CMIP5 Multi-model Ensemble Research Articles

Evaluation and projection of marine heatwaves in the South China Sea: insights from CMIP6 multi-model ensemble

Evaluation and projection of marine heatwaves in the South China Sea: insights from CMIP6 multi-model ensemble

Temperature extremes Projections over Bangladesh from CMIP6 Multi-model Ensemble

Temperature extremes Projections over Bangladesh from CMIP6 Multi-model Ensemble

Unusually low dust activity in North Africa in June 2023: Causes, impacts and future projections

Dust activity during the pre-monsoon season in Africa has an impact on the monsoon circulation and the Atlantic hurricane season. During early June 2023 the atmosphere was relatively clear over West Africa and the eastern tropical Atlantic, in contrast with the dustier June 2020. The negative phase of the North Atlantic Oscillation suppressed dust lifting, with an equatorward shifted African Easterly Jet limiting its downstream advection. On the other hand, dust accumulated in the atmosphere over northeastern Africa, with the negative dust aerosol optical depth (DAOD) anomalies over western Africa and the positive anomalies over eastern Africa more than two standard deviations away from the climatological mean. The lack of dust led to an up to 55 W m−2 increase in the surface downward shortwave radiation flux and a 35 W m−2 decrease in the longwave flux, and is in line with the record-breaking sea surface temperatures over the eastern tropical Atlantic and the active start to the Atlantic hurricane season. In order to explore future projections of DAOD, a multi-model ensemble (MME) is constructed from 29 models that integrate the sixth phase of the Coupled Model Intercomparison Project (CMIP6). It captures the positive trend in the June DAOD over the eastern tropical Atlantic during 1980–2014, although the amplitude is roughly a factor of six smaller than the 0.0017 year−1 in the reanalysis dataset. The CMIP6 MME projects a further increase in DAOD in the region at a rate of up to 0.0003 year−1 in the most extreme climate change scenario for 2066–2100, which is comparable to that seen during the historical period, even though the mean values are projected to decrease by 0.03–0.06. While lower dust loadings may lead to improved air quality, they will likely further fuel pre-season and early season storms in the North Atlantic, which have become more frequent in recent decades.

A comprehensive analysis of future solar energy potential variations using a CMIP6 multi-model ensemble approach in Colombia

A comprehensive analysis of future solar energy potential variations using a CMIP6 multi-model ensemble approach in Colombia

Historical and future extreme climate events in highly vulnerable small Caribbean Islands

Small Caribbean islands are on the frontline of climate change because of sea level rise, extreme rainfall and temperature events, and heavy hurricanes. The Archipelago of San Andrés, Providencia, and Santa Catalina (SAI), are Caribbean islands belonging to Colombia and declared a Biosphere Reserve by UNESCO. SAI is highly vulnerable to climate change impacts but no hydroclimatological study quantified the extreme climatic changes yet. This study analyzes historical (1960s-2020, 7 stations) and future (2071–2100, CMIP6 multi-model ensemble, for four scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) trends in mean and extreme precipitation and temperature duration, frequency, and intensity. We find that heatwaves have more than tripled in frequency and doubled their maximum duration since the end of the ‘80 s. Precipitation is historically reduced by 5%, with a reduction recorded in 5 stations and an increase in 2, while extreme rainfall events significantly increased in frequency and intensity in most stations. The hotter-and-drier climate is amplified in the future for all scenarios, with much drier extremes (e.g., -0.5─-17% wet days, +8%─30% consecutive dry days, and +60%─89% in hot days). Although we show that hurricanes Categories IV and V near SAI (< 600 km) more than doubled since the’60 s, only a small fraction of extreme rainfall in the archipelago is associated with hurricanes or tropical storms. La Niña events also have no substantial influence on extreme precipitation. Interestingly, opposite and heterogeneous historical extreme rainfall trends are found across such small territory (< 30 km2). Thus, downscaled hydrometeorological data and model simulations are essential to investigate future extreme climatic events and strengthen small Caribbean islands' climate change adaptation efforts.

Probabilistic projection of extreme precipitation changes over Iran by the CMIP6 multi-model ensemble

Probabilistic projection of extreme precipitation changes over Iran by the CMIP6 multi-model ensemble

Variations of Global Compound Temperature and Precipitation Events and Associated Population Exposure Projected by the CMIP6 Multi-Model Ensemble

Variations of Global Compound Temperature and Precipitation Events and Associated Population Exposure Projected by the CMIP6 Multi-Model Ensemble

Spatial prediction of changes in landslide susceptibility under extreme daily rainfall from the cmip6 multi-model ensemble

Spatial prediction of changes in landslide susceptibility under extreme daily rainfall from the cmip6 multi-model ensemble

Changes in Future Rainfall over Southeast Asia Using the CMIP6 Multi-model Ensemble

A multi-model ensemble from the new CMIP6 models was utilized to determine the future changes in precipitation over Southeast Asia (SEA; longitude: 90°E–140°E, latitude: 15°S–30°N). The changes are computed for the three (3) future time slices (2021–2040, 2041–2060, and 2081–2100) under four (4) different scenarios based on the Shared Socioeconomic Pathways (SSPs): 1-2.6, 2-4.5, 3-7.0, and 5-8.5. Our results indicate that future rainfall in the SEA-averaged region could increase by about 4%, 5%, 6%, and 9% towards the end of the century relative to the present-day average (1995–2014) under SSP1-2.6, 2-4.5, 3-7.0, and 5-8.5, respectively. Among all scenarios, SSP3-7.0 widely shows remarkably dry conditions whereas SSP5-8.5 suggests extremely wet conditions on different time scales. A clear dissociation of wet and dry areas is expected in the far-term period (2081–2100). Changes in the annual cycle indicate that monsoon rainfall could experience significant increases. The study also emphasizes the importance of moisture flux convergence (MFC) in determining precipitation patterns across different seasons and regions. The results suggest that MFC plays a crucial role in the projected increase or decrease of rainfall in SEA regions. Spatial correlation of future global mean temperature (GMT) and rainfall have a high positive (negative) correlation in the north (south) latitudes. Changes in rainfall are found to be sensitive to GMT. The responses to future rainfall changes per degree Celsius of warming are at the rate of 8.9%, 6.3%, 3.6%, and 2.7% under SSP1-2.6, 2-4.5, 3-7.0, and 5-8.5, respectively.

Anthropogenic influence on the extremely low September sea ice and hot summer of 2020 over the Arctic and its future risk of occurrence

During 2020, the Arctic is marked by extremely low sea ice coverage and hot climate. September Sea Ice Extent (SIE) was about 2.3 million km2 below the 1979–2014 mean and the 2nd lowest on the 1979–2020 record, while regional summer (June–August, JJA) mean 2 m air temperature (TAS) was about 1.3 °C above the 1979–2014 mean and was the hottest on record at the time. Locally, September Sea Ice Concentration (SIC) was approximately 70% lower and JJA TAS can be as much as 6.0 °C higher than the 1979–2014 mean. Although the proximate cause for the extreme event was the continuously favorable atmospheric circulation patterns, wind conditions and ice-albedo feedback, the main objective of this paper is probabilistic extreme event attribution studies to assess the anthropogenic influence. Based on the CMIP6 multi-model ensemble products, modeled long-term trends of Arctic sea ice and TAS are consistent with observed trends when including anthropogenic forcing or greenhouse gas (GHG) forcing, while cannot exhibit observed trends with only aerosol or natural forcing. Further analysis reveals that human influence including GHG forcing has substantially increased the probability of occurrence of the 2020-like extreme events, which are rare in aerosol-only or natural-only forcing. The frequencies of 2020-like low SIE increase by 19 times with all forcing and 16 times with GHG forcing than with natural forcing. Future climate simulations under different Shared Socioeconomic Pathway (SSP) scenarios of SSP126, SSP245 and SSP585 show that the 2020-like extreme event that is currently considered rare is projected to become the norm and almost occur 1-in-1 year beyond 2041–2060. The probabilities will be approximately in the range of 0.84–1.00 for SIE and 0.76–0.99 for TAS from low emission of SSP126 to high emission of SSP585.

Evaluation and projections of summer daily precipitation over Northeastern China in an optimal CMIP6 Multimodel Ensemble

Evaluation and projections of summer daily precipitation over Northeastern China in an optimal CMIP6 Multimodel Ensemble

A Data-Driven Probabilistic Network Approach to Assess Model Similarity in CMIP Ensembles

Abstract The different phases of the Coupled Model Intercomparison Project (CMIP) provide ensembles of past, present, and future climate simulations crucial for climate change impact and adaptation activities. These ensembles are produced using multiple global climate models (GCMs) from different modeling centers with some shared building blocks and interdependencies. Applications typically follow the “model democracy” approach which might have significant implications in the resulting products (e.g., large bias and low spread). Thus, quantifying model similarity within ensembles is crucial for interpreting model agreement and multimodel uncertainty in climate change studies. The classical methods used for assessing GCM similarity can be classified into two groups. The a priori approach relies on expert knowledge about the components of these models, while the a posteriori approach seeks similarity in the GCMs’ output variables and is thus data-driven. In this study, we apply probabilistic network models (PNMs), a well-established machine learning technique, as a new a posteriori method to measure intermodel similarities. The proposed methodology is applied to surface temperature fields of the historical experiments from the CMIP5 multimodel ensemble and different reanalysis gridded datasets. PNMs are able to learn the complex spatial dependency structures present in climate data, including teleconnections operating on multiple spatial scales, characteristic of the underlying GCM. A distance metric building on the resulting PNMs is applied to characterize GCM model dependencies. The results of this approach are in line with those obtained with more traditional methods but have further explanatory potential building on probabilistic model querying. Significance Statement The present study proposes the use of probabilistic network models (PNMs) to quantify model similarity within ensembles of global climate models (GCMs). This is crucial for interpreting model agreement and multimodel uncertainty in climate change studies. When applied to climate data (gridded global surface temperature in this study), PNMs encode the relevant spatial dependencies (local and remote connections). Similarities among the PNMs resulting from different GCMs can be quantified and are shown to capture similar GCM formulations reported in previous studies. Differently to other machine learning methods previously applied to this problem, PNMs are fully explainable (allowing probabilistic querying) and are applicable to high-dimensional gridded raw data.

Near-Future Projection of Sea Surface Winds in Northwest Pacific Ocean Based on a CMIP6 Multi-Model Ensemble

Information about wind variations and future wind conditions is essential for a monsoon domain such as the Northwest Pacific (NWP) region. This study utilizes 10 Generalized Circulation Models (GCM) from CMIP6 to evaluate near-future wind changes in the NWP under various climate warming scenarios. Evaluation against the ERA5 reanalysis dataset for the historical period 1985–2014 reveals a relatively small error with an average of no more than 1 m/s, particularly in the East Asian Marginal Seas (EAMS). Future projections (2026–2050) indicate intensified winds, with a 5–8% increase in the summer season in the EAMS, such as the Yellow Sea, East Sea, and East China Sea, while slight decreases are observed in the winter period. Climate mode influences show that winter El Niño tends to decrease wind speeds in the southern study domain, while intensifying winds are observed in the northern part, particularly under SSP5-8.5. Conversely, summer El Niño induces higher positive anomalous wind speeds in the EAMS, observed in SSP2-4.5. These conditions are likely linked to El Niño-induced SST anomalies. For the application of CMIP6 surface winds, the findings are essential for further investigations focusing on the oceanic consequences of anticipated wind changes such as the ocean wave climate, which can be studied through model simulations.

Extreme climate events under global warming in Iran

ABSTRACT Extreme climate events and precipitation depths are assessed for Iran with minimal uncertainties, considering three sources of uncertainty: general circulation models (GCMs), future scenarios, and downscaling method. The frequency, duration, severity, and intensity of extreme events, along with extreme precipitation depths for 2- to 50-year return periods, are evaluated for the baseline period (1980–2014) and three Shared Socioeconomic Pathways (SSPs): SSP1-1.9, SSP2- 4.5, and SSP5-8.5 (2021–2080). Daily precipitation and temperature data from multi-model ensemble of the Coupled Model Inter-comparison Project phase 6 are statistically downscaled for 51 stations across Iran. Extreme precipitation depths increase under SSP1-1.9, whereas the precipitation anomaly depends on location under SSP2-4.5 and SSP5-8.5. The southern coastal and southeastern regions (remaining stations) will experience more-frequent shorter (less-frequent longer) extreme events. The GCM and downscaling method are the main sources of uncertainty in projected precipitation, while climate scenario significantly contributes to the uncertainty in projected temperature.

Estimations of potential evapotranspiration from CMIP6 multi-model ensemble over Africa

Potential evapotranspiration (PET) plays a pivotal role in resource management and drought assessment. However, future PET estimates remain underexplored in the African region. This study employs twenty General Circulation Models (GCMs) to estimate past (1979–2014) and future PET changes across near-term (2021–2040), mid-term (2061–2080), and long-term (2081–2100) periods, considering four Shared Socioeconomic Pathways (SSPs) including SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. The research assesses the impact of various climatic factors on PET across Africa and its sub-regions using the Penman-Monteith model. The analysis reveals that Penman-Monteith estimates for PET during 1979–2014 exhibit an increase of 0.68 mm per year (mm/a) across Africa. Notably, the Northern region (NAF), Sahara (SAH), Southern region (SAF), and Eastern region (EAF) experience higher PET changes of 1.78 mm/a, 1.75 mm/a, 1.09 mm/a, and 0.12 mm/a, respectively. Conversely, the Western region (WAF) and Central region (CAF) exhibit negative trends of −0.03 mm/a, and − 0.28 mm/a. Future PET in whole Africa is projected to increase by 0.05 mm/a in SSP1–2.6 and SSP2–4.5, and 0.07 mm/a in higher emissions for 2021–2040, by 0.02 mm/an under SSP1–2.6, 0.07 in SSP2–4.5, 0.09 mm/a, and 0.16 mm/a, in SSP3–7.0 and SSP5–8.5 for 2061–2080, and by −0.01 mm/a in SSP1–2.6, 0.05 mm/a SSP2–4.5, 0.10 mm/a SSP3–7.0, and 0.18 mm/a SSP5–8.5 for 2081–2100. Furthermore, higher emissions are anticipated to drive PET increases in various regions during 2081–2100, with NAF, SAH, and SAF projected to rise by 0.17 mm/a, 0.16 mm/a, and 0.23 mm/a, respectively. WAF, CAF, and EAF are expected to experience increases of 0.20 mm/a, 0.19 mm/a, and 0.15 mm/a, respectively. Contribution analysis indicates that solar radiation played a major factor in PET over Africa as well as in WAF, CAF, and EAF. Maximum temperatures were pivotal in NAF, SAH, and SAF. In future periods (2021-2040, 2061-2080, and 2081-2100), maximum temperatures take precedence to Africa's PET, and at varying percentages to different sub-regions. The findings underscore the significance of PET estimation, particularly in the context of drought evaluation locally and regionally.

Assessing Climate Change Impacts on Wind Energy Resources over China Based on CMIP6 Multimodel Ensemble

Assessing Climate Change Impacts on Wind Energy Resources over China Based on CMIP6 Multimodel Ensemble

Prospective Evolution of Meteorological Drought in the Haihe River Basin and Its Connection With Large‐Scale Atmospheric Circulations Using CMIP6 Multimodel Ensemble

AbstractClimate change modifies monsoon rainfall patterns, leading to significant impacts on future meteorological droughts. This study investigated the spatiotemporal pattern of future precipitation, drought features, and the dynamic mechanisms governing their evolution induced by teleconnected atmospheric circulation in the Haihe River Basin, situated in the north China monsoon region. The investigation was conducted under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5 scenarios of CMIP6, utilizing the Standardized Precipitation Index and an improved run theory at seasonal and annual scales. The findings revealed that high‐emission scenarios cause a wetter climate trend, particularly in autumn and winter, related to the attenuation of the East Asian Trough from climate warming. The uneven intra‐annual precipitation patterns within the basin, induced by the Asian monsoon climate, are expected to improve in the far future, particularly under the SSP2‐4.5 scenarios. The drought frequency, intensity, and duration of future meteorological drought are expected to improve compared to historical conditions under all three scenarios. SSP2‐4.5 shows the shortest duration and lightest intensity of annual droughts. Extreme droughts are primarily driven by strong subsidence with low‐level moisture divergence. However, in the far future, the eastward shift of the western Pacific subtropical high results in reducing moisture transport from the western Pacific, leading to increasing contribution of advection term to atmospheric drying, particularly under the SSP1‐2.6. In winter, the increase in radiative forcing leads to enhanced evapotranspiration from the Eurasian continent, providing additional moisture and alleviating drought, especially in the far future under the SSP1‐2.6 and SSP2‐4.5 scenarios.

Influence of stratosphere-troposphere exchange on long-term trends of surface ozone in CMIP6

Surface ozone, which is a major air pollutant, has gained widespread public attention due to its significant harm to human health and ecosystems. Here we find that long-term trends of surface ozone are largely influenced by stratosphere-troposphere exchange (STE) using the historical simulations in the CMIP6 multi-model ensemble. It is found that STE contributes up to 39.5% of climatological surface ozone in the extratropics of the winter hemisphere. In the Southern Hemisphere (SH), prominent stratospheric ozone depletion tends to reduce tropospheric and surface ozone due to STE over 1960–1997, which has been reversed during 1997–2014. In the Northern Hemisphere (NH), STE enhances surface ozone during both ozone depletion and recovery periods. About 29.5% of the positive trends of surface ozone over 1960–2014 can be attributed to STE processes in boreal winter in the NH extratropics. The enhancement of STE is related to the changes in atmospheric circulation associated with global warming. Our results suggest that STE will further worsen surface ozone pollution in the future associated with the stratospheric ozone recovery and global warming.

Projection on Antarctic Temperature Extremes from the CMIP6 Multimodel Ensemble under Different Scenarios

Abstract Global warming has been accelerating the frequency and intensity of climate extremes, and has had an immense influence on the economy and society, but attention is seldom paid to future Antarctic temperature extremes. This study investigates five surface extreme temperature indices derived from the multimodel ensemble mean (MMEM) based on 14 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) under the shared socioeconomic pathways (SSPs) of SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. In Antarctica, the variations in extreme temperature indices exhibit regional and seasonal differences. The diurnal temperature range (DTR) usually illustrates a downward trend, particularly for the Antarctic Peninsula and Antarctic coast, and the strongest change occurs in austral summer. In all cases, the annual highest minimum/maximum temperature (TNx/TXx) increases faster in inland Antarctica. Antarctic amplification of extreme temperature indices is detected and is strongest at the lowest maximum temperature (TXn). At the Antarctic Peninsula, TXx amplification only appears in winter. Great DTR amplification appears along the Antarctic coast and is strongest in summer and weakest in winter. The changes in extreme temperature indices indicate the accelerated Antarctic warming in future scenarios.

Coupling crop simulation modelling and multi-criteria decision aid for ranking the sustainability of cropping sequences

IntroductionCrop sequences are important to make efficient use of natural resources and ensure food security. Climate change affects the sustainability of crops in cropping sequence. The application of crop sequence modelling to evaluate the sustainability of cropping sequences under projected climate is a less explored area.MethodsThe present work evaluated the impact of projected climate on six cropping sequences viz., rice-mustard-groundnut, rice-lentil-groundnut, rice-potato-groundnut, rice-wheat-groundnut, rice-maize-groundnut and rice-mustard-fallow in the Lower Gangetic Plain of India. CMIP-5 multi-model ensemble, long-term crop sequence modelling using DSSAT and a multiple criteria decision analysis tool, TOPSIS was used to evaluate sustainability. Future climate scenarios were developed using 29 GCMs from which a subset of 5 representative GCMs was selected for mid-century (2040-2069) and end-century (2070-99) under RCP4.5 and RCP8.5 emission scenarios. Weighted average ensemble yield, ETa and nitrogen fixed by all cropping sequences for the study period were used to rank cropping sequences for sustainability using TOPSIS.Results and DiscussionThe minimum and maximum temperatures during mid and end-centuries were projected to be consistently higher than the baseline period (1980-2010) for all the cropping seasons. Under all the periods, rice-lentil-groundnut had the highest weighted average ensemble yield, followed by rice-wheat-groundnut. Rice-lentil-groundnut fixed the highest quantity of nitrogen, followed by rice-maize-groundnut. Ranking of cropping sequences for sustainability by the TOPSIS method indicated that during mid-century (under both RCP4.5 and RCP8.5), the rice-lentil-groundnut sequence will be the most sustainable cropping sequence. However, by the end century, the rice-wheat-groundnut sequence will be the most sustainable cropping sequence, followed by rice-lentil-groundnut under both RCP4.5 and 8.5. In all the cropping sequences, rice will be benefitted by higher amount of nitrogen fixed and preceding groundnut. The three parameters considered for defining sustainability in crop production (yield, ETa and N-fixed) ensures higher produce and return for the farmer, less dependence on irrigation sources and increase in soil nitrogen content. In this paper, we show for the first time that cropping sequences can be evaluated for sustainability by combining crop sequence modelling, GCM ensemble and multi-criteria decision analysis. The results of the study will help the farmers of the study area to opt for the most sustainable cropping sequence and other alternatives in the context of climate change.