Coupled Model Intercomparison Project Phase Research Articles

Dynamic and Thermodynamic Contributions to Late 21st Century Projected Rainfall Change in the Congo Basin: Impact of a Regional Climate Model’s Formulation

Addressing the impacts of climate change requires, first of all, understanding the mechanisms driving changes, especially at the regional scale. In particular, policymakers and other stakeholders need physically robust climate change information to drive societal responses to a changing climate. This study analyses late 21st-century (2071–2100) precipitation projections for the Congo Basin under representative concentration pathway (RCP) 8.5, using the Rossby Centre Regional Climate Model (RCM) RCA4. Specifically, we examine the impact of the RCM formulation (reduction of turbulent mixing) on future change in seasonal mean precipitation by comparing the results of the modified model version (RCA4-v4) with those of the standard version (RCA4-v1) used in CORDEX (Coordinated Regional Climate Downscaling Experiment). The two RCM versions are driven by two global climate models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5). The results show that seasonal precipitation is largely affected by modifications in the atmospheric column moisture convergence or divergence, and, in turn, associated with changes in the dynamic (ΔDY) and thermodynamic (ΔTH) components of the moisture-budget equation. Projected decreased precipitation in the dry seasons (December–January–February and June–July–August) is linked to increased moisture divergence driven by dynamic effects (changes in circulation), with most experiments showing ΔDY as the main contributor (>60%) to the total moisture budget. Overall, precipitation is projected to increase in the wet seasons (March–April–May and September–October–November), which can be attributed to both dynamic and thermodynamic effects, but with a larger thermodynamic contribution (changes in specific humidity, ΔTH > 45%), compared to the dynamic one (ΔDY > 40%). Through a comparison of the two model versions, we found that the formulation (reducing turbulent mixing) and boundary conditions (driving GCM) strongly influence precipitation projections. This result holds substantial value for ensuring the fitness of models for future projections intended for decision-makers.

Improvement in the skill of CMIP6 decadal hindcasts for extreme rainfall events over the Indian summer monsoon region

Decadal climate predictions have been widely used to predict the near-term climate information relevant for decision-making at multi-year timescales. In the present study, we evaluate the quality of the Coupled Model Intercomparison Project phase-6 (CMIP6) Decadal Climate Prediction Project (DCPP) hindcasts in capturing the extreme rainfall events (EREs) over the monsoon core region during Indian summer monsoon season (June–September) up to lead years 1–10. For the first time, in this study, we have used quantile mapping approach to downscale and bias correct the DCPP CMIP6 simulation/hindcast rainfall for the better representation of EREs. Detailed analysis suggests that the models in general strongly underestimate the rainfall variability over the summer monsoon region. However, after the downscaling and bias correction, the representation of rainfall variability and intensity improved multifold. The bias-corrected decadal hindcasts in fact show ~ 80% improvement in capturing the frequency, intensity, and spatial distribution of rainfall associated with the EREs. Present study brought out a downscaled DCPP product, with potential prediction skill for EREs over India. It is important to highlight that the models predict an increase in the small and medium-area EREs as compared to the large-area EREs over the monsoon core region for the decade 2019–2028.

Strong aerosol cooling alone does not explain cold-biased mid-century temperatures in CMIP6 models

Abstract. The current generation of global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) exhibits a surprisingly cold-biased ensemble-mean mid-20th century global-mean surface temperature anomaly, compared to the previous generation Phase 5 (CMIP5) and to the observed mid-century (1940–1970) temperature anomaly. Most CMIP6 models, 31 of 36 models in contrast to 17 of 27 CMIP5 models, are colder than the uncertainty range of the observed anomaly, indicating that the CMIP6 suppressed warming is not caused by a few cold models. However, no clear cause that sufficiently explains the tendency towards suppressed mid-20th century warming emerges. Whereas models that best match observations exclusively exhibit weaker aerosol forcing than that exhibited by colder models, there is not a clear relationship between mid-century temperatures and aerosol forcing. Likewise, no systematic differences emerge among other model aerosol representations, such as inclusion of aerosol–cloud interactions for ice clouds in the model or the type of aerosol model input data set used, nor variations in greenhouse gas forcing or climate sensitivity, that could explain the suppressed warming. This indicates the presence of another cause, or more likely a set of causes, of the suppressed warming in many CMIP6 models. Thus, the prospect of a strong constraint on present-day aerosol forcing based on the mid-century warming is weakened, even if it is encouraging that those models that do match the observed warming best all have relatively weak aerosol forcing.

Investigating the Performance of CMIP6 Seasonal Precipitation Predictions and a Grid Based Model Heterogeneity Oriented Deep Learning Bias Correction Framework

AbstractClimate change is expected to alter the magnitude and spatiotemporal patterns of hydro‐climate variables such as precipitation, which has significant impacts on the ecosystem, human societies and water security. Global Climate Models are the major tools to simulate historical as well as future precipitation. However, due to imperfect model structures, parameters and boundary conditions, direct model outputs are subject to large uncertainty, which needs serious evaluation and bias correction before usage. In this study, seasonal precipitation predictions from 30 Coupled Model Inter‐comparison Project Phase 6 (CMIP6) models and Climate Research Unit observations are used to evaluate historical precipitation climatology in global continents during 1901–2014. A grid based model heterogeneity oriented Convolutional Neural Network (CNN) is proposed to correct the ensemble mean precipitation bias ratio. Besides, regression based Linear Scaling (LS), distribution based Quantile Mapping (QM) and spatial correlation CNN bias correction approaches are employed for comparison. Results of model performance evaluation indicate that generally precipitation prediction is more reliable in JJA than DJF on the global scale. Most models tend to have larger bias ratio for extreme precipitation. In addition, current CMIP6 models still have certain issues in accurate simulation of precipitation in mountainous regions and the regions affected by complex climate systems. Moreover, the proposed grid based model heterogeneity oriented CNN has better performance in ensemble mean bias correction than LS, QM, and spatial correlation CNN, which could consider the relative model performance and capture the features similar to actual climate dynamics.

Assessment of extreme rainfall events over the Indian subcontinent during the historical and future projection periods based on CMIP6 simulations

AbstractThe impact of climatic change on the summer monsoon season is studied to understand the rainfall pattern towards the end of the century utilizing the Coupled Model Intercomparison Project, Phase 6 (CMIP6) released by the World Climate Research Programme (WCRP). The analysis of model simulations from CMIP6 was carried out using 64 years of the historical period (1951–2014) and future projections till the end of the century (2015–2100). The models are compared with observed daily rainfall data from the APHRODITE (Asian Precipitation‐Highly‐Resolved Observational Data Integration Towards Evaluation). The analysis revealed that most of the models show an overestimation in the annual cycle of rainfall in the historical period; however, a few of them underestimate the values. The majority of them capture the onset signal of the summer monsoon in early June, along with a good seasonality in the daily rainfall climatology. The simulations that are coherent with the observational data sets are selected on the basis of the Taylor diagram for future projections in four scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5). The projections of the aforementioned scenarios are taken from the model outputs of EC‐Earth3‐Veg‐LR, INM‐CM4‐8, INM‐CM5‐0, MIROC‐ES2L and MPI‐ESM1‐2‐HR. The selected models exhibit far greater agreement among the 30 models when it comes to the features of rainfall during the summer monsoon. We have given more emphasis on summer monsoon rainfall in the historical and future projection periods since the trends are becoming more chaotic, as reported in observational studies. Over the Indian subcontinent, all of the chosen scenarios show an increased frequency of intense rainfall events with varying decadal and multidecadal features. Central India and west coastal belts are showing positive trends in extreme rainfall events towards the end of the century. At the turn of the century, the southern peninsular region experienced a decline in monsoon precipitation, whereas central India experienced an increase. The severity of rainfall variations during the monsoon season and trends in extremes are increasing as we move from the low‐emission scenario to the high‐emission scenario.

Increased drought and extreme events over continental United States under high emissions scenario

The frequency, severity, and extent of climate extremes in future will have an impact on human well-being, ecosystems, and the effectiveness of emissions mitigation and carbon sequestration strategies. The specific objectives of this study were to downscale climate data for US weather stations and analyze future trends in meteorological drought and temperature extremes over continental United States (CONUS). We used data from 4161 weather stations across the CONUS to downscale future precipitation projections from three Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase Six (CMIP6), specifically for the high emission scenario SSP5 8.5. Comparing historic observations with climate model projections revealed a significant bias in total annual precipitation days and total precipitation amounts. The average number of annual precipitation days across CONUS was projected to be 205 ± 26, 184 ± 33, and 181 ± 25 days in the BCC, CanESM, and UKESM models, respectively, compared to 91 ± 24 days in the observed data. Analyzing the duration of drought periods in different ecoregions of CONUS showed an increase in the number of drought months in the future (2023–2052) compared to the historical period (1989–2018). The analysis of precipitation and temperature changes in various ecoregions of CONUS revealed an increased frequency of droughts in the future, along with longer durations of warm spells. Eastern temperate forests and the Great Plains, which encompass the majority of CONUS agricultural lands, are projected to experience higher drought counts in the future. Drought projections show an increasing trend in future drought occurrences due to rising temperatures and changes in precipitation patterns. Our high-resolution climate projections can inform policy makers about the hotspots and their anticipated future trajectories.

Multi‐Objective Ensemble‐Processing Strategies to Optimize the Simulation of the Western North Pacific Subtropical High in Boreal Summer

AbstractThe western North Pacific Subtropical High (WNPSH) in boreal summer is a major atmospheric player affecting East Asian climate, but its simulation in state‐of‐the‐art climate models is still largely biased. Here we use a multi‐objective optimization strategy, the Pareto optimality, to incorporate multiple physical constraints in processing multi‐model simulations provided by the Coupled Model Intercomparison Project Phase 6. We aim to improve the simulation of WNPSH by this practice. Sea surface temperatures from three tropical oceanic basins are found highly related to WNPSH, and thus used as constraints. We also present an ameliorated strategy, which takes a subset of the raw Pareto optimality by imposing conditions of smallest errors. Results show that the overestimate of WNPSH is effectively corrected. The two multi‐objective optimization schemes both perform better than the traditional approach, revealing the importance of implementing physically based links in processing multi‐model ensemble simulations.

Persistent Ocean Anomalies as a Response to Northern Hemisphere Heating Induced by Biomass Burning Variability

Abstract Biomass burning aerosol (BBA) emissions in the Coupled Model Intercomparison Project phase 6 (CMIP6) historical forcing fields have enhanced temporal variability during the years 1997–2014 compared to earlier periods. Recent studies document that the corresponding inhomogeneous shortwave forcing over this period can cause changes in clouds, permafrost, and soil moisture, which contribute to a net terrestrial Northern Hemisphere warming relative to earlier periods. Here, we investigate the ocean response to the hemispherically asymmetric warming, using a 100-member ensemble of the Community Earth System Model version 2 Large Ensemble forced by two different BBA emissions (CMIP6 default and temporally smoothed over 1990–2020). Differences between the two subensemble means show that ocean temperature anomalies occur during periods of high BBA variability and subsequently persist over multiple decades. In the North Atlantic, surface warming is efficiently compensated for by decreased northward oceanic heat transport due to a slowdown of the Atlantic meridional overturning circulation. In the North Pacific, surface warming is compensated for by an anomalous cross-equatorial cell (CEC) that reduces northward oceanic heat transport. The heat that converges in the South Pacific through the anomalous CEC is shunted into the subsurface and contributes to formation of long-lasting ocean temperature anomalies. The anomalous CEC is maintained through latitude-dependent contributions from narrow western boundary currents and basinwide near-surface Ekman transport. These results indicate that interannual variability in forcing fields may significantly change the background climate state over long time scales, presenting a potential uncertainty in CMIP6-class climate projections forced without interannual variability.

Grassland Greening and Water Resource Availability May Coexist in a Warming Climate in Northern China and the Tibetan Plateau

AbstractGreening of Northern China and the Tibetan Plateau (NCTP) has been observed by increases in the remotely sensed leaf area index (LAI), driven primarily by CO2 fertilization effects, anthropogenic warming, and the implementation of ecological restoration programs. Continued growth of LAI throughout the 21st century is also projected by the Coupled Model Intercomparison Project Phase 6 (CMIP6) scenarios. However, the question of whether local water resources can sustain ongoing grassland greening has not been adequately investigated. Here we assessed the sustainability of water resources under grassland greening across NCTP under various climate scenarios using water yield (WY, defined as precipitation minus actual evapotranspiration) as the key metric. Unexpectedly, we observe the coexistence of increases in LAI and WY in most of NCTP. In a warming climate with increasing precipitation and CO2, we find that grasses maintain high water use efficiency to sustain their growth, contributing to continued local water resource availability. Thus, livestock production may also continue to increase under the simultaneous growth of LAI and WY in the future.

Differences Between the CMIP5 and CMIP6 Antarctic Sea Ice Concentration Budgets

AbstractCompared to the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models, the Antarctic sea ice area (SIA) has been improved in Phase 6 (CMIP6). However, the lack of knowledge about the reliability of sea ice dynamic and thermodynamic processes in the CMIP6 models still limits the accuracy of Antarctic sea ice projections. Here, by using a novel and systematic statistical metric, the performance of CMIP5 and CMIP6 models with near‐realistic SIAs was assessed. We found improvements in CMIP6 models relative to CMIP5. Moreover, forcing the sea ice‐ocean model with atmospheric reanalysis led to excessive ice convergence compared to the fully coupled ocean‐sea ice‐atmosphere model, although the SIA bias could be much smaller. This prevalent insufficient ice divergence in the models is highly correlated with the negative ice thickness bias, highlighting the importance of ice thickness in the correct simulation of sea ice dynamics.

Role of anthropogenic forcing and atmospheric circulation in a low-temperature event in February 2022 in eastern China

In February 2022, eastern China experienced prolonged low temperatures, with significant impacts on agriculture, transportation, and power supply. The regional average temperature anomaly for February 2022 was −0.65 °C, making it the third coldest February from 2000 to 2022. Understanding the influence of anthropogenic forcing and atmospheric circulation on low-temperature events is important for climate change adaptation and mitigation in the region. This study used observational and model data from the Coupled Model Intercomparison Project Phase 6 to conduct an extreme event attribution study. The results showed that anthropogenic forcing led to a 1.5-fold decrease in the probability of 2022-like events, with anthropogenic greenhouse gases being the primary contributors to this reduction. Conversely, aerosols slightly increased the likelihood of such events. To evaluate the role of atmospheric circulation in this event, we chose Siberian High as the key circulation system and employed circulation similarity, intensity classification, and regression methods to estimate the impacts of circulation patterns, circulation intensity, and human-induced circulation change. The results demonstrated that under the influence of the same anthropogenic forcing, the presence of an anomalous Siberian High and anomalously enhanced intensity of the Siberian High both increased the likelihood of such a cold event occurrence. However, anthropogenic forcing exerts a negligible effect on such cold events via anomalous circulation.

Climate Change Will Increase Biomass Proportion of Global Forest Carbon Stocks Under an SSP5–8.5 Climate Trajectory

AbstractA large amount of carbon is stored in global forests. However, the fraction of carbon stored as plant biomass versus soil organic carbon (SOC) varies among forest types, and potential changes over the 21st century are uncertain. Here, we used extensive data derived from inventories and remote sensing and Coupled Model Intercomparison Project Phase 6 (CMIP6) models to examine the current and 21st century dynamics in the proportion of biomass and SOC across global forests. We found that precipitation, elevation, soil, and wildfire were the primary controls of these differences in carbon pools. Under the SSP5–8.5 climate scenario, CMIP6 models project that the ratio of biomass to ecosystem carbon in global forests will increase across the 21st century, with the largest increases in boreal forests (95 ± 37%) compared to moist tropical forests (16 ± 15%). Changes in forest carbon pools resulting in greater biomass fraction will affect disturbance, and ecosystem carbon and energy balances, all of which interact with the climate system.

Unique ocean circulation pathways reshape the Indian Ocean oxygen minimum zone with warming

Abstract. The global ocean is losing oxygen with warming. Observations and Earth system model projections, however, suggest that this global ocean deoxygenation does not equate to a simple and systematic expansion of tropical oxygen minimum zones (OMZs). Previous studies have focused on the Pacific Ocean; they showed that the outer OMZ deoxygenates and expands as oxygen supply by advective transport weakens, the OMZ core oxygenates and contracts due to a shift in the composition of the source waters supplied by slow mixing, and in between these two regimes oxygen is redistributed with little effect on OMZ volume. Here, we examine the OMZ response to warming in the Indian Ocean using an ensemble of Earth system model high-emissions scenario experiments from the Coupled Model Intercomparison Project Phase 6. We find a similar expansion–redistribution–contraction response but show that the unique ocean circulation pathways of the Indian Ocean lead to far more prominent OMZ contraction and redistribution regimes than in the Pacific Ocean. As a result, only the outermost volumes (oxygen>180 µmol kg−1) expand. The Indian Ocean experiences a broad oxygenation in the southwest driven by a reduction in waters supplied by the Indonesian Throughflow in favor of high-oxygen waters supplied from the southern Indian Ocean gyre. Models also project a strong localized deoxygenation in the northern Arabian Sea due to the rapid warming and shoaling of marginal sea outflows (Red Sea and Persian Gulf) and increases in local stratification with warming. We extend the existing conceptual framework used to explain the Pacific OMZ response to interpret the response in the Indian Ocean.

Interdecadal Variations of Radiative Feedbacks Associated With the El Niño and Southern Oscillation (ENSO) in CMIP6 Models

AbstractInternally generated radiative feedback at interannual timescales can influence climate sensitivity estimates. This study has clarified the mechanisms of variability in radiative feedback associated with the El Niño and Southern Oscillation based on preindustrial control simulations for Coupled Model Intercomparison Project Phase 6. Radiative feedback showed large modulation with a comparable magnitude to the intermodel uncertainty associated with internal changes in the zonal gradient of the ocean thermocline and sea surface temperature (SST) in the equatorial Pacific on interdecadal timescales. When the interdecadal SST anomalies in the eastern and western equatorial Pacific were simulated as positive and negative, respectively, the statistical properties of the interannual variations were also modified with diminished Walker circulation changes per unit of global mean surface temperature rise. Meanwhile, the enhanced local Hadley circulation led to a middle‐to upper‐level cloud decrease and a low‐level cloud increase in the subtropics, causing enhanced negative radiative feedback as a global average.

Comparison of ocean deoxygenation between CMIP models and an observational dataset in the North Pacific from 1958 to 2005

This study investigated the relationship between the observed and simulated dissolved oxygen (O2) inventory changes in the North Pacific by analyzing an observational dataset and the outputs of Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5/6) between 1958 and 2005. A total of 204 ensembles from 20 models were analyzed. Many of the models in the North Pacific subarctic region have higher climatological O2 concentrations than observed at deeper water depths. Therefore, the negative trend of O2 inventories tends to be larger, and in fact, several model ensemble members have a larger negative trend in O2 inventories than observed. The variability among model ensemble members is more influenced by the uncertainty due to internal variability than by the uncertainty resulting from model dependency. An inter-model empirical orthogonal function (EOF) analysis revealed that the different simulated magnitudes of the negative O2 trend is closely associated with the first EOF mode, and ensemble members with strong negative trends are characterized by large oxygen reduction in the subarctic North Pacific, especially around the boundaries between the North Pacific Ocean and the Sea of Okhotsk as well as the Bering Seas. The modeled strong O2 decrease in the subarctic North Pacific is consistent with the spatial pattern of the observed O2 trend. Further analysis of climate models indicated that the O2 decrease in the subarctic region was primarily caused by physical factors. This conclusion is supported by the significantly high correlation is present between the potential temperature and O2 inventory trend in the subarctic region, whereas an insignificant correlation coefficient is present between dissolved organic carbon and the O2 inventory trend. However, the observations have a larger ratio of O2 inventory trend to temperature trend than any of the ensembles, and thus the relationship between O2 and temperature change in the subarctic North Pacific seen in the CMIP5/6 simulations is not exact.

Projected Climate Change Effects on Global Vegetation Growth: A Machine Learning Approach

In this study, a machine learning model was used to investigate the potential consequences of climate change on vegetation growth. The methodology involved analyzing the historical Normalized Difference Vegetation Index (NDVI) data and future climate projections under four Shared Socioeconomic Pathways (SSPs). Data from the Global Inventory Monitoring and Modeling System (GIMMS) dataset for the period 1981–2000 were used to train the machine learning model, while CMIP6 (Coupled Model Intercomparison Project Phase 6) global climate projections from 2021–2100 were employed to predict future NDVI values under different SSPs. The study results revealed that the global mean NDVI is projected to experience a significant increase from the period 1981–2000 to the period 2021–2040. Following this, the mean NDVI slightly increases under SSP126 and SSP245 while decreasing substantially under SSP370 and SSP585. In the near-term span of 2021–2040, the average NDVI value of SSP585 slightly exceeds that of SSP245 and SSP370, suggesting a positive vegetation development in response to a more pronounced temperature increase in the near term. However, if the trajectory of SSP585 persists, the mean NDVI will commence a decline over the subsequent three periods (2041–2060, 2061–2080, and 2080–2100) with a faster speed than that of SSP370. This decline is attributed to the adverse effects of a rapid temperature rise on vegetation. Based on the examination of individual continents, it is projected that the NDVI values in Africa, South America, and Oceania will decline over time, except under the scenario SSP126 during 2081–2100. On the other hand, the NDVI values in North America and Europe are anticipated to increase, with the exception of the scenario SSP585 during 2081–2100. Additionally, Asia is expected to follow an increasing trend, except under the scenario SSP126 during 2081–2100. In the larger scope, our research findings carry substantial implications for biodiversity preservation, greenhouse gas emission reduction, and efficient environmental management. The utilization of machine learning technology holds the potential to accurately predict future changes in vegetation growth and pinpoint areas where intervention is imperative.

Crop yield increments will enhance soil carbon sequestration in coastal arable lands by 2100

Coastal lands are crucial arable land reserves that play a vital role in ensuring future global food security. In arable coastal lands, low soil quality often results in limited crop yield and soil organic carbon (SOC), making them promising sites for enhancing both soil carbon sequestration and agricultural production. However, a scarcity of long-term site experiments has led to a research gap regarding SOC sequestration in coastal arable lands, leaving potential uncertainties due to variations in planting systems, crop yields, and the influence of climate change. In this study, we focused on the Yellow River Delta (YRD) in China, which is a typical coastal agricultural area. We verified the adaptability of the Denitrifcation-Decomposition (DNDC) model in coastal farmlands based on an 11-year long-term observation experiment and continuous sampling survey data. We further explored the spatiotemporal changes of regional SOC stocks under two Shared Socio-Economic Pathways (SSPs) scenarios based on five Global Climate Models (GCMs) derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6), along with three yield increase scenarios from 2020 to 2100. The results indicated that promoting single-cotton cultivation in coastal saline–alkali lands would result in at least 88.2% of farmland being converted into carbon sinks. In contrast, promoting the cultivation of a wheat–maize rotation system can promote regional SOC sequestration as a whole, with a maximum increase of 31.90 Mg C ha−1. By 2100, the SOC stocks decreased in cotton and wheat–maize rotation farmland by 3.9% and 11.6%, respectively, in the SSP585 scenario compared with that in the SSP126 scenario. Except for farmlands with wheat-maize rotation cropping system under the SSP126 scenario, the SOC stocks in all other scenarios are projected to reach their theoretical maximum values before 2050. Additionally, when reaching the North China Plain (S1) and national (S2) high-yield levels of farmland, the SOC stocks in cotton farmland by 2100 increased by 16.7% and 21.6%, respectively, whereas the SOC stocks in wheat-maize farmland increased by 1.8% and 4.8%, respectively. Our study revealed the mutually beneficial relationship between yield increment and SOC sequestration in coastal farmlands under climate change, as well as the potential for future SOC sequestration. We provided scientific support for land management and climate change mitigation strategic decision-making in coastal saline–alkali farmlands.

Climate Change Effects on Rainfall Intensity–Duration–Frequency (IDF) Curves for the Lake Erie Coast Using Various Climate Models

This study delved into the analysis of hourly observed as well as future precipitation data in the towns of Willoughby and Buffalo on the Lake Erie Coast to examine the variations in IDF relationships over the 21st century. Several regional climate models (RCMs) and general circulation models (GCMs) from the Coupled Model Intercomparison Project (CMIP) Phases 5 and 6 were used. The study evaluated three RCMs with historical and Representative Concentration Pathway (RCP) 8.5 scenarios for each CMIP5 and three GCMs with historical and Shared Socioeconomic Pathways (SSPs) (126, 245, 370, and 585) scenarios for each CMIP6. The results suggested that the town of Willoughby would experience an increase of 9–46%, whereas Buffalo would experience an upsurge of 6–140% in the hourly precipitation intensity under the worst-case scenarios of RCP8.5 for CMIP5 and SSP585 for CMIP6. This increase is expected to occur in both the near (2020–2059) and far future (2060–2099), with a return period as low as 2 years and as high as 100 years when compared to the baseline period (1980–2019). The analysis indicated an increased range of 9–39% in the near future and 20–55% in the far future for Willoughby, while the Buffalo region may experience an increase of 2–95% in the near future and 3–192% in the far future as compared to the baseline period. In contrast to CMIP6 SSP585 models, CMIP5 RCP8.5 models predicted rainfall with an intensity value that is up to 28% higher in the town of Willoughby, while the reverse was true for the Buffalo region. The findings of this study are expected to be helpful for the design of water resource infrastructures.

Uncertainty separation of drought projection in the 21st century using SMILEs and CMIP6

Reliable projection of future drought conditions is critical for informing adaptation in response to future climate change. However, the uncertainty associated with drought projection impedes the precise estimation of drought risk and consequently efficient adaptation strategy. To better understand the role of uncertainty in drought projection, this study distinguished the total uncertainty of drought projection into scenario uncertainty, model uncertainty, and internal variability uncertainty by variance analysis using realizations from the Single Model Initial-condition Large Ensembles (SMILEs) and the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results showed that SMILEs differ from CMIP6 mainly in the magnitude of internal variability and model uncertainty. The contribution of internal variability to total drought projection uncertainty in SMILEs (31–56 %) is greater than that in CMIP6 (9–21 %), while model uncertainty of SMILEs (36–41 %) is nearly half that of CMIP6 (67–76 %). The estimation of total uncertainty in drought projection is comparable between SMILEs and CMIP6, indicating that total uncertainty attains a minimum in the mid-21st century (the 2060 s for SMILEs and 2050 s for CMIP6). In addition, SMILEs and CMIP6 consistently show that model uncertainty is dominant in tropical regions, and scenario uncertainty is the main uncertainty contributor in western North America, eastern South America, the Mediterranean, and southern Australia.

Assessing Arctic wetting: Performances of CMIP6 models and projections of precipitation changes

The Arctic region is experiencing a notable increase in precipitation, known as Arctic wetting, amidst the backdrop of Arctic warming. This phenomenon has implications for the Arctic hydrological cycle and numerous socio-ecological systems. However, the ability of climate models to accurately simulate changes in Arctic wetting has not been thoroughly assessed. In this study, we analyze total precipitation in the Arctic using station data, multiple reanalyses, and 35 models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). By employing the moisture budget equation and an evaluation method for model performance with ERA5 reanalysis as a reference, we evaluated the models' capability to reproduce past Arctic wetting patterns. Our findings indicate that most reanalyses and models are able to replicate Arctic wetting. However, the CMIP6 models generally exhibit an overestimation of Arctic wetting during the warm season and an underestimation during the cold season from 1979 to 2014 when compared to the ERA5 reanalysis. Further investigation reveals that the overestimation of wetting during the warm season is largest over the Arctic Ocean's northern part, specifically the Canadian Arctic Archipelago, and is associated with an overestimation of atmospheric moisture transport. Conversely, the models significantly underestimate wetting over the Barents-Kara Sea during the cold season, which can be attributed to an underestimation of evaporation resulting from the models' inadequate representation of sea ice reduction in that region. The models with the best performance in simulating historical Arctic wetting indicate a projected intensification of Arctic wetting, and optimal models significantly reduce uncertainties in future projections compared to the original models, particularly in the cold season and oceanic regions. Our study highlights significant biases in the CMIP6 models' simulation of Arctic precipitation, and improving the model's ability to simulate historical Arctic precipitation could reduce uncertainties in future projections.