CMIP5 Models Research Articles

Investigating the “Too Bright” Issue Pertaining to Non‐PBL Clouds Over the South Pacific Trade‐Wind Region in CMIP6 Global Climate Models

AbstractThis paper examines the “too bright” issue pertaining to non‐planetary boundary layer (PBL) clouds over the South Pacific trade‐wind region and its potential link to the falling ice radiative effects (FIREs). We run sensitivity experiments with CESM2‐CAM6 (CESM2) global climate model with FIREs on (SON) and off (NOS). The model exhibits more in‐cloud liquid water content (CLWC) and droplet above the PBL in NOS, leading to larger shortwave (SW) reflectivity at the top of the atmosphere than in SON over the trade wind regions. CMIP6 models are divided into three subsets: separately calculates the radiative effects of cloud ice and falling ice (SON2), combined (SON1) and without falling ice (NOS). SON2 models exhibit improved CLWC and SW reflectivity similar to CESM2‐SON, while NOS and SON1 models are akin to CESM2‐NOS owing to weaker surface wind stress and warmer ocean surface, caused by the lack of FIREs over the convective zones.

Assessing CMIP Models’ Ability to Detect Observed Surface Warming Signals Related to Climate Change

Abstract Assessing if CMIP models can detect observed climate change signals is crucial for evaluating their realism and strengthening confidence in future projections. These signals can only be compared once distinguishable from the background climate “noise.” Built for impact studies but not detection purpose, the time of emergence (ToE) estimates when this occurs using a fixed signal-to-noise ratio. To more accurately assess models’ ability to reproduce observed climate change signals, we introduce the time of detection (ToD), which employs a statistical significance test of the nonlinear trend, accounting for the time series length. ToD and ToE are computed for sea surface temperature (SST) and relative SST (RSST) across four observational and 69 CMIP5/6 historical and shared socioeconomic pathways 5–8.5 (SSP5–8.5) scenario datasets. ToD precedes ToE and is less sensitive to the chosen threshold. While already detectable in most of the tropics, SST warming in the equatorial Pacific is detectable in ∼90% of models but only one out of four observational datasets, due to a stronger modeled warming. Because RSST signals are weaker than SST signals, they do not emerge anywhere but are detectable in two regions. The enhanced warming in the western Indian Ocean, detected in ∼45% of models, is not robust in observations when excluding pre-1960 dubious data. Conversely, the subdued southeast Pacific warming detected in observations and ∼80% of models is robust and consistent with a poleward extension of the Southern Hemisphere Hadley cell. The ToD’s ability to detect subtle signals allows for assessing the realism of the CMIP warming pattern in this region. Significance Statement Recent studies emphasize the importance of the climate change warming pattern and its apparent discrepancy between climate models and observations in the equatorial Pacific. We argue that our newly proposed time of detection (ToD) is better suited than the well-known time of emergence (ToE) for investigating where modeled and observed warming patterns can be confidently compared. In agreement with previous studies, our analysis indicates model–observations disagreement over the equatorial Pacific. Using ToD instead of ToE allows for detecting a robust subdued warming (i.e., weaker than the tropical average) in the southeast Pacific and therefore confirms the realism of the CMIP warming pattern in this region.

Projections of future bioclimatic indicators using bias-corrected CMIP6 models: a case study in a tropical monsoon region.

This study evaluates the potential impacts of climate change on Bangladesh by analyzing 19 bioclimatic indicators based on temperature and precipitation. Data from 18 bias-corrected CMIP6 global climate models (GCMs) were used, covering four Shared Socioeconomic Pathways (SSPs)-SSP126, SSP245, SSP370, and SSP585-across three future timeframes: near-term (2015-2044), mid-term (2045-2074), and long-term (2075-2100). Under the high-emission SSP585 scenario, average temperatures are projected to rise by up to 3.76°C, and annual precipitation could increase by 52.6%, reaching up to 3446.38mm by the end of the century. The maximum temperature (Bio5) could reach 32.91°C, while the minimum temperature (Bio6) might rise by 4.43°C, particularly during winter. Precipitation seasonality (Bio15) is projected to increase by as much as 7.9% in the northwest, indicating heightened variability between wet and dry seasons. The diurnal temperature range (Bio2) is expected to decrease by up to - 1.3°C, signifying reduced nighttime cooling, which could exacerbate heat stress. Significant reductions in temperature seasonality (Bio4) are forecast for the northeast, with notable declines in isothermality (Bio3) under SSP585, pointing to increased climatic extremes. These climatic shifts pose severe risks to agricultural productivity, water resource availability, and biodiversity, particularly in flood-prone regions. The findings highlight the need for urgent adaptation measures, including improved flood management systems, efficient water resource use, and climate-resilient agricultural practices. By providing robust region-specific projections, this study offers critical insights for policymakers and stakeholders to mitigate the adverse effects of climate change and safeguard environmental and economic sustainability in Bangladesh.

Role of the Europe–China Pattern Teleconnection in the Interdecadal Autumn Dry–Wet Fluctuations in Central China

Based on statistical analyses of long-term reanalysis data, we have investigated the interdecadal variations of autumn precipitation in central China (APC-d) and the associated atmospheric teleconnection. It reveals that the increased autumn rainfall in central China during the last decade is a portion of the APC-d, which exhibits a high correlation coefficient of 0.7 with the interdecadal variations of the Europe–China pattern (EC-d pattern) teleconnection. The EC-d pattern teleconnection presents in a “+-+” structure over Eurasia, putting central China into the periphery of a quasi-barotropic anticyclonic high-pressure anomaly. Driven by positive vorticity advection and the inflow of warmer and moist air from the south, central China experiences enhanced ascending motion and abundant water vapor supply, resulting in increased rainfall. Further analysis suggests that the EC-d pattern originates from the exit of the North Atlantic jet and propagates eastward. It is captured by the Asian westerly jet stream and proceeds towards East Asia through the wave–mean flow interaction. The wave train acquires effective potential energy from the mean flow by the baroclinic energy conversion and simultaneously obtains kinetic energy from the basic westerly jet zones across the North Atlantic and the East Asian coasts. The interdecadal variation of the mid-latitude North Atlantic sea surface temperature (MAT-d) exhibits a significant negative relationship with EC-d, serving as a modulating factor for the EC-d pattern teleconnection. Experiments with CMIP6 models predict that the interdecadal variations in APC-d, EC-d, and MAT-d will maintain stable high correlations for the rest of the 21st century. These findings may contribute to forecasting the interdecadal autumn dry–wet conditions in central China.

Future Changes in Climate Extremes: Insights from CMIP6 Model Simulations for the Kagera River Sub-basin, Tanzania

Future Changes in Climate Extremes: Insights from CMIP6 Model Simulations for the Kagera River Sub-basin, Tanzania

Escalating threat of human-perceived heatwaves in Brazil

Abstract Heatwaves pose significant threats to socioeconomic and environmental systems, with their intensity and frequency expected to increase due to climate change. Despite their critical impacts, future heatwaves in Brazil remain underexplored, especially from a human-perceived perspective, which is crucial for assessing potential public health impacts. Here, we propose a method to assess heatwaves using the humidex (H) – a climate index that combines temperature and relative humidity to indicate human-perceived heat - alongside traditional temperature-based measures. Using bias-corrected simulations from 10 CMIP6 models under SSP2-4.5 and SSP5-8.5 scenarios, we quantified projected changes in heatwaves across Brazil. The results indicate that heatwaves will become more severe and prolonged, with greater changes under the SSP5-8.5 scenario by the end of the century, particularly in the North, Northeast, and Central regions. The magnitude of human-perceived heatwaves is expected to rise faster than temperature-based ones, underscoring the need for public health-focused assessments. CMIP6 models strongly agree on increased future heatwaves, potentially tripling population exposure in most Brazilian states, with the Southeast experiencing greater changes due to its larger population. These events are expected not only to affect more people but also to be more severe, exceeding over 60 days per year of serious danger (H > 45°C) by the end of the century under SSP5-8.5. Record-shattering events in the historical period are projected to become the norm by mid-century, highlighting the accelerating nature of these extreme events. Our findings emphasize the importance of considering human-perceived heat in climate impact studies and public health planning to mitigate potential impacts.

Assessing the Climatological Characteristics of Observed and Simulated Seasonal Onset of Precipitation Over Southern and Eastern Africa

ABSTRACTPrediction of seasonal onset is crucial to agriculture in southern and eastern Africa. Here, we applied two definitions of onset, namely meteorological and agricultural (crop‐germination), to evaluate CMIP6 models through the lens of rainfall onset over representative maize agricultural regions of South Africa, Tanzania, Malawi and Zambia. We use the ERA5 reanalysis as a proxy for observations, and robust regression to calculate a statistical comparison of the onset definitions for the period 1979–2021. Evaluation of ERA5 reanalysis shows similar magnitude and pattern as gauge based MSWEP. Our results show that, for meteorological onset, Johannesburg, with a subtropical highland climate, experienced earliest onset after 23 December; and an increasing trend (later onset) but not statistically significant (p = 0.2). Over Bethlehem, which has continental climate, the earliest onset date was after October 9 and an increasing interannual variability since 2000 is noted. The standard deviation of onset dates across the regions shows an East‐Central‐South gradient. We also found that the crop‐germination onset definition shows earlier onset of seasonal rains, it differs considerably across regions, and has higher interannual variability, in comparison with the meteorological definition. Over Lilongwe, Mbeya and Lusaka, late meteorological onset with a weak positive and insignificant trend is observed. The CMIP6 model's representation of onset trend differs from reanalysis data, with inter‐model differences. Late meteorological onset is underestimated by GFDL‐CM4 and MPI while INM5, MPI and NorESM overestimate the observed earliest onset. The largest bias is shown by INM and MPI which simulate earliest and latest onset as 190 (07 January) and 206 (23 January) respectively. In addition, models often fail to simulate sufficient precipitation to produce onset for seed germination and crop development. The ACCESS model showed an insignificant trend (p value = 2) and later onset over Lilongwe, an insignificant trend (p value = 0.9) over Lusaka, and an earlier onset over Mbeya. Using the agricultural onset definition, over Bethlehem, all the models and the ERA5 reanalysis did not produce enough precipitation to meet onset conditions. We suggest that rainfall onset studies use several definitions or metrics of onset and that the choice of metric be informed by the research question. Using such an ensemble of onset metrics contributes to a better understanding of variability and uncertainties in agricultural productivity.

Antarctic sea ice multidecadal variability triggered by Southern Annular Mode and deep convection

Antarctic sea ice exerts great influence on Earth’s climate by controlling the exchange of heat, momentum, freshwater, and gases between the atmosphere and ocean. Antarctic sea ice extent has undergone a multidecadal slight increase followed by a substantial decline since 2016. Here we utilize a 300-yr sea ice data assimilation reconstruction and two NOAA/GFDL and five CMIP6 model simulations to demonstrate a multidecadal variability of Antarctic sea ice extent. Stronger westerlies associated with the Southern Annular Mode (SAM) enhance the upwelling of warm and saline water from the subsurface ocean. The consequent salinity increase weakens the upper-ocean stratification, induces deep convection, and in turn brings more subsurface warm and saline water to the surface. This salinity-convection feedback triggered by the SAM provides favorable conditions for multidecadal sea ice decrease. Processes acting in reverse are found to cause sea ice increase, although it evolves slower than sea ice decrease.

Constraints on the Projected Tropical Pacific Sea Surface Temperature Warming Pattern by the Tropical North Atlantic Cold SST Bias in CMIP6 Models

AbstractReliable projections of the tropical Pacific sea surface temperature (SST) warming (TPSW) patterns are critically important for exploring the future climate change. However, climate models suffer from long‐standing common biases in simulating the present‐day climate, raising doubts about the model projected TPSW patterns. Here by using outputs from 30 CMIP6 models, we find the projected TPSW patterns are significantly correlated with the simulated present‐day SST in the tropical North Atlantic (TNA), with higher present‐day TNA SSTs tending to project more weakened zonal SST gradients by producing more present‐day low‐level clouds and the resultant positive cloud–shortwave–SST feedbacks over the eastern equatorial Pacific. An emergent constraint using observed TNA SST reveals a consistent El Niño‐like warming pattern in all models with more weakened zonal SST gradient than before in most models, together with a reduction of the inter‐model uncertainty in the zonal SST gradient change by more than 20%.

Mapping Climatic Regions of the Cerrado: General Patterns and Future Change

ABSTRACTThe Cerrado biome, renowned for its biodiversity and threatened by rapid land transformation, encompasses a vast savanna ecosystem in Brazil. The region is characterised by a seasonal climate, influenced by a myriad of meteorological systems creating diverse and non‐homogeneous rainfall regimes across the region. To account for this heterogeneity, we propose a novel classification of the Cerrado using rainfall data to delineate three distinct climatic regions: Eastern, Southern and Central‐West Cerrado. The Eastern region exhibited the driest and most seasonal climate, marked by high vapour pressure deficit (VPD) and predominantly open‐canopy vegetation. Conversely, the Southern region, characterised by lower seasonality, boasts a higher proportion of forest cover and lower mean VPD. The Central‐West region, encompassing diverse landscapes, featured areas with higher precipitation levels, particularly along the Amazônia border. Furthermore, we conducted trend analyses on observed station data and used CMIP6 models to evaluate future scenarios under differing emissions trajectories. While observed trends in mean rainfall were marginal, VPD demonstrated a notable upward trend of approximately 1% annually throughout the biome. Climate models indicated a substantial drying close to the Amazônia border, and wetter conditions in the southeast. All Cerrado regions are anticipated to experience amplified seasonality and VPD, with VPD projected to surge by approximately 30% (60%) under low (high) emissions scenarios by the end of the century. Notably, the transition from the dry to wet season was the most affected. Our study provides critical insights on how the climatic heterogeneity of the Cerrado shapes vegetation structure distribution and how future changes will exacerbate water stress throughout the biome. These findings underscore the importance of understanding climate variability for effective conservation and management strategies in the region.

A comprehensive evaluation of biases in convective-storm parameters in CMIP6 models over North America

A comprehensive evaluation of biases in convective-storm parameters in CMIP6 models over North America

Changes in the SST Seasonal Cycle in a Warmer North Pacific without Ocean Dynamical Feedbacks

Abstract Climate models project a significant intensification of the sea surface temperature (SST) seasonal cycle over the subpolar North Pacific due to global warming, with the shallower mixed layer widely recognized as the dominant factor. However, employing slab ocean experiments with only ocean–atmosphere thermal coupling, we find a substantial contribution from changes in surface heat flux to this seasonal cycle intensification. In particular, the stronger Newtonian cooling effect in winter acts as a more potent damping than in summer. This differential damping inhibits the warming in colder seasons, significantly contributing to the intensified SST seasonal cycle in the subpolar North Pacific. In addition, consistent phase shifts in the North Pacific are identified across CMIP6 models. In the northwest North Pacific, a phase advance is associated with anomalous heating in early spring, driven by enhanced warm atmospheric advection from lower latitudes and sea ice melting in marginal seas. In contrast, the southeast North Pacific exhibits a phase delay attributed to the anomalous cooling in spring relative to autumn. This cooling is due to weakened trade winds and increased presence of high clouds. The former leads to stronger evaporative cooling in spring, while the latter impedes shortwave radiation from reaching the ocean.

Depiction of Drought Over Africa in the Light of Changing Climate from CMIP6 Models

Depiction of Drought Over Africa in the Light of Changing Climate from CMIP6 Models

Subpolar North Atlantic Mean State Affects the Response of the Atlantic Meridional Overturning Circulation to the North Atlantic Oscillation in CMIP6 Models

Abstract The Atlantic meridional overturning circulation (AMOC) plays an important role in climate, transporting heat and salt to the subpolar North Atlantic. The AMOC’s variability is sensitive to atmospheric forcing, especially the North Atlantic Oscillation (NAO). Because AMOC observations are short, climate models are a valuable tool to study the AMOC’s variability. Yet, there are known issues with climate models, like uncertainties and systematic biases. To investigate this, preindustrial control experiments from models participating in the phase 6 of Coupled Model Intercomparison Project (CMIP6) are evaluated. There is a large, but correlated, spread in the models’ subpolar gyre mean surface temperature and salinity. By splitting models into groups of either a warm–salty or cold–fresh subpolar gyre, it is shown that warm–salty models have a lower sea ice cover in the Labrador Sea and, hence, enable a larger heat loss during a positive NAO. Stratification in the Labrador Sea is also weaker in warm–salty models, such that the larger NAO-related heat loss can also affect greater depths. As a result, subsurface density anomalies are much stronger in the warm–salty models than in those that tend to be cold and fresh. As these anomalies propagate southward along the western boundary, they establish a zonal density gradient anomaly that promotes a stronger delayed AMOC response to the NAO in the warm–salty models. These findings demonstrate how model mean state errors are linked across variables and affect variability, emphasizing the need for improvement of the subpolar North Atlantic mean states in models.

Projected Increasing Negative Impact of Extreme Events on Gross Primary Productivity During the 21st Century in CMIP6 Models

AbstractIn the context of increasingly frequent and severe climate extremes, an understanding of the impacts of these events on gross primary production (GPP) and thus on land carbon uptake is crucial. However, research utilizing new model outputs to assess the future trends, characteristics, and driving factors of GPP reduction associated with extreme events remains limited. Here, we use model outputs from Phase Six of the Coupled Model Intercomparison Project (CMIP6) to investigate the spatiotemporal patterns of negative GPP extreme events during the 21st century. We find a notable increase in negative GPP extremes globally under the SSP5‐8.5 scenario. They are characterized by longer durations and larger sizes, despite the smaller number of events. Under the SSP1‐2.6 scenario, while the total negative GPP extremes remain relatively stable, hotspots, including tropical forests, southern China, and boreal forest zones, still experience increases in negative extremes. By attributing these GPP extremes to climate conditions, we identified compound hot and dry conditions, which contributed to over 40% of the negative GPP extremes under both scenarios, as the dominant driver, followed by single‐driver dry conditions. Under SSP5‐8.5, the increasing contribution of compound hot and dry conditions leads to greater GPP reductions through prolonged and intensified negative extreme events. Compared with CMIP5 models, CMIP6 models project an asymmetry of negative and positive GPP extreme events that favors more negative extremes across most regions. Our findings highlight the escalating damage from climate extremes on future ecosystem productivity, emphasizing the urgent need for effective mitigation and adaptation actions.

Future photovoltaic potential in India: navigating the interplay between air pollution control and climate change mitigation

Abstract India has set ambitious solar energy targets to meet its climate commitments. However, climate change, already evident in the country, poses significant challenges to solar power generation. Therefore, assessing the impact of climate change on future photovoltaic potential in India is essential. This study evaluates the mid-century (2041-2050) solar photovoltaic potential across Indian power grids using CMIP6 models under two scenarios: SSP2-4.5 (moderate climate action with intermediate air pollution) and SSP5-8.5 (weak climate action with strong air pollution control). The results indicate that the nationally averaged photovoltaic potential is projected to decrease by -2.3±0.6% (SSP5-8.5) to -3.3±0.9% (SSP2-4.5) compared to the 1985-2014 baseline, primarily due to reduced radiation and increased temperatures. Additionally, cell temperatures are expected to rise by 1.5±0.13°C (SSP2-4.5) to 2±0.11°C (SSP5-8.5), leading to efficiency losses and additional 18±5 days under SSP2-4.5 (26±3 days under SSP5-8.5) of efficiency de-rating, particularly in solar-rich regions. This translates to a loss of 600±160 GWh under SSP2-4.5 and 840±100 GWh under SSP5-8.5, based on the solar generation status of 2023-24. Overall, SSP5-8.5 projects a smaller reduction in photovoltaic potential, it also predicts greater temperature-induced efficiency losses compared to SSP2-4.5, due to aerosol direct effect and weak climate action. Finally, this assessment highlights the need for combined climate and pollution mitigation efforts to boost India’s photovoltaic potential and secure a sustainable, resilient energy future.

Centennial‐Scale Variability of the Atlantic Meridional Overturning Circulation in CMIP6 Models Shaped by Arctic–North Atlantic Interactions and Sea Ice Biases

AbstractClimate variability on centennial timescales has often been linked to internal variability of the Atlantic Meridional Overturning Circulation (AMOC). However, due to the scarceness of suitable paleoclimate proxies and long climate model simulations, large uncertainties remain on the magnitude and physical mechanisms driving centennial‐scale AMOC variability. For these reasons, we perform a systematic multi‐model comparison of centennial‐scale AMOC variability in pre‐industrial control simulations of state‐of‐the‐art global climate models. Six out of nine models in this study exhibit a statistically significant mode of centennial‐scale AMOC variability. Our results show that freshwater exchanges between the Arctic Ocean and the North Atlantic provide a plausible driving mechanism in a subset of models, and that AMOC variability can be amplified by ocean–sea ice feedbacks in the Labrador Sea. The amplifying mechanism is linked to sea ice cover biases, which could provide an observational constraint for centennial‐scale AMOC variability.

Structural fluctuations of the Arctic Oscillation tied to the Atlantic Multidecadal Oscillation

The Arctic Oscillation (AO) has been observed to undergo distinct decadal structural fluctuations that significantly influence regional weather and climate. Understanding the drivers and mechanisms behind the AO’s spatial nonstationarity is critical for improving climate predictions related to the AO. We present evidence that the Atlantic Multidecadal Oscillation (AMO) plays a pivotal role in modulating AO’s Pacific center in recent decades. The poleward amplified cooling associated with negative AMO enhances the north-south temperature gradient and results the strengthened westerly winds and stratospheric polar vortex (SPV) responses, which reflects more planetary waves from the North Pacific to the North Atlantic. This enhances the atmospheric coupling between these regions and leads to a more pronounced Pacific center within the AO pattern. Numerical simulations from ECHAM5 and 35 CMIP6 models further corroborate the essential role of the AMO. These findings advance our understanding of the mechanisms driving the variability of the AO pattern.

Implications of CMIP6 Models‐Based Climate Biases and Runoff Sensitivity on Runoff Projection Uncertainties Over Central India

ABSTRACTAccurate runoff projections are vital for developing climate adaptation strategies, yet significant uncertainties persist. The commonly employed approaches to constrain these uncertainties rely on the stationarity of climate biases and runoff sensitivity, which may not hold for climate‐sensitive regions (e.g., semi‐arid regions). This study investigates the validity of the stationarity assumption across 29 CMIP6 models, encompassing diverse climate biases (Dry Warm, Wet Warm, Dry Cold, and Wet Cold), utilising a semi‐arid region in central India as a testbed. The implications of this assumption on runoff projection uncertainties were comprehensively assessed across the runoff modelling chain for three time periods (the 2030s, 2060s and 2090s) based on the Soil and Water Assessment Tool (SWAT) simulations. The results highlight the non‐stationary nature of climate biases and runoff sensitivity under future scenarios, challenging the widespread applicability of common uncertainty‐constraining approaches. Moreover, the impact of non‐stationarity on runoff projection uncertainty was found to be strongly influenced by the choice of GCMs, preprocessing methods and climate change scenarios. In the 2030s, GCMs dominate runoff uncertainty, with dry models exhibiting ~10%–15% higher uncertainty compared to warm models, which is further amplified when interacting with warm biases. However, from the mid‐century onwards, the bias‐adjustment approaches and climate change scenarios significantly shape runoff projection uncertainties under non‐stationary conditions. These findings emphasise the potential of climate bias and runoff sensitivity‐based GCM selection for reducing runoff uncertainty in near‐future assessment (2030s). For mid‐term and long‐term runoff projections, addressing diverse climate biases through bias‐adjustment approaches is more viable. This study offers critical insights to prioritise the development of a non‐stationarity‐based approach for reliable runoff projections in climate‐sensitive regions.

Recent trends in extratropical lows and their rainfall over Australia

Low pressure systems are an important source of rainfall in southern Australia, particularly deep lows that extend from the surface to at least 500 hPa. This paper uses multiple reanalyses to assess long-term trends in lows over the period 1959–2023, and identifies statistically significant decreasing trends in the number of surface low pressure systems near southern Australia during May–October, linked to a decrease in cyclogenesis near south-western Western Australia. Trends in lows at 500 hPa are also negative but weaker than at the surface, and are less consistent between reanalyses owing to less consistent observations through time. The spatial pattern of observed declines during the cool season is consistent with trends using eight CMIP6 models, but global climate models systematically underestimate the magnitude of the observed decline in surface lows. Trends in rainfall associated with lows are also shown, including assessing the sensitivity of trends to the specific years used. Despite well above average numbers of lows and enhanced rainfall during recent La Niña years 2020–2022, total rainfall from low pressure systems is declining during the cool season in south-east Australia. Trends in rainfall from lows are largest on the east coast, where they explain more than 70% of observed rainfall changes since the 1960s.