Coupled Model Intercomparison Project Phase 5 Models Research Articles

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

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number of days with precipitation exceeding 20 mm increases by more than 90 by the end of the century, compared to the historical reference period, under the SSP3-7.0 and SSP5-8.5 scenarios.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

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

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

Evaluation of global fire simulations in CMIP6 Earth system models

Abstract. Fire is the primary form of terrestrial ecosystem disturbance on a global scale and an important Earth system process. Most Earth system models (ESMs) have incorporated fire modeling, with 19 of them submitting model outputs of fire-related variables to the Coupled Model Intercomparison Project Phase 6 (CMIP6). This study provides the first comprehensive evaluation of CMIP6 historical fire simulations by comparing them with multiple satellite-based products and charcoal-based historical reconstructions. Our results show that most CMIP6 models simulate the present-day global burned area and fire carbon emissions within the range of satellite-based products. They also capture the major features of observed spatial patterns and seasonal cycles, the relationship of fires with precipitation and population density, and the influence of the El Niño–Southern Oscillation (ENSO) on the interannual variability of tropical fires. Regional fire carbon emissions simulated by the CMIP6 models from 1850 to 2010 generally align with the charcoal-based reconstructions, although there are regional mismatches, such as in southern South America and eastern temperate North America prior to the 1910s and in temperate North America, eastern boreal North America, Europe, and boreal Asia since the 1980s. The CMIP6 simulations have addressed three critical issues identified in CMIP5: (1) the simulated global burned area being less than half of that of the observations, (2) the failure to reproduce the high burned area fraction observed in Africa, and (3) the weak fire seasonal variability. Furthermore, the CMIP6 models exhibit improved accuracy in capturing the observed relationship between fires and both climatic and socioeconomic drivers and better align with the historical long-term trends indicated by charcoal-based reconstructions in most regions worldwide. However, the CMIP6 models still fail to reproduce the decline in global burned area and fire carbon emissions observed over the past 2 decades, mainly attributed to an underestimation of anthropogenic fire suppression, and the spring peak in fires in the Northern Hemisphere mid-latitudes, mainly due to an underestimation of crop fires. In addition, the model underestimates the fire sensitivity to wet–dry conditions, indicating the need to improve fuel wetness estimation. Based on these findings, we present specific guidance for fire scheme development and suggest a post-processing methodology for using CMIP6 multi-model outputs to generate reliable fire projection products.

Exploring the Relationship Between Upper Ocean States and the Falling Ice Radiative Effects using ECCO Product and Global Climate Models

Abstract This study seeks to explore the relationship between upper ocean current (UOC) anomalies (above 200 meters) and surface wind stress (TAU), focusing on the influence of falling ice (snow) radiative effects (FIREs) over the tropical and subtropical Pacific regions. To achieve this, we conducted sensitivity experiments with the CESM1-CAM5 model, using the Coupled Model Intercomparison Project phase 5 (CMIP5) historical run setting, with FIREs turned off (NOS) and on (SON). The monthly ocean current and temperature of the ocean reanalysis from the NASA Estimating the Circulation and Climate of the Ocean (ECCO) project, which assimilates satellite and in situ measurements, serve as a reference for this study. The spatial patterns of the horizontal UOC anomaly (UOCA) differences between the NOS and SON experiments show a strong correlation with the TAU patterns across the studied domain. When compared to the experiments with NOS, the experiments with SON demonstrate an improvement in the annual mean UOC. The improvement in UOC can be attributed to the enhancements in TAU, specifically in the trade-wind regions. The enhancements in TAU play a significant role in influencing the UOCA patterns and contribute to the overall improvement observed in the experiments with SON. In SON, the average absolute bias of simulated UOCA over the study area is reduced by up to 30% compared to NOS against ECCO. 
Although biases in UOC are present over the southern and northern flanks of the equator in SON, the improvements in annual mean ocean currents are closely related to enhancements in TAU driven by the inclusion of FIREs. Notably, stronger ocean current magnitudes correspond to more significant changes in TAU due to Coriolis forces. When evaluating the ensemble mean absolute biases of UOC from the CMIP5 models, similarities to NOS, however, are limited over the South Pacific region.

Influence of the Southern Hemisphere Supergyre on Antarctic Intermediate Water Properties in CMIP6 Models

AbstractThe supergyre in the Southern Hemisphere is thought to connect the Atlantic, Indian, and Pacific subtropical gyres together. The aim of the study is to investigate whether the supergyre is identifiable in the Coupled Model Intercomparison Project Phase 6 (CMIP6) models and in the Estimating the Circulation and Climate of the Ocean (ECCO) reanalysis and to evaluate the influence of the supergyre on the properties of Antarctic Intermediate Water (AAIW), the dominant water mass at intermediate depths in the Southern Hemisphere. CMIP6 models and ECCO are in agreement at the surface with supergyres connected across all basins but present some differences at depth in both position and strength. AAIW core properties (temperature and salinity) present a high degree of similarity across basins within the supergyre but not outside of it. By the end of the century, the supergyre reduces in size and intensifies at intermediate depths, and the AAIW core depth warms in all basins and freshens in the Pacific although no clear trend in salinity can be found in the Atlantic and Indian basins in the SSP5‐8.5 scenario. The high degree of similarity across basins within the supergyre is maintained in the future scenario. The results suggest that by connecting the basins together at intermediate depth, the supergyre plays a key role in circulating and homogenizing the AAIW core properties. Our results emphasize the role of the supergyre in circulating water masses at the surface and intermediate depths in CMIP6 models and hence its importance to the global circulation.

The Summer North Atlantic Oscillation, Arctic sea ice, and Arctic jet Rossby wave forcing.

We use Coupled Model Intercomparison Project Phase 6 (CMIP6) coupled and Atmospheric Model Intercomparison Project (AMIP) climate models, dynamical analyses, and observations to investigate interactions between summer Arctic sea ice concentration (SIC) variations and the Summer North Atlantic Oscillation (SNAO). Observations suggest that SIC-SNAO relationships mainly come from the East Siberian to Arctic Canada (ESAC) region where a weak atmospheric jet stream exists in summer. Twelve CMIP6 models with the most realistic atmospheric climatologies over the North Atlantic and Europe agree well with reanalyses on relationships between SIC and Northern Hemisphere atmospheric circulation. CMIP6 model data indicate that ESAC SIC influences the SNAO with a lead time of several weeks. However, AMIP simulations do not reproduce the observed atmospheric circulation when observed sea ice is prescribed. Rossby wave analyses do though support observed ESAC SIC influences on the SNAO. We conclude that ESAC Arctic SIC modestly influences the SNAO, and such investigations require the use of coupled models.

Processes Driving the Intermodel Spread of the Southern Hemisphere Hadley Circulation Expansion in CMIP6 Models

AbstractThe Hadley circulation (HC) has been expanding poleward in recent decades. The Coupled Model Intercomparison Project Phase 6 (CMIP6) models predict that the expansion will accelerate in the future, more so in the Southern Hemisphere (SH). However, the extent of the expansion varies widely among the models. We investigate the mechanisms driving the intermodel spread in SH HC expansion predictions. The intermodel spread is obtained by an empirical orthogonal function analysis on the SH HC trend patterns of 16 CMIP6 model simulations using the historical and shared socioeconomic pathway 5–8.5 scenarios. The leading mode, showing a mean meridional stream function anomaly at the poleward SH HC extent, explains 49.73% of the variance and significantly correlates (r = 0.94) with the SH HC expansion. By analyzing the extended Kuo‐Eliassen equation, we find that the intermodel difference in the representation of diabatic heating is responsible for about 14% of the intermodel spread. The meridional eddy momentum and heat fluxes contribute to about 21% and 18% of the intermodel spread, respectively. The models simulating a relatively large SH HC expansion tend to show increased precipitation in the Southern Pacific Convergence Zone, reduced baroclinic instability in the subtropics, and an enhanced poleward shift of jet stream in the midlatitudes. This suggests that the uncertainty in the HC projection may be constrained by reducing the bias in the trend of the mean fields.

Bayesian estimates for changes of the Russian river runoff in the 21st century as based on the CMIP6 model ensemble simulations

Based on ensemble calculations with the CMIP6 (Coupled Model Intercomparison Project, phase 6) climate models and using Bayesian averaging, an analysis was conducted on the changes in the 21st century runoff of several Russian rivers – the Volga, Ob, Yenisei, Lena, Amur, and Selenga. Bayesian weights considered the quality of models’ reproduction of runoff (long-term average runoff, linear runoff trend over the time interval with available runoff observations, interannual and interdecadal variability). The quality of runoff characteristics reproduction by individual models in the CMIP6 ensemble varies most significantly for the long-term average runoff, runoff trend, and, to a lesser extent, for interannual variability. In the 21st century, the ensemble average runoff increases for most of the analyzed rivers, except for the Volga. This increase is more pronounced under scenarios with larger anthropogenic impacts. It is especially significant for the SSP5-8.5 scenario (Shared Socioeconomic Pathways, 5-8.5), under which the runoff increase trend from 2015 to 2100 relative to its current long-term average is (10 ± 4)% for the Ob, (16 ± 3)% for the Yenisei, (39 ± 7)% for the Lena, (36 ± 7)% for the Amur, and (18 ± 6)% for the Selenga. The primary reason for the change in ensemble average runoff in the 21st century in models under all SSP scenarios is the change in precipitation. Accounting for differences in model quality in reproducing river runoff on average for 2015–2100 reduces inter-model deviations relative to the corresponding values with uniform weighting of model results by 6–26%, depending on the SSP scenario and river basin.

Tropical cyclone landfalls in the Northwest Pacific under global warming

AbstractThis study projects the changes in tropical cyclone (TC) landfalls in the western North Pacific under shared socioeconomic pathway (SSPs) scenarios during the TC peak season by using low‐resolution global climate models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6). Projections are based on the relationship between mid‐ and lower‐level atmospheric circulation and TC landfall frequency during the historical period from 1985 to 2014 and the future climate period from 2015 to 2100. The landfall areas for TCs are divided into northern East Asia (NEA), middle East Asia (MEA) and southern East Asia (SEA); the TC peak seasons are July–September for NEA and MEA, and July–November for SEA. To evaluate reproducibility, both ensemble and individual model outputs for mid‐ and lower‐level atmospheric circulations associated with TC landfall in each East Asian subregion are compared to the reanalysis. An ensemble of seven models with stable results for all three regions is more reasonable in simulating atmospheric circulation patterns than an ensemble of all CMIP6 models. The findings suggest that TC landfall is projected to increase by about 12% and 32% in NEA and MEA, respectively, in the late 21st century under the SSP5‐8.5 scenario compared to the historical period, while decreasing by 13% in SEA. These changes are consistent under both warming scenarios, and are more pronounced in the SSP5‐8.5 scenario compared to SSP1‐2.6, particularly in the later period of this century. An analysis of future atmospheric circulations suggests that global warming will weaken the western North Pacific subtropical high and cause its boundary to retreat eastward. This will lead to changes in the steering flow, which is closely related to TC tracks, resulting in TC landfalls to increase or decrease depending on the East Asian subregion.

Strong contribution from sensible heat to global precipitation increase in climate models is not supported by observational based data

It has previously been shown that trends in sensible heat from climate models have had a substantial contribution to global precipitation changes. We illustrate that this is the case also in the most recent Coupled Model Intercomparison Project Phase 6 (CMIP6). However, we find that over the period since 1980 reanalyses do not support the reduction in sensible heat from the CMIP6 models and rather estimate a global increase in sensible heat which would contribute to a precipitation reduction. Satellite data over a period of two decades over global ocean generally show an opposite sign of the sensible heat trend to the CMIP6 models, similarly to the reanalyses.

Forced trends and internal variability in climate change projections of extreme European windstorm frequency and severity

AbstractClimate change projections of European windstorm damages are highly uncertain because of different climate model responses and large internal variability. This study uses generalized linear models and a weighted median estimation to optimally extract forced trends in a number of European windstorm metrics. Footprints of windstorms associated with extratropical cyclones are created for an ensemble of models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) across a full transient time series from 1980 to 2100. Trends are assessed over time, but also as a function of global mean surface temperature changes. Trends in aggregate severity are attributed to changes in storm average severity, frequency, and area impacted, with changes in area being the dominant driver of changes to average storm severity. Confidence in the findings is assessed, with high confidence of declines in frequency for southern and northern Europe, medium confidence of an increase in average windstorm severity for parts of northwestern Europe, and low confidence of any changes for eastern Europe. A 15‐member ensemble of the MPI‐ESM1‐2‐LR model is used to assess internal variability. Trends between individual members can vary significantly; however, the uncertainty due to internal variability in the 15‐member ensemble is generally only 50% of that in the multimodel ensemble of CMIP6 models for aggregate severity. With largest uncertainty coming from model differences, a large proportion of uncertainty in future windstorms is therefore potentially reducible with modelling advances.

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.

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.

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.

Evaluation of CMIP6 models for sea surface temperature and sea surface salinity variability over the Arabian Sea

The capability of Coupled Model Inter-comparison Project Phase-6 (CMIP6) is analysed in simulating the seasonal variability of Sea Surface Temperature (SST) and Sea Surface Salinity (SSS) over the Arabian Sea in this study. The historical simulations of 11 CMIP6 models, which come from various source are validated with the observational data (HadISST for SST and SODA for SSS). This study covers the Arabian Sea, spanning 5°S to 32°N and 33°E to 80°E from 1985 to 2014 (30 years). Seasonal mean climatology and annual cycle of SST and SSS are estimated over the Arabian Sea. The deviation of coupled model-simulated SST and SSS from the observation is quantified by determining the biases during all the seasons. Four distinct model evaluation measures are used to access the performance of CMIP6 models: root mean square error (RMSE), standard deviation (STD), correlation coefficient (CC) and interannual variability skill score (IVS). CMCC-CM2-SR5, NESM3 and IITM-ESM models for SST yield CC/RMSE values of 0.988/0.174, 0.983/0.204 and 0.963/0.299, respectively. CMCC-CM2-SR5, IPSL-CM6A-LR and CanESM5 models for SSS yield CC/RMSE values of 0.956/0.035, 0.937/0.042 and 0.820/0.069, respectively. Thus, these models having higher CC and lower RMSE among all models, give better performance. The present study is important as the future scenarios can be predicted using the best models.

Projection of a winter ice-free Barents-Kara Sea by CMIP6 models with the CCHZ-DISO method

The winter sea ice over the Barents-Kara Sea (BKS) is reducing at an alarming rate, which impacts the Arctic shipping routes and the local ecosystems as well as the climate system across other regions. Consequently, it is imperative to project the winter ice-free state in this region to adequately prepare for future climate change and its impacts. However, most models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) show higher climatological values, weaker decreasing trends, and lower interannual variabilities in winter BKS sea ice concentration compared to observation. Additionally, it can be also seen that there exist great uncertainties in the projections of different models on whether the BKS region will be ice-free in future winters and the ice-free period, which spans almost the entire 21st century. Therefore, the study adopts two approaches developed from distance between indices of simulation and observation (DISO) method to project the winter ice-free period of the BKS under different emission scenarios. The results indicate that under SSP1–2.6, the winter BKS will not be ice-free by 2100. For SSP2–4.5, SSP3–7.0, and SSP5–8.5, the winter BKS is projected to be ice-free during 2076–2086, 2063–2068, and 2049–2061, respectively. By employing the DISO method, the projection uncertainty is reduced and the results highlight the urgency of reducing greenhouse gas emissions to delay the winter ice-free period over the BKS. These projections are intended to provide a reference for policymakers.

Aerosol climatology, variability, and trends over the Indo-Gangetic Plain in CMIP6 models

We evaluate the performance of Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating aerosol optical depth (AOD) climatology, variability, and trends over the Indo-Gangetic Plain (IGP) considered for the period of space-borne MODIS satellite observations during 2003–2014. The multi-model ensemble (MME) mean of CMIP6 models, is considered when better correlations (≥0.5) of models against the MODIS-derived AOD observations using standard statistical skill metrics. Analysis suggests that the CMIP6 MME successfully reproduces the spatial distribution of AOD climatology and its seasonal variability, albeit with some discrepancies. In particular, CMIP6 MME underestimates AOD spatial distributions across the IGP region (∼ bais varies between 0.2 and 0.4 during different seasons). CMIP6 MME captures coarse-mode aerosol distribution similar to AERONET over the IGP, while it has limited skill in capturing the fine-mode aerosol distribution, which could be due to uncertainties pertinent to anthropogenic aerosol emissions. Furthermore, the CMIP6 MME captures the seasonal evolution of aerosols over the IGP and its sub-regions (i.e., eastern and western IGP regions). Notably, the CMIP6 MME successfully captures the dominance among various aerosol species and their contributions to total AOD over the IGP. The CMIP6 MME mimic the observed AOD trends (varies between 0.01 and 0.03 year−1) well across the study regions, except the western IGP, where the MME is unable to replicate declining AOD trends during the pre-monsoon and monsoon seasons compared to MODIS. By conducting a principal component analysis on AOD data, it is apparent that the CMIP6 MME captures AOD variances that are comparable to MODIS observations, albeit with some discrepancies. This finding of the present research will help in policymaking, and mitigation strategies in the region.

Low-latitude mesopelagic nutrient recycling controls productivity and export.

Low-latitude (LL) oceans account for up to half of global net primary production and export1-5. It has been argued that the Southern Ocean dominates LL primary production and export6, with implications for the response of global primary production and export to climate change7. Here we applied observational analyses and sensitivity studies to an individual model to show, instead, that 72% of LL primary production and 55% of export is controlled by local mesopelagic macronutrient cycling. A total of 34% of the LL export is sustained by preformed macronutrients supplied from the Southern Ocean via a deeper overturning cell, with a shallow preformed northward supply, crossing 30° S through subpolar and thermocline water masses, sustaining only 7% of the LL export. Analyses of five Coupled Model Intercomparison Project Phase 6 (CMIP6) models, run under both high-emissions low-mitigation (shared socioeconomic pathway (SSP5-8.5)) and low-emissions high-mitigation (SSP1-2.6) climate scenarios for 1850-2300, revealed significant across-model disparities in their projections of not only the amplitude, but also the sign, of LL primary production. Under the stronger SSP5-8.5 forcing, with more substantial upper-ocean warming, the CMIP6 models that account for temperature-dependent remineralization promoted enhanced LL mesopelagic nutrient retention under warming, with this providing a first-order contribution to stabilizing or increasing, rather than decreasing, LL production under high emissions and low mitigation. This underscores the importance of a mechanistic understanding of mesopelagic remineralization and its sensitivity to ocean warming for predicting future ecosystem changes.

Projection of snowfall and precipitation phase changes over the Northwest China based on CMIP6 multimodels

The change of precipitation phase could have profound influence on water cycle and ecological environment in Northwest China (NWC). It has been observed that snowfall is inclined to transition into rainfall under climate warming. However, the evolution process of precipitation phase in the future over the NWC remains poorly understanding. In this study, the performance of snowfall simulated by Coupled Model Intercomparison Project Phase 6 (CMIP6) is evaluated by results of observation-based rain/snow separation, subsequently analyzing the changes in snowfall and precipitation phase under different climate scenarios over the NWC. The results indicated that most of CMIP6 models overestimate snowfall while underestimate the Snowfall to Precipitation Ratio (SPR) over the NWC. The performance of CMIP6 models is poorer in high-altitude mountainous region but relatively more robust in low-altitude areas. Superior performance of five models are selected for analysing changes of snowfall and precipitation phase in the future. Under the SSP1-2.6 scenario, the trend of changes in snowfall is not obviously in the future. Conversely, snowfall is projected to decline at rates of −1.04 mm·10a-1 and −2.94 mm·10a-1 under the SSP2-4.5 and SSP5-8.5 scenarios, respectively, with significant declines in high-altitude zones. The SPR will decrease at rates of −0.19 %·10a-1, −0.64 %·10a-1, and −1.50 %·10a-1 under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenario, respectively. The magnitudes in changes of snowfall and SPR show significant differences across sub-regions in the future, with low-altitude areas exhibiting upward trends in snowfall and SPR under certain conditions. Winter snowfall in the future shows an increased trend, while declines are observed in spring and autumn snowfall overall, with notable disparities between scenarios. The correlation between snowfall biases and biases of temperature and precipitation indicate that temperature is a pivotal factor contributing to precipitation phase bias. The findings are helpful for enhancing the simulation precision of hydrological processes and providing scientific strategies for future water resource management over the NWC.