Coupled Model Intercomparison Project Phase Research Articles

Pronounced spatial disparity of projected heatwave changes linked to heat domes and land-atmosphere coupling

Heatwaves are projected to substantially increase at a global scale, exacerbating worldwide heat-related risks in the future. However, understanding future heterogeneous heatwave changes and their origins remains challenging. By analyzing the output of various climate models from the Coupled Model Intercomparison Project Phase 6, we found pronounced spatial disparity of projected heatwave increases in the Northern Hemisphere, even outstretching seven-fold inter-regional differences in extreme heatwave occurrences, attributed primarily to future changes in heat-dome-like circulations and soil moisture–temperature coupling. Specifically, we found that by the end of the 21st century, the modulations of combined Pacific El Niño and positive Pacific Meridional Mode on magnified heat-dome-like circulations would be translated into summertime hotspots over western Asia and western North America. Amplified soil moisture–temperature couplings then further aggravate the heatwave intensity over these two hotspots. This study provides support for formulating impact-based mitigation strategies and efficiently addressing the potential future risks of heatwaves.

Projected changes in wet and dry extremes in the CMIP6 multi-model ensemble over the IGAD region of Eastern Africa

Wet and dry days, as well as wet and dry spells, are crucial pieces of information for rain-fed agriculture, food security, and numerous socioeconomic activities in East Africa. This study examines the projected changes in wet/dry days and wet/dry spells using data from the Multi-Model Ensemble (MME) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Statistical criteria and thresholds are used to project the changes in wet and dry days, as well as wet and dry spells, under the shared socioeconomic pathways (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for the annual, March–May (MAM), June–September (JJAS), and October-December (OND) seasons. The analysis indicates that the CMIP6 models generally overestimate the number of wet days (wet spells) across all seasons and scenarios at the 1 mm threshold. A projected 10–20% increase in total rainfall over the IGAD region is driven by an increase in rainfall intensity and the number of wet days under all scenarios during the near (2021–2050) and far (2071–2100) futures. A decrease in wet days and spells (increase in dry days and spells) is projected over South Sudan in JJAS, while an increase in wet spells is projected over Kenya, Somalia, and Sudan in OND during the near and far future compared to the baseline period. By the end of the century, wet days are projected to increase by approximately 20–30%, and dry spells are projected to decrease by 10–20% under the SSP1-2.6 and SSP5–8.5 scenarios. The EnsMean 10 CMIP6 tends to smooth out the extremes (wet or dry biases), creating a result that may appear more accurate but does not reflect the extremes or variations projected by individual models over South Sudan, Uganda, Kenya and Ethiopia. The projected patterns of wet and dry spells under SSP1-2.6 highlight the importance of mitigation efforts to limit temperature rise to below 1.5°. It is necessary to model how shifts in extreme dry and wet conditions will likely affect climate-sensitive sectors, such as agriculture and food security, in order to make well-informed decisions.

The influence of El Niño on springtime synoptic-scale precipitation extremes in Southeastern China: insights from CMIP6 model simulations

This study focuses on El Niño impacts on springtime extreme precipitation in Southeastern China (SEC) by comparing observations with data from the Coupled Model Intercomparison Project phase 6 (CMIP6) historical runs. Observational and simulated results suggest that synoptic-scale temperature advection patterns over East Asia (EA) are closely associated with extreme precipitation in SEC, encompassing the Pearl River Basin (PRB), Yangtze River Basin (YRB), and Huaihe River Basin (HRB). Based on this, we introduce a temperature advection index (TAI) tailored to capture the cold-warm temperature advection dipole, which shows a significant positive correlation with SEC precipitation. Both observations and CMIP6 indicate that TAI-related circulations, characterized by upper-level synoptic-scale waves and a north–south oriented temperature gradient over EA, are conducive to extreme precipitation in northern PRB (NPRB)–YRB–HRB. However, the TAI-related synoptic-scale activities have a lesser impact on extreme precipitation in southern PRB (SPRB), as these disturbances mainly affect the mid-latitude weather. Further investigation reveals that during boreal spring following El Niño, 85% of extreme events in YRB–HRB are associated with positive TAI values, compared to 76% under climatological conditions. However, such a change in the association with TAI is not evident in CMIP6 simulations. From observations, atmospheric baroclinicity along the East Asian westerly jet is enhanced during El Niño, which promotes the development of TAI-related synoptic-scale disturbances. In contrast, CMIP6 models struggle to reproduce these observed baroclinicity signals during El Niño. This challenge arises from the background westerly jet bias and mean-state cold tongue bias in tropical Pacific temperature in models.

Chile Niño/Niña in the coupled model intercomparison project phases 5 and 6

The north and central coast of Chile is influenced by El Niño-Southern Oscillation (ENSO) through oceanic and atmospheric teleconnections. However, it also experiences episodic oceanic warmings off central Chile (30°S) lasting a few months that are not necessarily associated with ENSO. These episodes, called “Chile Niño” events, besides their ecological and socio-economical impacts, have also the potential to influence tropical Pacific variability. Here, we investigate how realistically the models in the Coupled Model Intercomparison Project (CMIP, Phases 5 and 6) simulate Chile Niño/Niña (CN) events, and quantify their changes under anthropogenic forcing. Despite limitations of the global models in simulating realistically coastal upwelling dynamics, we show that they simulate reasonably well the observed spatial pattern, amplitude and seasonal evolution of CN events. They however fail to properly represent the positive skewness from observations. The analysis of a sub-group of models (36) that simulate ENSO realistically reveals that CN events increase in amplitude and variance in the future climate with no changes in their frequency of occurence. This is interpreted as resulting from compensating effects amongst changes in remote drivers and local feedbacks. In particular, ENSO variance increases while that of the South Pacific Oscillation decreases. Conversely, we found that while the Wind-Evaporation-SST feedback tends to increase and the coupling between mixed-layer depth and SST weakens, favoring the development of CN events, the thermocline and wind-SST feedbacks decrease. However, only the change in the thermocline feedback is correlated to changes in CN variance amongst the models, suggesting a dominant role of local oceanic stratification changes in constraining the sensitivity of CN to global warming.

Human activities increased compound drought and heat events over China during 1961–2014

AbstractCompared to independent drought or extreme heat events, compound drought and heat events (CDHEs) can pose more adverse impacts on ecosystems and human society. As the anthropogenic influence on CDHEs in China remains largely unquantified, we analyse both observed and simulated changes in the occurrence of CDHEs over China from 1961 to 2014, and perform detection and attribution analyses utilizing an optimal fingerprinting method, based on observations, reanalysis data and numerical experiments from seven Coupled Model Intercomparison Project Phase 6 (CMIP6) models. Upward trends are found in observed CDHEs during 1961–2014 over the majority of China, especially in the last two decades. The increase in CDHEs is largely attributed to human influence. Anthropogenic forcing is detectable in the observed CDHEs changes in the whole China and its eastern region. Furthermore, a three‐signal analysis indicates that greenhouse gases play a key role in increasing CDHEs over China, while natural forcing has a negligible effect. Anthropogenic aerosol emissions also play a detectable role in offsetting the greenhouse gas‐induced increase of CDHEs over China.

Advanced prediction of rice yield gaps under climate uncertainty using machine learning techniques in Eastern India

The current study focuses on applying machine learning approaches to forecast future Kharif rice yield gaps in eastern India while accounting for climate change implications. To achieve the United Nations Sustainable Development Goals (SDGs), food security must be prioritized. Rice yield has been predicted using Cubist, GBM, MARS, RF, SVM, and XGB machine learning methods, considering six factors: elevation, soil moisture, precipitation, temperature, soil temperature, and actual evapotranspiration. Climatic change scenarios were generated using the latest climatic Coupled Model Intercomparison Project Phase 6 (CMIP6 MIROC6) Shared Socioeconomic Pathways (SSP) 2–4.5 and SSP5-8.5 datasets between 1990 and 2030. Finally, machine learning algorithms were used to identify rice yield gaps to achieve sustainable agricultural intensification. The rice yield validation was completed with 1889 field-based farmer observation records. The results suggest that Murshidabad and Purba Bardhaman districts had very high rice yields (5.60–3.45 t/ha) when using the Cubist model compared to another model. The findings also reveal a poor rice-yielding zone (1.44–0.39 t/ha) in the western region (Purulia) and a northwestern region (half of the west of Birbhum). The Cubist and RF models' maximum and minimum R2 values were 0.73 and 0.72, respectively. The R2 values were also negligible for the XGB, GBM, SVM, and MARS, machine learning models. Projections for rice production in 2030 indicate that the northern and north-eastern regions (Murshidabad and Purba Bardhaman) as well as the southeastern areas (Jhargram and Paschim Medinipur) have the highest yields, categorized as extremely very high (5.56–3.49 t/ha) and high (3.49–2.49 t/ha). A significant rice yield gap (50-40 %) was found in the center and south-east areas (Bankura, Jhargram, and Paschim Medinipur), the northern region (Murshidabad and Birbhum), and the western region (Purulia). In 2030, the north-western region (Birbhum), as well as the middle and south-eastern regions (Bankura, Jhargram, and Paschim Medinipur districts), had the highest proportion of high (50%–40 %) and very high (60%–50 %) rice yield gap. Our findings can contribute to a new viewpoint on agricultural planning and management for sustainable growth in the face of changing climate circumstances.

Recent improvements and maximum covariance analysis of aerosol and cloud properties in the EC-Earth3-AerChem model

Abstract. Given the importance of aerosols and clouds and their interactions in the climate system, it is imperative that the global Earth system models accurately represent processes associated with them. This is an important prerequisite if we are to narrow the uncertainties in future climate projections. In practice, this means that continuous model evaluations and improvements grounded in observations are necessary. Numerous studies in the past few decades have shown both the usability and the limitations of utilizing satellite-based observations in understanding and evaluating aerosol–cloud interactions, particularly under varying meteorological and satellite sensor sensitivity paradigms. Furthermore, the vast range of spatio-temporal scales at which aerosol and cloud processes occur adds another dimension to the challenges faced when evaluating climate models. In this context, the aim of this study is two-fold. (1) We evaluate the most recent, significant changes in the representation of aerosol and cloud processes implemented in the EC-Earth3-AerChem model in the framework of the EU project FORCeS compared with its previous CMIP6 version (Coupled Model Intercomparison Project Phase 6; https://pcmdi.llnl.gov/CMIP6/, last access: 13 February 2019). We focus particularly on evaluating cloud physical properties and radiative effects, wherever possible, using a satellite simulator. We report on the overall improvements in the EC-Earth3-AerChem model. In particular, the strong warm bias chronically seen over the Southern Ocean is reduced significantly. (2) A statistical, maximum covariance analysis is carried out between aerosol optical depth (AOD) and cloud droplet (CD) effective radius based on the recent EC-Earth3-AerChem/FORCeS simulation to understand to what extent the Twomey effect can manifest itself in the larger spatio-temporal scales. We focus on the three oceanic low-level cloud regimes that are important due to their strong net cooling effect and where pollution outflow from the nearby continent is simultaneously pervasive. We report that the statistical covariability between AOD and CD effective radius is indeed dominantly visible even at the climate scale when the aerosol amount and composition are favourably preconditioned to allow for aerosol–cloud interactions. Despite this strong covariability, our analysis shows a strong cooling/warming in shortwave cloud radiative effects at the top of the atmosphere in our study regions associated with an increase/decrease in CD effective radius. This cooling/warming can be attributed to the increase/decrease in low cloud fraction, in line with previous observational studies.

Sketching the spatial disparities in heatwave trends by changing atmospheric teleconnections in the Northern Hemisphere

Pronounced spatial disparities in heatwave trends are bound up with a diversity of atmospheric signals with complex variations, including different phases and wavenumbers. However, assessing their relationships quantitatively remains a challenging problem. Here, we use a network-searching approach to identify the strengths of heatwave-related atmospheric teleconnections (AT) with ERA5 reanalysis data. This way, we quantify the close links between heatwave intensity and AT in the Northern Hemisphere. Approximately half of the interannual variability of heatwaves is explained and nearly 80% of the zonally asymmetric trend signs are estimated correctly by the AT changes in the mid-latitudes. We also uncover that the likelihood of extremely hot summers has increased sharply by a factor of 4.5 after 2000 over areas with enhanced AT, but remained almost unchanged over the areas with attenuated AT. Furthermore, reproducing Eastern European heatwave trends among various models of the Coupled Model Intercomparison Project Phase 6 largely depends on the simulated Eurasian AT changes, highlighting the potentially significant impact of AT shifts on the simulation and projection of heatwaves.

Multivariate bias correction and downscaling of climate models with trend-preserving deep learning

Global climate models (GCMs) and Earth system models (ESMs) exhibit biases, with resolutions too coarse to capture local variability for fine-scale, reliable drought and climate impact assessment. However, conventional bias correction approaches may cause implausible climate change signals due to unrealistic representations of spatial and intervariable dependences. While purely data-driven deep learning has achieved significant progress in improving climate and earth system simulations and predictions, they cannot reliably learn the circumstances (e.g., extremes) that are largely unseen in historical climate but likely becoming more frequent in the future climate (i.e., climate non-stationarity). This study shows an integrated trend-preserving deep learning approach that can address the spatial and intervariable dependences and climate non-stationarity issues for downscaling and bias correcting GCMs/ESMs. Here we combine the super-resolution deep residual network (SRDRN) with the trend-preserving quantile delta mapping (QDM) to downscale and bias correct six primary climate variables at once (including daily precipitation, maximum temperature, minimum temperature, relative humidity, solar radiation, and wind speed) from five state-of-the-art GCMs/ESMs in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found that the SRDRN-QDM approach greatly reduced GCMs/ESMs biases in spatial and intervariable dependences while significantly better-reducing biases in extremes compared to deep learning. The estimated drought based on the six bias-corrected and downscaled variables captured the observed drought intensity and frequency, which outperformed state-of-the-art multivariate bias correction approaches, demonstrating its capability for correcting GCMs/ESMs biases in spatial and multivariable dependences and extremes.

Association of precipitation extremes and crops production and projecting future extremes using machine learning approaches with CMIP6 data.

Precipitation extremes have surged in frequency and duration in recent decades, significantly impacting various sectors, including agriculture, water resources, energy, and public health worldwide. Pakistan, being highly susceptible to climate change and extremes, has experienced adverse events in recent times, emphasizing the need for a comprehensive investigation into the relationship between precipitation extremes and crops production. This study focuses on assessing the association between precipitation extremes on crops production, with a particular emphasis on the Punjab province, a crucial region for the country's food production. The initial phase of the study involved exploring the associations between precipitation extremes and crops production for the duration of 1980-2014. Notably, certain precipitation extremes, such as maximum CDDs (consecutive dry days), R99p (extreme precipitation events), PRCPTOT (precipitation total) and SDII (simple daily intensity index) exhibited strong correlations with the production of key crops like wheat, rice, garlic, dates, moong, and masoor. In the subsequent step, four machine learning (ML) algorithms were trained and tested using observed daily climate data (including maximum and minimum temperatures and precipitation) alongside model reference data (1985-2014) as predictors. Gradient boosting machine (GBM) was selected for its superior performance and employed to project precipitation extremes for three distinct future periods (F1: 2025-2049, F2: 2050-2074, F3: 2075-2099) under the SSP2-4.5 and SSP5-8.5 derived from the CMIP6 (Coupled Model Intercomparison Project Phase 6) archive. The projection results indicated an increasing and decreasing trend in CWDs (maximum consecutive wet days) and CDDs, respectively, at various meteorological stations. Furthermore, R10mm (the number of days with precipitation equal to or exceeding 10mm) and R25mm displayed an overall increasing trend at most of the stations, though some exhibited a decreasing trend. These trends in precipitation extremes have potential consequences, including the risk of flash floods and damage to agriculture and infrastructure. However, the study emphasizes that with proper planning, adaptation measures, and mitigation strategies, the potential losses and damages can be significantly minimized in the future.

Coupled model intercomparison project phase 6 (CMIP6) high resolution model intercomparison project (HighResMIP) bias in extreme rainfall drives underestimation of amazonian precipitation

Extreme rainfall events drive the amount and spatial distribution of rainfall in the Amazon and are a key driver of forest dynamics across the basin. This study investigates how the 3-hourly predictions in the High Resolution Model Intercomparison Project (HighResMIP, a component of the recent Coupled Model Intercomparison Project, CMIP6) represent extreme rainfall events at annual, seasonal, and sub-daily time scales. TRMM 3B42 (Tropical Rainfall Measuring Mission) 3 h data were used as observations. Our results showed that eleven out of seventeen HighResMIP models showed the observed association between rainfall and number of extreme events at the annual and seasonal scales. Two models captured the spatial pattern of number of extreme events at the seasonal and annual scales better (higher correlation) than the other models. None of the models captured the sub-daily timing of extreme rainfall, though some reproduced daily totals. Our results suggest that higher model resolution is a crucial factor for capturing extreme rainfall events in the Amazon, but it might not be the sole factor. Improving the representation of Amazon extreme rainfall events in HighResMIP models can help reduce model rainfall biases and uncertainties and enable more reliable assessments of the water cycle and forest dynamics in the Amazon.

Uncertainties Inherent from Large-Scale Climate Projections in the Statistical Downscaling Projection of North Atlantic Tropical Cyclone Activity

Abstract North Atlantic tropical cyclone (TC) activity under a high-emission scenario is projected using a statistical synthetic storm model coupled with nine phase 6 of the Coupled Model Intercomparison Project (CMIP6) climate models. The ensemble projection shows that the annual frequency of TCs generated in the basin will decrease from 15.91 (1979–2014) to 12.16 (2075–2100), and TC activity will shift poleward and coastward. The mean of lifetime maximum intensity will increase from 66.50 to 75.04 kt (1 kt ≈ 0.51 m s−1). Large discrepancies in TC frequency and intensity projections are found among the nine CMIP6 climate models. The uncertainty in the projection of wind shear is the leading cause of the discrepancies in the TC climatology projection, dominating the uncertainties in the projection of thermodynamic parameters such as potential intensity and saturation deficit. The uncertainty in the projection of wind shear may be related to the different projections of horizontal gradient of vertically integrated temperature in the climate models, which can be induced by different parameterizations of physical processes including surface process, sea ice, and cloud feedback. Informed by the uncertainty analysis, a surrogate model is developed to provide the first-order estimation of TC activity in climate models based on large-scale environmental features.

Estimating Uncertainty in Simulated ENSO Statistics

AbstractLarge ensembles of model simulations are frequently used to reduce the impact of internal variability when evaluating climate models and assessing climate change induced trends. However, the optimal number of ensemble members required to distinguish model biases and climate change signals from internal variability varies across models and metrics. Here we analyze the mean, variance and skewness of precipitation and sea surface temperature in the eastern equatorial Pacific region often used to describe the El Niño–Southern Oscillation (ENSO), obtained from large ensembles of Coupled model intercomparison project phase 6 climate simulations. Leveraging established statistical theory, we develop and assess equations to estimate, a priori, the ensemble size or simulation length required to limit sampling‐based uncertainties in ENSO statistics to within a desired tolerance. Our results confirm that the uncertainty of these statistics decreases with the square root of the time series length and/or ensemble size. Moreover, we demonstrate that uncertainties of these statistics are generally comparable when computed using either pre‐industrial control or historical runs. This suggests that pre‐industrial runs can sometimes be used to estimate the expected uncertainty of statistics computed from an existing historical member or ensemble, and the number of simulation years (run duration and/or ensemble size) required to adequately characterize the statistic. This advance allows us to use existing simulations (e.g., control runs that are performed during model development) to design ensembles that can sufficiently limit diagnostic uncertainties arising from simulated internal variability. These results may well be applicable to variables and regions beyond ENSO.

Climate-driven changes in drought metrics across the Yellow River Basin based on Coupled Model Intercomparison Project Phase 6

Climate-driven changes in drought metrics across the Yellow River Basin based on Coupled Model Intercomparison Project Phase 6

Climate‐Driven Projections of Future Global Wetlands Extent

AbstractWetlands are crucial components of the Earth's system, interacting with various processes such as the hydrological cycle, energy exchanges with the atmosphere, and global nitrogen and carbon cycles. The future trajectory of wetlands is anticipated to be influenced not only by direct human activities, but also by climate change. Here we present our assessment of climate‐driven global changes in wetlands extent, focusing on the main wetland complexes. We used an approach based on the Topographic Hydrological model (TOPMODEL) and soil liquid water content projections from 14 models of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Our analysis reveals a consistent decrease in wetlands extent in the Mediterranean, Central America, and northern South America, with a substantial loss of 28% in the western Amazon Basin for the end of the 21st century (2081–2100) under the SSP370 scenario. Conversely, Central Africa exhibits an increase in wetlands extent, except in the Congo Basin. Nevertheless, most of the areas studied (80%) present uncertain results, due to conflicting projections of changes between the models. Notably, we show that there is significant uncertainty among CMIP6 models regarding liquid soil water content in high latitudes. By narrowing our focus to 10 models, which seem to better represent the thawing of permafrost, we obtain a better inter‐model agreement. We then find a modest declines in the overall global area (<5%), but an average loss of 13% beyond 50°N. Specific areas like the Hudson Bay Lowlands experiencing a 21% decrease and the Western Siberian Lowlands a 15% decrease.

Assessing the influence of land use/land cover dynamics and climate change on water resources in Upper Blue Nile, Ethiopia

ABSTRACT This study evaluates the impact of climate and land use changes on the Lake Tana Basin's hydrology, using datasets on land use, weather patterns, topography, soil characteristics, and discharge. Future climate data were obtained from Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase Five (CMIP5) and generated using the Weather Generator (LARS-WG) tool from the Long Ashton Research Station with five distinct GCMs. Land use changes were projected using the Markov chain model based on cellular automata (CA). The Soil and Water Assessment Tool (SWAT) model was used to assess changes in hydrological elements between reference and future periods, with calibration and validation ensured by the Integrated Parameter Estimation and Uncertainty Analysis Tool (IPEAT). Projections indicate a 4.9 °C increase in ensemble mean annual temperature and a 16% rise in precipitation by the end of the 21st century under Representative Concentration Pathway (RCP) 8.5. Additionally, average annual hydrological components, including water yield, soil water, percolation, lateral flow, runoff, and actual and potential evapotranspiration, are expected to increase due to combined climate and land use changes. Therefore, it is crucial to fully understand these cumulative impacts before formulating and implementing water resource management strategies in the basin.

Historical and Future Asymmetry of ENSO Teleconnections with Extremes

Abstract El Niño-Southern Oscillation (ENSO) is the dominant source of climate variability globally. Many of the most devastating impacts of ENSO are felt through extremes. Here we present and describe a spatially complete global synthesis of extreme temperature and precipitation relationships with ENSO. We also investigate how these relationships evolve under a future warming scenario under high greenhouse gas emissions using fourteen models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble. Firstly, we demonstrate that models broadly capture observed ENSO teleconnections to means and extremes using the Twentieth Century Reanalysis version 3 (20CRv3). The models project that more regions will experience an amplification of the historical ENSO teleconnection with mean temperature and precipitation than a dampening under a high-emissions climate projection. The response of the ENSO teleconnection with extremes is very similar to the mean response, with even larger changes in some regions. Hence, regions that are predicted to experience an amplification of the ENSO teleconnection under future warming can also expect a comparable amplification in the intensity of extremes. Furthermore, models that suggest greater amplification of ENSO amplitude also tend to exhibit greater intensification of teleconnections. Future changes in regional climate variability may be better constrained if changes in ENSO itself are better understood.

Impact of projected climate variability on agricultural water demand of key crops in drought-prone central India region

ABSTRACT Water scarcity predominantly affects agriculture production. It is mandatory to save future water to prevent its shortage due to variations in climate. Variation in climate is a significant factor responsible for frequent droughts over the Bundelkhand region in central India. It is necessary to obtain early warning studies in this region to manage and arrange future agricultural water. Out of best Regional Climate Models (RCMs) under the Coordinated Regional Downscaling Experiment (CORDEX) and their driving Coupled Model Intercomparison Project Phase 5 (CMIP5), General Circulation Models (GCMs) for India, the most effective model has been found based on the observed multiscale data for the region. The daily projected data (2021–2100) of best GCM, Earth System Model, ESM-2M have been used to evaluate the future water requirements for major rabi and hot weather crops grown in the region. The climate change effect is seen in the majority, especially in the upper Bundelkhand part. Most of the crops will need more water than the present condition, especially from 2071 to 2100. The findings will also help researchers and policymakers to utilize the future projected data to propose suitable water management strategies for agriculture.

Effects of climate warming on energetics and habitat of the world's largest marine ectotherm

Responses of organisms to climate warming are variable and complex. Effects on species distributions are already evident and mean global surface ocean temperatures are likely to warm by up to 4.1 °C by 2100, substantially impacting the physiology and distributions of ectotherms. The largest marine ectotherm, the whale shark Rhincodon typus, broadly prefers sea surface temperatures (SST) ranging from 23 to 30 °C. Whole-species distribution models have projected a poleward range shift under future scenarios of climate change, but these models do not consider intraspecific variation or phenotypic plasticity in thermal limits when modelling species responses, and the impact of climate warming on the energetic requirements of whale sharks is unknown. Using a dataset of 111 whale shark movement tracks from aggregation sites in five countries across the Indian Ocean and the latest Earth-system modelling produced from Coupled Model Intercomparison Project Phase 6 for the Intergovernmental Panel on Climate Change, we examined how SST and total zooplankton biomass, their main food source, may change in the future, and what this means for the energetic balance and extent of suitable habitat for whale sharks. Earth System Models, under three Shared Socioeconomic Pathways (SSPs; SSP1–2.6, SSP3–7.0 and SSP5–8.5), project that by 2100 mean SST in four regions where whale shark aggregations are found will increase by up to 4.9 °C relative to the present, while zooplankton biomass will decrease. This reduction in zooplankton is projected to be accompanied by an increase in the energetic requirements of whale sharks because warmer water temperatures will increase their metabolic rate. We found marked differences in projected changes in the extent of suitable habitat when comparing a whole-species distribution model to one including regional variation. This suggests that the conventional approach of combining data from different regions within a species' distribution could underestimate the amount of local adaptation in populations, although parameterising local models could also suffer from having insufficient data and lead to model mis-specification or highly uncertain estimates. Our study highlights the need for further research into whale shark thermal tolerances and energetics, the complexities involved in projecting species responses to climate change, and the potential importance of considering intraspecific variation when building species distribution models.

Storylines of summer Arctic climate change constrained by Barents–Kara seas and Arctic tropospheric warming for climate risk assessment

Abstract. While climate models broadly agree on the changes expected to occur over the Arctic with global warming on a pan-Arctic scale (i.e. polar amplification, sea ice loss, and increased precipitation), the magnitude and patterns of these changes at regional and local scales remain uncertain. This limits the usability of climate model projections for risk assessments and their impact on human activities or ecosystems (e.g. fires and permafrost thawing). Whereas any single or ensemble mean projection may be of limited use to stakeholders, recent studies have shown the value of the storyline approach in providing a comprehensive and tractable set of climate projections that can be used to evaluate changes in environmental or societal risks associated with global warming. Here, we apply the storyline approach to a large ensemble of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models with the aim of distilling the wide spread in model predictions into four physically plausible outcomes of Arctic summertime climate change. This is made possible by leveraging strong covariability in the climate system associated with well-known but poorly constrained teleconnections and local processes; specifically, we find that differences in Barents–Kara sea warming and lower-tropospheric warming over polar regions among CMIP6 models explain most of the inter-model variability in pan-Arctic surface summer climate response to global warming. Based on this novel finding, we compare regional disparities in climate change across the four storylines. Our storyline analysis highlights the fact that for a given amount of global warming, certain climate risks can be intensified, while others may be lessened, relative to a “middle-of-the-road” ensemble mean projection. We find this to be particularly relevant when comparing climate change over terrestrial and marine areas of the Arctic which can show substantial differences in their sensitivity to global warming. We conclude by discussing the potential implications of our findings for modelling climate change impacts on ecosystems and human activities.