaluated using CMIP5 models (2070–2100). When compared to historical tracks, the geographic distribution of TCs generated by both detection techniques is consistent with what would be expected, and the frequency of TCs in the models is, with a few exceptions, consistent with observations. Generally, the OWZ plan results in more TCs per unit time than the CDD scheme. Though there are significant differences between the two tracking techniques, a small number of models have TC counts that are virtually similar. Compared to the CDD plan, the OWZ scheme generally has higher performance in the NIO area.

Future precipitation changes in California: Comparison of CMIP5 and CMIP6 intermodel spread and its drivers

AbstractCalifornia is one of the major uncertainty hotspots for climate change, as climate models have historically been split between projecting wetter and drier future conditions over the region. We analysed the future (mid‐century and end‐century) projections of California's winter precipitation changes from the latest Coupled Model Intercomparison Project Phase 6 (CMIP6), and studied its respective model agreement in comparison to the previous CMIP5 projections. Over northern California more than two thirds of the models in each ensemble agree on wetter future conditions. However, over southern California both ensembles show highly uncertain precipitation changes, with model projections almost equally divided between wetter or drier conditions. Projected end‐century precipitation changes range from −30% to +70% in CMIP5 and −20% to +80% in CMIP6. The CMIP6 ensemble mean changes are generally wetter and show larger model disagreement compared to CMIP5. Distribution of year‐to‐year precipitation indicates more extremely wet or dry years over southern California in CMIP6 compared to CMIP5, with some models suggesting that the five wettest years account for as much as ~55% of the 20‐year rainfall, and the five driest for as little as ~5%. Dynamically, both ensembles project weakened subsidence over Baja California that is stronger in CMIP6 than in CMIP5, in line with the wetter mean conditions in CMIP6. In the western tropical Pacific we find strengthening of the Hadley circulation in CMIP6 that is not seen in CMIP5, and more El Niño than La Niña conditions in the equatorial Pacific. More CMIP6 models also project an increase in ENSO events compared to CMIP5, and a stronger impact of ENSO on California's precipitation is found in CMIP6 than in CMIP5. These factors also contribute to larger model disagreement and more extremely wet or dry years over southern California in CMIP6.

Evaluating the climatic state of Indian Summer Monsoon during the mid-Pliocene period using CMIP6 model simulations

The Indian Summer Monsoon Rainfall (ISMR) plays a critical role in agriculture, thereby significantly affecting the economy of India. Yet, there is a large spread in the ISMR variability for future projections (by the end of 21st century) as simulated by coupled general circulation models. Gaining insight into the variations of the ISMR during warm periods could enhance our ability to understand ISMR variability in the future. In this study, we have selected the mid-Pliocene warm period from 3.0 to 3.3 million years ago (Ma), which has similar external forcing (orbital parameters) comparable to the end of the 21st century. To evaluate the ISMR mean state during the mid-Pliocene, we have used six available Coupled Model Intercomparison Project phase 6 (CMIP6) model simulations and their multi-model ensemble mean. Our analysis suggests that the ensemble of CMIP6 models is better than individual models in capturing the ISM rainfall patterns and its characteristics for the historical period of 1914–2013. During the mid-Pliocene, we find an increase in the JJAS rainfall over most parts of India in comparison to the pre-industrial period with an increase of 34% in seasonal precipitation. This higher precipitation conditions during the mid-Pliocene is accompanied by thermo dynamical (higher CO2 forcing led to higher tropospheric temperature and higher precipitable water) and dynamical (larger tropospheric temperature gradient between Indian landmass and southern Indian Ocean corresponds to enhanced moisture transport, enhanced low-level cross-equatorial flow and intensified Monsoon Hadley Circulation) aspects.

An Intercomparison of Snow Mass Budget over Arctic Sea Ice Simulated by CMIP6 Models

Abstract The Arctic has experienced rapid changes in recent decades. For the first time, we intercompare five snow mass budget processes over Arctic sea ice simulated by 22 models from the Coupled Model Intercomparison Project phase 6 (CMIP6) using new diagnostics that have not been available for previous CMIPs. Our analysis suggests that snowfall accumulation (melt) is the dominant process contributing to nearly 100% (70.4% ± 10.1%) of the annual snow growth (loss). Snow mass change through sea ice dynamics, snow–ice conversion, and sublimation contribute 10.9% ± 4.9%, 9.7% ± 5.9%, and 9.0% ± 7.7% to the total snow mass loss. The seasonal cycle of various snow processes simulated by most of the CMIP6 models generally follows similar variations. There is reduced snowfall accumulation, melt, and sea ice dynamics during 1993–2014. However, substantial temporal and spatial discrepancies are noteworthy between the CMIP6 models. There is a large spread of snowfall accumulation and snowmelt in summer and fall, snow–ice conversion from autumn to spring, sublimation in late spring and summer, and snow mass change due to sea ice dynamics from winter to midspring. About half the models show decreasing trends of snowfall accumulation during 1993–2014, with no trends in others. Divergent trends in snow–ice conversion and sublimation occur in the Greenland and Barents Seas. The discrepancies are attributed equally to internal variability and model structural differences. Future projections that remove the identified outlier models suggest a significant reduction in snowfall accumulation, snowmelt, and snow mass change due to sea ice dynamics in the Arctic Ocean from 2015 to 2099. Snow–ice conversion and sublimation are also projected to be reduced but with less confidence.

MJO Structure–Propagation Nexus and Impacts of Background Mean States in CMIP6 Models

Abstract Eastward propagation is an essential feature of the Madden–Julian oscillation (MJO). Yet, it remains a challenge to realistically simulate it by global climate system models, and the reasons are not fully understood. This study evaluates the capability of 20 Coupled Model Intercomparison Project phase 6 (CMIP6) models in simulating MJO’s eastward propagation and its intrinsic links with the dynamic–thermodynamic structures and the background mean states, aiming at better understanding the sources of the simulation errors. The metrics to evaluate the MJO internal dynamics consists of six parameters: 1) the east–west asymmetry in the low-level circulation, 2) the boundary layer moisture convergence propagation, 3) the vertical tilt of equivalent potential temperature or moist static energy, the vertical structures of 4) diabatic heating and 5) available potential energy generation, and 6) upper-level diabatic heating and divergence. We also gauge the performance of three MJO-related background mean-state fields, including precipitation, sea surface temperature, and low-level moist static energy. It is argued that these parameters are relevant internal and external factors that could affect MJO eastward propagation. We find that the boundary layer moisture convergence is most tightly coupled with the eastward propagation of MJO and controls the premoistening, destabilization, and the leading low-level diabatic heating and available potential energy generation. The CMIP6 models exhibit significant improvements against CMIP5 models in simulating MJO dynamic–thermodynamic structures and the mean states. The diagnostics in this study could help to identify the possible processes related to CMIP6 models’ shortcomings and shed light on how to improve simulation of MJO eastward propagation in the future.

Historical trends and future projections of annual rainfall from CMIP6 models in Ho Chi Minh City, Vietnam

Climate risks have posed a major threat to many local communities living in low-lying coastal megacities across the globe, including Ho Chi Minh City, Vietnam. Hence, this study first aimed to contribute towards a comprehensive understanding of temporal trend patterns of annual rainfall and absolute extremes in Ho Chi Minh City over the last 4 decades (1980-2022) through multiple non-parametric statistical trend tests. We employed the quantile delta mapping (QDM) method to develop daily bias-corrected rainfall data based on the outputs in the latest Coupled Model Intercomparison Project phase 6 (CMIP6) under 8 shared socio-economic pathway (SSP) greenhouse gas emission scenarios. Evaluation of model performance was implemented by repeatedly omitting 5 successive years in turn for estimating testing errors. The outcomes indicate the high applicability of well-calibrated transfer functions, even for high quantiles, to the production of future rainfall scenarios. The projected changes in annual rainfall and absolute extremes were obtained by estimating multi-model medians from CMIP6 models for future periods (i.e. 2021-2040, 2041-2060, 2061-2080, and 2081-2100), with reference to the base period (1995-2014). In general, annual rainfall in Ho Chi Minh City is projected to increase substantially, and Thu Duc station consistently shows the highest increases in annual rainfall. Projected changes are approximately 30.9% (8.3 to 77.8%) under the high-end scenario (i.e. SSP5-8.5) by the end of the 21st century. It is expected that these findings will yield several solid arguments for mitigating climate-related risks in Ho Chi Minh City.

Human influence on the duration of extreme temperature events in Asia's hotspot regions

Observations and models indicate that human activities exert a considerable impact on the frequency and intensity of extreme temperature events, which are associated with global warming. However, changes in the duration of extreme temperature events and their association with human influence have not been considered in most studies. Thus, the possible relationship between the observed changes in the warm and cold spell duration (WSDI and CSDI) in hotspot regions during 1960–2014 and human influence was investigated based on the NCEP/NCAR reanalysis version 1 and Coupled Model Inter-comparison Project Phase 6 (CMIP6) data. Constraint projection based on these attribution results was also performed. The optimal fingerprinting technique was used to compare observed changes in WSDI and CSDI to simulated changes averaged across eight CMIP6 models. Results show that anthropogenic (ANT) forcing contributed to the observed increase in WSDI in the three hotspot regions (West Asia, South Asia and Southeast Asia), with the majority of the changes being attributed to greenhouse gas forcing. However, a generally weak ANT signal can be observed in the decreasing trend of CSDI and can be detected in South and Southeast Asia. The influence of aerosol forcing remains undetected in either WSDI or CSDI, which differs from the results for frequency and intensity of extreme temperatures. The attribution results revealed that the constrained projection of WSDI is lower than the raw projection for 2015–2100 in West Asia and Southeast Asia. However, no differences in future CSDI changes are found in Southeast Asia between the constrained and raw projections.

Understanding the Inter-Model Spread of PDO’s Impact on Tropical Cyclone Frequency over the Western North Pacific in CMIP6 Models

This work investigates the inter-model diversity of the Pacific Decadal Oscillation’s (PDO) impact on tropical cyclone frequency (TCF) over the Western North Pacific (WNP) from the historical simulation of twenty-two Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The impact of the PDO is expressed as the TCF difference between the positive and negative PDO phases. The comparison between the models with high PDO skill and low PDO skill shows that the PDO-related sea surface temperature (SST) gradient between the western and central tropical Pacific plays an important role in changing the large-scale atmospheric dynamic fields for TC genesis and, thus, the TCF over the WNP. This SST gradient also significantly contributes to the inter-model spread of PDO’s impact on TCF across the 22 CMIP6 models. We, therefore, stress that the PDO-related eastward SST gradient between the western and central tropical Pacific triggers the lower troposphere westerly and eastward extending of the monsoon trough over the WNP. The moistening of the atmosphere and enhancing ascending motion in the mid-troposphere promote convection, leading to the easterly wind anomaly over the upper troposphere, which reduces the vertical wind shear. Those favorable dynamic conditions consistently promote the TC formation over the southeastern part of the Western North Pacific. Our results highlight that PDO could impact the WNP TCF through its associated tropical SST gradient.

Evaluating future water availability in Texas through the lens of a data-driven approach leveraged with CMIP6 general circulation models

Climate change is escalating the frequency and intensity of extreme precipitation events, significantly influencing the spatial and temporal distributions of water resources. This is particularly evident in Texas, a rapidly growing state with a pronounced west-east gradient in water supply. This study utilizes Coupled Model Intercomparison Project Phase 6 (CMIP6) data and data-driven methodology to improve projections of Texas's future water resources, focusing on actual evapotranspiration (AET) and water availability through enhanced Multi-Model Ensembles. The results reveal that the data-driven model significantly outperforms the CMIP5 and CMIP6 models across all skill metrics, underscoring the potential of data-driven methodologies in advancing climate science. Furthermore, the study provides an in-depth analysis of the projected changes in net water availability (NWA) and estimated water demand for different regions in Texas over the next six decades from 2015 to 2074, which reveal fluctuating patterns of water stress, with the regions (nine out of sixteen water planning regions in Texas, especially for the most populated regions) poised for heightened challenges in reconciling water demand and availability. While increasing trends are found in precipitation, AET, and NWA for the northern region of Texas based on SSP2–4.5, decreasing trends are found over the southern region for all three parameters based on SSP5–8.5. These findings underscore the importance of factoring both spatial and temporal variations in water availability and demand for effective water management strategies and the need for adaptive water management strategies for the changing water availability scenarios.

A MaxEnt Model of Citrus Black Fly Aleurocanthus woglumi Ashby (Hemiptera: Aleyrodidae) under Different Climate Change Scenarios.

The citrus blackfly (CBF), Aleurocanthus woglumi Ashby, is an exotic pest native to Southeast Asia that has spread rapidly to the world's main centers of citrus production, having been recently introduced to Brazil. In this study, a maximum entropy niche model (MaxEnt) was used to predict the potential worldwide distribution of CBF under current and future climate change scenarios for 2030 and 2050. These future scenarios came from the Coupled Model Intercomparison Project Phase 6 (CMIP6), SSP1-2.6, and SSP5-8.5. The MaxEnt model predicted the potential distribution of CBF with area under receiver operator curve (AUC) values of 0.953 and 0.930 in the initial and final models, respectively. The average temperature of the coldest quarter months, precipitation of the rainiest month, isothermality, and precipitation of the driest month were the strongest predictors of CBF distribution, with contributions of 36.7%, 14.7%, 13.2%, and 10.2%, respectively. The model based on the current time conditions predicted that suitable areas for the potential occurrence of CBF, including countries such as Brazil, China, the European Union, the USA, Egypt, Turkey, and Morocco, are located in tropical and subtropical regions. Models from SSP1-2.6 (2030 and 2050) and SSP5-8.5 (2030) predicted that suitable habitats for CBF are increasing dramatically worldwide under future climate change scenarios, particularly in areas located in the southern US, southern Europe, North Africa, South China, and part of Australia. On the other hand, the SSP5-8.5 model of 2050 indicated a great retraction of the areas suitable for CBF located in the tropical region, with an emphasis on countries such as Brazil, Colombia, Venezuela, and India. In general, the CMIP6 models predicted greater risks of invasion and dissemination of CBF until 2030 and 2050 in the southern regions of the USA, European Union, and China, which are some of the world's largest orange producers. Knowledge of the current situation and future propagation paths of the pest serve as tools to improve the strategic government policies employed in CBF's regulation, commercialization, inspection, combat, and phytosanitary management.

Spatio-temporal correspondence of aerosol optical depth between CMIP6 simulations and MODIS retrievals over India.

In the present work, the aerosol optical depth (AOD) at 550nm of the moderate resolution imaging spectroradiometer (MODIS) onboard the Terra satellite was utilised to evaluate the AOD simulations of newly emerged general circulation models (GCMs) of coupled model intercomparison project-phase 6 (CMIP6) over the Indian landmass. Further, the AOD from the CMIP6 models has been compared with its previous generation models from CMIP5 to examine the extent of uncertainties in AOD with reference to the MODIS AOD datasets. The evolution of aerosols over India using the different shared socioeconomic pathways (SSPs) has also been studied till the year 2050. The results show that the CMIP5 and CMIP6 models underestimated the mean annual AOD of the Indian region as a whole. A multi-model mean (MMM) of thirteen GCMs from CMIP6 showed an underestimation of AOD by 40 to 60% over the Indo-Gangetic plains, while an overestimation of 60 to 80% in AOD was observed over the Peninsular and Central Indian regions in comparison with MODIS for the study period of 2001 to 2014. In future simulations, the pathway SSP370 has shown a significant increasing trend of AOD whereas SSP126 and SSP585 have shown significant decreasing trends of AOD by the year 2050. In the future, the changes in the AOD will mainly be contributed by the anthropogenic aerosols (AOA, BC, and Sulphates) emissions in all SSPs. The large bias of MMM with the MODIS requires further research in terms of analysing the accuracy of emission datasets that have been used to simulate the AODs by the CMIP6 models over the Indian region.