Coupled Model Intercomparison Project Phase Research Articles

Assessing consistency in drought risks in India with multiple multivariate meteorological drought indices (MMDI) under climate change.

This study investigates the spatio-temporal consistency of different MMDI formulations and their role in meteorological drought characterization uncertainty under historic and future climates using ERA5 reanalysis, and outputs from eight Coupled Model Intercomparison Project Phase 6 models, respectively, across different climate zones and shared socioeconomic pathways (SSP) in the Indian subcontinent. Six MMDI formulations namely the Standardized Precipitation Evaporation Index (SPEI), Reconnaissance Drought Index (RDI), and self-calibrated Palmer Drought Severity Index (scPDSI), Standardized Palmer Drought Index (SPDI), Standardized Moisture Anomaly Index (SZI) and Supply Demand Drought Index (SDDI) are used. A suite of analysis including agreement mapping, category difference analysis and uncertainty contribution analysis using global sensitivity analysis (GSA) are employed to quantify the consistency of MMDIs and uncertainty in drought characterization due to the MMDI formulation. The variation in MMDI consistency due to different reference evapotranspiration (ETo) methods is also studied. Results demonstrate strong agreement among the MMDIs under historic climate. Under climate change scenarios our findings demonstrate broad agreement among majority of the MMDIs across the study domain, but in substantial areas where MMDI not agree, especially for higher emission scenarios and arid zones. Increased uncertainty under climate change is due to SDDI and SPEI projecting dryer conditions while scPDSI projecting wetter conditions in the far future period owing to varying degrees of sensitivity of MMDIs to its constituent variables (Precipitation and ETo). Results also show that the uncertainty due to MMDIs varied considerably based on ETo methods as well. Finally, based on GSA analysis, the most significant sources of uncertainty in drought projections under climate change are attributed to MMDI-GCM interactions and MMDIs for the Penman-Monteith method. Discrepancies in drought estimates caused by the MMDI selection highlight the need for careful evaluation of drought indices before adopting for climate change impact assessment.

Projecting future snow changes at kilometer scale for adaptation using machine learning and a CMIP6 multi-model ensemble.

Assessing future snow cover changes is challenging because the high spatial resolution required is typically unavailable from climate models. This study, therefore, proposes an alternative approach to estimating snow changes by developing a super-spatial-resolution downscaling model of snow depth (SD) for Japan using a convolutional neural network (CNN)-based method, and by downscaling an ensemble of models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) dataset. After assessing the coherence of the observed reference SD dataset with independent observations, we leveraged it to train the CNN downscaling model; following its evaluation, we applied the trained model to CMIP6 climate simulations. The downscaled mean ensemble reproduced the spatial distribution and seasonality of the reference observations. We found an average decrease in the snow-covered area by about 20% in winter and 25% in early spring, an altitude-dependent of the SD changes, and a delayed snow cover appearance by the middle of the 21st Century under a high emission scenario. Overall, the downscaling model captures physically plausible relationships, enables high-resolution assessments of future SD based on a multi-model ensemble, produces results consistent with regional climate models, and provides valuable insights into how future snow changes will affect winter tourism and water resources, highlighting its potential benefits for a wide range of adaptation studies.

Enhanced interannual variability of the terrestrial carbon sink in China under high emissions

Abstract Interannual variability (IAV) of terrestrial carbon uptake is a major contributor to the variation of atmospheric CO2. With the influence of the East Asian monsoon, future climate variability would significantly increase in China. However, how these future changes will modulate the IAV of China’s terrestrial carbon sinks remains unclear. Here, we analyzed the IAV of China’s terrestrial net ecosystem productivity (NEPIAV) and investigated the potential impacts of climate change under various scenarios during the 21st century using the outputs from nine Coupled Model Intercomparison Project Phase 6 models. The results reveal that China’s terrestrial NEPIAV would be enhanced under higher emissions scenarios from 2015 to 2100. The standard deviation of national NEPIAV under the SSP585 scenario rises by 12% compared with SSP126. The most prominent contribution to this enhancement in the total NEPIAV comes from a larger NEP IAV in summer (10%), particularly in the subtropical–tropical monsoonal climate zone in China. Moreover, the enhancement is largely attributed to the intensified IAV in temperature and precipitation in the monsoonal climate zones as well as the heightened sensitivity to them, especially in the ecosystems of the subtropical–tropical monsoonal zone. Compared with monsoonal precipitation, IAV of temperature in the subtropical–tropical monsoonal climate zone also plays an important role in NEPIAV under higher emissions scenarios. Our results highlight the crucial influence of future fluctuations in monsoon climate systems on terrestrial carbon sink IAV and the urgency of reducing the uncertainties of Earth system models in predicting both climate in monsoon regions and the responses of carbon cycling processes to temperature.

Dengue dynamics, predictions, and future increase under changing monsoon climate in India

The global burden of dengue disease is escalating under the influence of climate change, with India contributing a third of the total. The non-linearity and regional heterogeneity inherent in the climate-dengue relationship and the lack of consistent data makes it difficult to make useful predictions for effective disease prevention. The current study investigates these non-linear climate-dengue links in Pune, a dengue hotspot region in India with a monsoonal climate and presents a model framework for predicting both the near-term and future dengue mortalities. Dengue mortality and meteorological conditions over a twelve-year period (2004–2015) are analyzed using statistical tools and machine learning methods. Our findings point to a significant influence of temperature, rainfall, and relative humidity on dengue mortality in Pune, at a time-lag of 2–5 months, providing sufficient lead time for an early warning targeted at curbing dengue outbreaks. We find that moderate rains spread over the summer monsoon season lead to an increase in dengue mortality, whereas heavy rains reduce it through the flushing effect, indicating the links between dengue and monsoon intraseasonal variability. Additionally, warm temperatures above 27°C and humidity levels between 60% and 78% elevate the risk of dengue. Based on these weather-dengue associations, we developed a machine-learning model utilizing the random forest regression algorithm. The dengue model yields a skillful forecast, achieving a statistically significant correlation coefficient of r = 0.77 and a relatively low Normalized Root Mean Squared Error score of 0.52 between actual and predicted dengue mortalities, at a lead time of two months. The model finds that the relative contributions of temperature, rainfall, and relative humidity to dengue mortality in Pune are 41%, 39%, and 20%, respectively. We use the dengue model in conjunction with the climate change simulations from the Coupled Model Intercomparison Project phase 6 for the future dengue mortality projections under a global warming scenario. In a changing climate, dengue-related mortality in Pune is projected to rise by 13% in the near future (2021–2040), 23–40% in the mid-century (2041–2060), and 30–112% in the late century (2081–2100) under low-to-high emission pathways in response to the associated increase in temperature and changes in monsoon rainfall patterns.

Compensatory effects conceal large uncertainties in the modelled processes behind the relationship between the El Niño–Southern Oscillation (ENSO) and CO2

Abstract. A large fraction of the interannual variation in the global carbon cycle can be explained and predicted by the impact of the El Niño–Southern Oscillation (ENSO) on net biome production (NBP). It is therefore crucial that the relationship between ENSO and NBP is correctly represented in Earth system models (ESMs). In this work, we look beyond the top-down ENSO–CO2 relationship by describing the characteristic ENSO–NBP pathways in 22 Coupled Model Intercomparison Project Phase 6 (CMIP6) ESMs. These pathways result from the configuration of three interacting processes that contribute to the overall ENSO–CO2 relationship: ENSO strength, ENSO-induced climate anomalies, and the sensitivity of NBP to climate. The analysed ESMs agree on the direction of the sensitivity of global NBP to ENSO but exhibit very high uncertainty with regard to its magnitude, with a global NBP anomaly of −0.15 to −2.13 Pg C yr−1 per standardised El Niño event. The largest source of uncertainty lies in the differences in the sensitivity of NBP to climate. This uncertainty among the ESMs increases even further when only the differences in NBP sensitivity to climate are considered. This is because differences in the climate sensitivity of NBP are partially compensated for by ENSO strength. A similar phenomenon occurs regarding the distribution of ENSO-induced climate anomalies. We show that even models that agree on global NBP anomalies exhibit strong disagreement with regard to the contributions of different regions to the global anomaly. This analysis shows that while ESMs can have a comparable ENSO-induced CO2 anomaly, the carbon fluxes contributing to this anomaly originate from different regions and are caused by different drivers. These alternative ENSO–NBP pathways can lead to a false confidence in the reproduction of CO2 by assimilating the ocean and the dismissal of predictive performance offered through ENSO. We suggest improving the underlying processes by using large-scale carbon flux data for model tuning in order to capture the ENSO-induced NBP anomaly patterns. The increasing availability of carbon flux data from atmospheric inversions and remote sensing products makes this a tangible goal that could lead to a better representation of the processes driving interannual variability in the global carbon cycle.

Comparative study of multivariate hybrid neural networks for global sea level prediction through 2050

The ongoing rise in global sea levels poses significant risks to coastal regions such as storms surges, floodings and necessitates accurate predictive models to inform the relevant government organizations that are responsible of mitigation strategies. This study leverages advanced hybrid deep learning techniques to forecast global sea level changes up to the year 2050. Utilizing a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, our model integrates historical global sea level data from climate.gov and global air temperature projections from the CMIP6 (Coupled Model Intercomparison Project Phase 6) model. Performance evaluation, based on metrics such as Nash-Sutcliffe Efficiency, Mean Squared Error (MSE), and the Diebold-Mariano Test, demonstrates the superior accuracy of the hybrid models over traditional deep learning models. Results show that the hybrid LSTM-CNN model outperforms the standalone models, achieving an MSE of 0.4644 mm and an NSE of 0.9994, compared to the LSTM model’s MSE of 2.4450 mm and NSE of 0.9970. These findings underscore the potential of deep learning methodologies in enhancing the precision of long-term sea level predictions, providing valuable insights for policymakers and researchers in climate science.

Dominant inflation of the Arctic Ocean’s Beaufort Gyre in a warming climate

The Arctic Ocean’s Beaufort Gyre, the largest Arctic freshwater reservoir, plays a crucial role for climate and marine ecosystems. Understanding how it changes in a warming climate is therefore essential. Here, using high-resolution simulations and Coupled Model Intercomparison Project phase 6 data, we find that the Beaufort Gyre will increasingly accumulate freshwater, elevate sea level, and spin up its circulation as the climate warms. These changes, collectively referred to as inflation, are more pronounced in the Beaufort Gyre region than in other Arctic areas, amplifying the spatial asymmetry of the Arctic Ocean. The inflation is driven by increased surface freshwater fluxes and intensified surface stress from wind strengthening and sea ice decline. Current climate models tend to underestimate this inflation, which could be alleviated by high-resolution ocean models and improved atmospheric circulation simulations. The inflation of the Beaufort Gyre underscores its growing importance in a warming climate.

Counteracting greenhouse gas and aerosol influences intensify global water seasonality over the past century

Variations in water availability seasonality significantly impact society and ecosystems. While many studies have focused on mean or extreme precipitation, the response of water availability seasonality, influencing yearly water distribution beyond individual extremes, to human-induced climate change remains underexplored. Here we examine global and regional water availability seasonality changes from 1915 to 2014, quantifying how anthropogenic greenhouse gases and aerosols have influenced these variations using reanalysis and simulations from Coupled Model Intercomparison Project Phase 6. Despite large spatiotemporal uncertainties due to regional variability and model assumptions, we find that greenhouse gases significantly amplify the seasonality, while aerosols reduce it. Given that the positive effects of greenhouse gases surpass the aerosols’ negative effects, the counterbalancing influences have led to an overall enhancement in seasonality of water availability over the past century. This trend is expected to continue in the future as greenhouse gases-induced warming continues to rise and aerosol levels decline.

Data supporting the North Atlantic Climate System Integrated Study (ACSIS) programme, including atmospheric composition; oceanographic and sea-ice observations (2016–2022); and output from ocean, atmosphere, land, and sea-ice models (1950–2050)

Abstract. The North Atlantic Climate System Integrated Study (ACSIS) was a large multidisciplinary research programme funded by the UK's Natural Environment Research Council (NERC). ACSIS ran from 2016 to 2022 and brought together around 80 scientists from seven leading UK-based environmental research institutes to deliver major advances in the understanding of North Atlantic climate variability and extremes. Here, we present an overview of the data generated by the ACSIS programme. The datasets described cover the North Atlantic Ocean, the atmosphere above it (including its composition), and Arctic sea ice. Atmospheric composition datasets include measurements from seven aircraft campaigns (45 flights in total, 0–10 km altitude range) in the northeastern Atlantic (∼ 15–55° N, ∼ 40° W–5° E) made at intervals of 6 months to 2 years between February 2017 and May 2022. The flights measured chemical species (including greenhouse gases; ozone precursors; and volatile organic compounds – VOCs) and aerosols (organic aerosol – OA; SO4; NH4; NO3; and non-sea salt chloride – nss-Cl) (https://doi.org/10.5285/6285564c34a246fc9ba5ce053d85e5e7, FAAM et al., 2024). Ground-based stations at the Cape Verde Atmospheric Observatory (CVAO), Penlee Point Atmospheric Observatory (PPAO), and Plymouth Marine Laboratory (PML) recorded ozone, ozone precursors, halocarbons, greenhouse gases (CO2 and methane), SO2, and photolysis rates (CVAO; http://catalogue.ceda.ac.uk/uuid/81693aad69409100b1b9a247b9ae75d5, National Centre for Atmospheric Science et al., 2010); O3 and CH4 (PPAO, https://catalogue.ceda.ac.uk/uuid/8f1ff8ea77534e08b03983685990a9b0 (Plymouth Marine Laboratory and Yang, 2017); and aerosols (PML, https://doi.org/10.5285/e74491c96ef24df29a9342a3d57b5939, Smyth, 2024), respectively. Complementary model simulations of atmospheric composition were performed with the UK Earth System Model (UKESM1) for the period from 1982 to 2020 using Coupled Model Intercomparison Project Phase 6 (CMIP6) historical forcing up to 2014 and Shared Socioeconomic Pathways (SSP) scenario SSP3-7.0 from 2015 to 2020. Model temperature and winds were relaxed towards ERA reanalysis. Monthly mean model data for ozone, NO, NO2, CO, methane, stratospheric ozone tracers, and 30 regionally emitted tracers are available for download (https://data.ceda.ac.uk/badc/acsis/UKESM1-hindcasts, Abraham, 2024). ACSIS also generated new ocean heat content diagnostics (https://doi.org/10/g6wm, https://doi.org/10/g8g2, Moat et al., 2021a–b) and gridded temperature and salinity based on objectively mapped Argo measurements (https://doi.org/10.5285/fe8e524d-7f04-41f3-e053-6c86abc04d51 King, 2023). An ensemble of atmosphere-forced global-ocean sea-ice simulations using the NEMO-CICE model was performed with horizontal resolutions of 1/4 and 1/12° covering the period from 1958 to 2020 using several different atmosphere-reanalysis-based surface forcing datasets, supplemented by additional global simulations and stand-alone sea-ice model simulations with advanced sea-ice physics using the CICE model (http://catalogue.ceda.ac.uk/uuid/770a885a8bc34d51ad71e87ef346d6a8, Megann et al., 2021e). Output is stored as monthly averages and includes 3D potential temperature, salinity, zonal velocity, meridional velocity, and vertical velocity; 2D sea-surface height, mixed-layer depth, surface heat, and freshwater fluxes; ice concentration and thickness; and a wide variety of other variables. In addition to the data presented here, we provide a very brief overview of several other datasets that were generated during ACSIS and have been described previously in the literature.

Climate model downscaling in central Asia: a dynamical and a neural network approach

Abstract. High-resolution climate projections are essential for estimating future climate change impacts. Statistical and dynamical downscaling methods, or a hybrid of both, are commonly employed to generate input datasets for impact modelling. In this study, we employ COSMO-CLM (CCLM) version 6.0, a regional climate model, to explore the benefits of dynamically downscaling a general circulation model (GCM) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), focusing on climate change projections for central Asia (CA). The CCLM, at 0.22° horizontal resolution, is driven by the MPI-ESM1-2-HR GCM (at 1° spatial resolution) for the historical period of 1985–2014 and the projection period of 2019–2100 under three Shared Socioeconomic Pathways (SSPs), namely the SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios. Using the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) gridded observation dataset as a reference, we evaluate the performance of CCLM driven by ERA-Interim reanalysis over the historical period. The added value of CCLM, compared to its driving GCM, is evident over mountainous areas in CA, which are at a higher risk of extreme precipitation events. The mean absolute error and bias of climatological precipitation (mm d−1) are reduced by 5 mm d−1 for summer and 3 mm d−1 for annual values. For winter, there was no error reduction achieved. However, the frequency of extreme precipitation values improved in the CCLM simulations. Additionally, we employ CCLM to refine future climate projections. We present high-resolution maps of heavy precipitation changes based on CCLM and compare them with the CMIP6 GCM ensemble. Our analysis indicates an increase in the intensity and frequency of heavy precipitation events over CA areas already at risk of extreme climatic events by the end of the century. The number of days with precipitation exceeding 20 mm increases by more than 90 by the end of the century, compared to the historical reference period, under the SSP3-7.0 and SSP5-8.5 scenarios. The annual 99th percentile of total precipitation increases by more than 9 mm d−1 over mountainous areas of central Asia by the end of the century, relative to the 1985–2014 reference period, under the SSP3-7.0 and SSP5-8.5 scenarios. Finally, we train a convolutional neural network (CNN) to map a GCM simulation to its dynamically downscaled CCLM counterpart. The CNN successfully emulates the GCM–CCLM model chain over large areas of CA but shows reduced skill when applied to a different GCM–CCLM model chain. The scientific community interested in downscaling CMIP6 models could use our downscaling data, and the CNN architecture offers an alternative to traditional dynamical and statistical methods.

Atlantic overturning inferred from air-sea heat fluxes indicates no decline since the 1960s

The Atlantic Meridional Overturning Circulation (AMOC) is crucial for global ocean carbon and heat uptake, and controls the climate around the North Atlantic. Despite its importance, quantifying the AMOC’s past changes and assessing its vulnerability to climate change remains highly uncertain. Understanding past AMOC changes has relied on proxies, most notably sea surface temperature anomalies over the subpolar North Atlantic. Here, we use 24 Earth System Models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to demonstrate that these temperature anomalies cannot robustly reconstruct the AMOC. Instead, we find that air-sea heat flux anomalies north of any given latitude in the North Atlantic between 26.5°N and 50°N are tightly linked to the AMOC anomaly at that latitude on decadal and centennial timescales. On these timescales, air-sea heat flux anomalies are strongly linked to AMOC-driven northward heat flux anomalies through the conservation of energy. On annual timescales, however, air-sea heat flux anomalies are mostly altered by atmospheric variability and less by AMOC anomalies. Based on the here identified relationship and observation-based estimates of the past air-sea heat flux in the North Atlantic from reanalysis products, the decadal averaged AMOC at 26.5°N has not weakened from 1963 to 2017 although substantial variability exists at all latitudes.

CMIP6 GCMs Projected Future Koppen‐Geiger Climate Zones on a Global Scale

AbstractThis study compared the future global climate zones based on four radiative forcings ranging from low‐end (SSP1‐2.6) to high‐end (SSP5‐8.5) using the Köppen‐Geiger climate classification. To reduce uncertainties in future projected precipitation and temperature, multimodel projections comprising 25 general circulation models (GCMs) were sourced from the recent Coupled Model Intercomparison Project phase six (CMIP6) and used to create a Multi‐Model Ensemble. The changes in historical climate zones on CMIP6 simulations were divided into six periods considering data availability (1954–1964; 1964–1974; 1974–1984; 1984–1994; 1994–2004; and 2004–2014). Furthermore, the climate zone reproducibility of 25 CMIP6 GCMs was compared with the reference data sourced from Global Precipitation Climatology Centre precipitation and Climatic Research Unit temperature. The future climate zones were projected into seven periods using monthly precipitation and surface temperature under four main SSP scenarios. Consequently, the climate variables from GCMs were overestimated compared to the reference data, and the composition of the climate zones was less complex. While temperature discrepancies of 1–2°C may not drastically alter the Köppen‐Geiger climate zone classifications, precipitation‐based classifications are significantly impacted by the observed errors. Thus, it is crucial to recognize that despite the advancements in GCMs, they still possess limitations in accurately predicting “real” future climate changes. The projected future climate zones are simpler compared to the historical periods across six continents, with tundra and ice caps expected to disappear. This study highlights potential risks by projecting future climate zones based on varying greenhouse gas concentration levels, stressing the importance of using these projections with caution given the inherent uncertainties and limitations of GCMs.

Runoff simulation and hydropower resource prediction of the Kaidu River Basin in the Tianshan Mountains, China

The Tianshan Mountains of Central Asia, highly sensitive to climate change, has been comprehensively assessed for its ecosystem vulnerability across multiple aspects. However, studies on the region’s main river systems and hydropower resources remain limited. Thus, examining the impact of climate change on the runoff and gross hydropower potential (GHP) of this region is essential for promoting sustainable development and effective management of water and hydropower resources. This study focused on the Kaidu River Basin that is situated above the Dashankou Hydropower Station on the southern slope of the Tianshan Mountains, China. By utilizing an ensemble of bias-corrected global climate models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6) and the Variable Infiltration Capacity (VIC) model coupled with a glacier module (VIC–Glacier), we examined the variations in future runoff and GHP during 2017–2070 under four shared socio-economic pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) compared to the baseline period (1985–2016). The findings indicated that precipitation and temperature in the Kaidu River Basin exhibit a general upward trend under the four SSP scenarios, with the fastest rate of increase in precipitation under the SSP2-4.5 scenario and the most significant changes in mean, maximum, and minimum temperatures under the SSP5-8.5 scenario, compared to the baseline period (1980–2016). Future runoff in the basin is projected to decrease, with rates of decline under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios being 3.09, 3.42, 7.04, and 7.20 m3/s per decade, respectively. The trends in GHP are consistent with runoff, with rates of decline in GHP under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios at 507.74, 563.33, 1158.44, and 1184.52 MW/10a, respectively. Compared to the baseline period (1985–2016), the rates of change in GHP under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios are −20.66%, −20.93%, −18.91%, and −17.49%, respectively. The Kaidu River Basin will face significant challenges in water and hydropower resources in the future, underscoring the need to adjust water resource management and hydropower planning within the basin.

Global drought-flood abrupt alternation: Spatiotemporal patterns, drivers, and projections

<p>The spatiotemporal patterns and driving factors of drought-flood abrupt alternations (DFAA) have been investigated across several regional and watershed scales; however, comprehensive examination at the global scale is lacking. Here, we employed the long period drought-flood abrupt change index (LDFAI), derived from an ensemble of 40 output datasets from eight Coupled Model Intercomparison Project phase 6 (CMIP6) models, to assess the spatiotemporal patterns, drivers, and future projections of global DFAA. The results indicate that DFAA are influenced by various anthropogenic forcings, and greenhouse gas emissions exert the most significant impact. The changes in the intensity of global DFAA (1950–2014), attributed to natural forcing (NAT), anthropogenic aerosols (AER), and greenhouse gas (GHG) forcing, accounted for 5.65%, 14.57%, and 33.55%, respectively. The rates of change of the DFAA intensity under shared socioeconomic pathways (SSPs) from 2014 to <styled-content style-type="number">2100</styled-content> were estimated to be 21.73% (SSP1-2.6), 45.37% (SSP2-4.5), 63.1% (SSP3-7.0), and 69.51% (SSP5-8.5). This means that under high radiative forcing, the regional rivalry and fossil-fuel development models will lead to a significant increase in DFAA. These findings can aid in the development of global adaptive policies related to DFAA.</p>

A novel early‐warning standardized indicator for drought preparedness and management under multiple climate model projections

AbstractIncreasing global temperatures have triggered several environmental and ecological challenges. Recurring droughts across the globe are an adverse consequence of global warming. In this research, a new drought forecasting index—the Multimodal Forecastable Standardized Precipitation Evapotranspiration Index (MFSPEI)—has been suggested using projections from multiple climate models. The MFSPEI methodology is primarily based on the first component of the Forecastable Component Analysis (FCA) and the Standardized Precipitation Evapotranspiration Index (SPEI). For application purposes, the time series data of SPEI from 10 climatic models endorsed by the Coupled Model Intercomparison Project phase 6 (CMIP‐6) at 50 random locations over the region of the Tibetan Plateau (TP) have been considered. The outcomes show that the first component of FCA captures a sufficient amount of variation while maintaining high forecastability in all the selected grid points and the chosen prominent timescales of drought monitoring indices. To assess the predictive performance of the proposed index (MFSPEI), comparison matrices of artificial neural network (ANN) models were identified. During the training and testing phases, the forecast efficiency of the developed indicator (MFSPEI) proved superior to that of the individual SPEI. The numerical assessment indicates that the deviations and difficulties in interpreting SPEI data from individual climate models can be addressed more effectively with the proposed indicator. Therefore, MFSPEI effectively reinforces drought predictions for drought preparedness and management in the context of multiple climate model projections.

Assessment of Droughts and Floods During the Indian Summer Monsoon Using the Coupled Model Intercomparison Project Phase 6 Historical and Future Simulations

ABSTRACTIndian summer monsoon (ISM) is the prominent global monsoon system, which occur annually from June to September and impacts the lives of over a quarter of the world's population. Studies show that global warming is one of the key reasons for the extreme events such as droughts and floods, and that also alter regional dynamics of ISM. Consequently, a comprehensive investigation of flood and drought events in India is imperative, because of their important role in the economy of the country. Here, we employ simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6) models to evaluate the intensity and frequency of droughts and floods across historical (1950–2014) and future (2015–2100) periods. We explore the best‐performing model for the ISM rainfall (ISMR) to unravel the characteristics of floods and droughts by analysing rainfall data from 53 models. Although majority of the models successfully replicate the annual cycle of ISMR, there exists a significant difference in rainfall amounts. Following an initial screening to identify the most efficient models, eight are selected for an in‐depth assessment. The chosen models slightly overestimate drought conditions, although they demonstrate a commendable concurrence in simulating the flood occurrences in India. Regarding the future projections, we analyse the Shared Socioeconomic Pathways (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5) for near (2015–2035)‐, mid (2047–2067)‐ and far (2079–2099)‐future periods, in addition to the total projection period (2015–2100). Our analysis reveals an increasing trend of droughts in the near‐future, compared to an increase in floods during the far‐future. It is also highlighted that the intensity of droughts is projected to amplify in the far‐future, while the intensity of floods is likely to diminish. Therefore, this study serves as a valuable resource for decision‐making processes, particularly in the flood, drought and agricultural disaster preparedness.

Evaluating CMIP6 Global Climate Models Performances Over Nigeria: An Integrated Approach

ABSTRACTThe choice of global climate models (GCMs) for climate or hydrological studies remains a challenge due to their temporal and spatial variations and different performances in different parts of the globe. This study assesses the performances of 33 GCMs of the Coupled Model Intercomparison Project Phase 6 (CMIP6) for precipitation, maximum temperature and minimum temperature over Nigeria in order to select the best performing GCMs for aggregation into a multi model ensemble (MME). The study uses three statistical metrics (SM) and Random Forest (RF) machine learning method for the evaluation of the GCMs. In addition, the GCM performances were also estimated using spatial assessment, boxplots, scatter plots and mean monthly comparison at each grid point over the period 1985–2014. Finally, the average was used to generate variations of MMEs by increasing the number of models in the MME considering the inclusion of the better ranking ones first in order to determine the optimum MME for the variables. The highest ranking GCMs based on the average of the scores of the SM and RF were NESM3, CMCC‐ES, IPSL‐L‐R‐INCA and IPSL‐L‐R, MPI‐HAM and SAMO‐UNICON for precipitation; BCC‐C‐MR, MRI‐ESM, BCC‐ESM1, ACC‐ESM1‐5 and GISS‐E2‐CC for maximum temperature; and GFDL‐ESM, AWI‐C‐MR, IPSL‐L‐R, CAS‐ESM2 and AWI‐C‐LR for minimum temperature. The highest‐ranking model for all variables is ACC‐ESM1‐5, which ranked highest with a score of 0.6920 followed by BCC‐C‐MR with 0.6898, CAS‐ESM2 with 0.6597 and BCC‐ESM1 with 0.6545 score. The results of the spatial assessment, boxplots, scatter plots and the mean monthly comparison aligns with this. In the aggregation of MME for the three variables, the optimum number of models was obtained after averaging of the first four best ranking GCMs. This study presents a localised study, which is expected to reduce uncertainty in the projection of climate over Nigeria.

Multi‐Model Projection of Climate Extremes under 1.5°C–4°C Global Warming Levels across Iran

ABSTRACTThis study investigates the spatial and temporal patterns of climate extremes in Iran and projects future changes using data from seven General Circulation Models (GCMs) that participated in the Coupled Model Intercomparison Project phase 6 (CMIP6). We assess the impacts of climate change under the SSP2‐4.5 and SSP5‐8.5 emission scenarios, considering global warming levels of 1.5°C, 2°C, 3°C, and 4°C above preindustrial levels. Gridded observations are derived from ground measurements, using the SYMAP algorithm at a 1/8° latitude–longitude resolution. Subsequently, statistical downscaling of GCMs is performed using the Multivariate Bias Correction (MBC) and Bias Correction Constructed Analogues with Quantile Mapping Reordering (BCCAQ) approaches. Projected changes in extreme temperature and precipitation events are evaluated using the CLIMDEX indices. The findings indicate consistent rises in annual temperatures across Iran, with temperature indices such as warm spell duration and the monthly minimum value of daily temperature exhibiting substantial increases, about twofold by the +4.0°C period. Additionally, the study highlights a potential intensification in precipitation extremes (Rx1day, Rx5day, R90p, R95p), suggesting a heightened risk of more frequent and severe floods, particularly in the western, northern, and northwestern regions. These insights underline the critical need for region‐specific adaptation strategies to address the risks associated with climate change in Iran.

The Source of the Uncertainties in CMIP6 Model's Projections of the Summertime Northwest Pacific Subtropical High

AbstractBased on monthly data from 30 models in the Coupled Model Intercomparison Project Phase 6 (CMIP6), the leading mode of the inter‐model uncertainties in the projections of the summertime Northwest Pacific subtropical high (NWPSH) under the high emissions scenario (SSP5‐8.5) is identified by inter‐model empirical orthogonal function (EOF) analysis of the sea level pressure (SLP) over the East Asia‐Northwest Pacific region. The leading mode is characterized by positive SLP anomalies over the East Asia‐Northwest Pacific region. Inter‐model regression analyses reveal the relationship between the leading mode and historical sea surface temperature (SST). The inter‐model uncertainties of the NWPSH projections are closely related to the historical tropical North Atlantic (TNA) cold SST anomalies. The TNA cold SST anomalies result in increased precipitation in the subtropical North Pacific through westward propagated Rossby wave responses, further enhancing the subtropical North Pacific cold SST anomalies in the future through the reinforced convection‐shortwave‐SST negative feedback. Suppressed convection over the subtropical North Pacific strengthens the NWPSH through the anticyclonic Rossby wave response. The emergent constraints based on the observed TNA SST pattern reduce the uncertainties of the NWPSH projections by 32.5%, indicating a weaker NWPSH compared to the multi‐model mean. Cold biases of the simulated present‐day TNA SST in most CMIP6 models indicate that the multi‐model ensemble mean (MME) may overestimate the intensity of NWPSH in the warmer future. This study helps improve the accuracy of NWPSH projections, contributing to climate change adaptations for many important sectors impacted by NWPSH variation, such as agriculture, infrastructure, and energy.

Projecting temperature-related dengue burden in the Philippines under various socioeconomic pathway scenarios

IntroductionAs climate change advances, the looming threat of dengue fever, intricately tied to rising temperatures, intensifies, posing a substantial and enduring public health challenge in the Philippines. This study aims to investigate the historical and projected excess dengue disease burden attributable to temperature to help inform climate change policies, and guide resource allocation for strategic climate change and dengue disease interventions.MethodsThe study utilized established temperature-dengue risk functions to estimate the historical dengue burden attributable to increased temperatures. Future projections were derived using Coupled Model Intercomparison Project Phase 6 (CMIP6) Shared Socioeconomic Pathway (SSP) scenarios to estimate the excess dengue burden on a national scale. Current health burden estimates were calculated by charting the attributable fraction per epidemiological week against the exponential risk function.ResultsProjections indicate a substantial increase in temperature-related dengue incidence across all SSP climate scenarios by 2100. Between 2010-2019, 72.1% of reported dengue cases in the Philippines were attributable to temperature, demonstrating that temperature is a significant driver in dengue transmission. The highest attributable fractions were observed between the warm-dry season to early rainy season (Epi Weeks 15–25). Southern, periequatorial areas, particularly those undergoing rapid urbanization, had the highest temperature-related dengue incidence.DiscussionThe findings emphasize the critical interplay between climate change and socioeconomic factors in shaping future dengue risk. By incorporating future climate scenarios and provincial-level projections, this study provides valuable insights for policy planning, early warning systems, and public health programming. Strengthening health infrastructure, promoting sustainable urban development, and implementing effective vector control measures are crucial to mitigating the future dengue burden in the Philippines.