Multi-model Ensemble Mean Research Articles

Quantifying the Contribution of Internal Atmospheric Drivers to Near-Term Projection Uncertainty in September Arctic Sea Ice

Abstract Arctic sea ice has undergone rapid loss in all months of the year in recent decades, especially in September. The September sea ice extent (SSIE) in the multimodel ensemble mean of climate models shows a large divergence from observations since the 2000s, which indicates the potential influence of internal variability on SSIE decadal variations. Reasons previously identified for the accelerated decrease in SSIE are largely related to the tendency toward a barotropic geopotential height rise in summer over the Arctic. We used a 40-member ensemble of simulation by the Community Earth System Model version 1 (CESM1) and a 100-member ensemble simulation by the Max Planck Institute Earth System Model (MPI-ESM) to reveal that the internal variability of the local atmosphere circulation change can contribute 12%–17% to the uncertainties in the projected SSIE changes during 2016–45 in both CESM-LE and MPI-ESM. The tropical Pacific Ocean may act as a remote driver for the sea ice melting but the coupling between them is more intense on decadal time scales than that on year-to-year scales. Our quantitative estimation of the contribution of the internal atmospheric circulation to SSIE during the next three decades may be underestimated due to models’ inability to capture the observed Rossby wave train originating from the tropical Pacific Ocean propagating into the Arctic. Further efforts toward investigating causes of the model limitations and quantifying the contribution of local and remote component to Arctic sea ice on different time scales may help to improve the future sea ice prediction.

Upper-Tropospheric Temperature Pattern Over the Asian-Pacific Region in CMIP6 Simulations: Climatology and Interannual Variability

Temperature is a most important indicator for climate change. However, compared to surface air temperature, relatively less attention has been shown to the upper-tropospheric temperature (UTT). Given that the Asian-Pacific UTT plays a remarkable role in the climate system, its future change deserves great attention. In this study, based on the Coupled Model Intercomparison Project phase 6 (CMIP6) simulations, the fidelity of 30 CMIP6 models on the Asian-Pacific UTT patterns was evaluated and their future changes under the scenarios of the Shared Socioeconomic Pathway (SSP) 2–4.5 and 5–8.5 were projected. The evaluation indicates that the CMIP6 models have a good capacity to reproduce the climatology and interannual variability of seasonal UTT during 1965–2014, with the multi-model ensemble mean (MME) outperforming individual models. The observed seesaw oscillation between the Asian UTT and the North Pacific UTT during four seasons, named Asian-Pacific Oscillation (APO), is also well performed. The MME projects a similar spatial change under both scenarios in the second half of the 21st century, with lager changes in magnitude under SSP5-8.5 than under SSP2-4.5. Compared to 1965–2014, during 2050–2099, spring, summer and autumn UTTs are projected to cool (warm) in a widespread area of Asia (the North Pacific). The projected winter UTT decreases in East Asia and most of the North Pacific. In addition, an increased interannual variability of seasonal UTT is anticipated particularly in the mid-low latitudes of the Asian-Pacific sector. The APO phenomenon is expected to still be dominant in the future climate, but its intensity (interannual variability) tends to weaken (enlarge) in each season as compared to the current.

Quantitative evaluations of subtropical westerly jet simulations over East Asia based on multiple CMIP5 and CMIP6 GCMs

As a salient feature of the Asian monsoon system, the East Asian subtropical westerly jet (EASWJ) exerts significant impacts on weather and climate changes in China and even throughout East Asia. In this paper, we applied a new self-adaptive algorithm to detect the EASWJ, identify its boundaries, and then represent its characteristics by defining three indices: the intensity index, meridional displacement index, and width index. Compared to the reanalysis data, we carried out a comprehensive, objective, and quantitative EASWJ evaluation using historical experiments from multiple global climate models (GCMs) in the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6). The results show that the multimodel ensemble mean (MME) of both CMIP5 and CMIP6 can simulate the characteristics of winter EASWJ well. While for the other three seasons, the MME of both phase models underestimate the 200-hPa zonal wind (U200) strength in the jet coverage area and overestimate the U200 outside the jet area, such simulation results weaken the meridional shear of the wind field. The EASWJ simulations from the CMIP5 GCMs had no consistent intensity or location characteristic tendencies, and most CMIP5 GCMs tended to simulate relatively wide-coverage jets. In contrast, most CMIP6 GCMs are inclined to simulate significantly weaker, wider, and more-northward jets. Compared to the predecessors in CMIP5, about half of CMIP6 GCMs significantly minimized the jet intensity bias, but remarkable errors were still observed in their jet location and coverage representations. Furthermore, the comparative analysis performed by classifying models based on their evaluated simulation results suggested that the simulated performance of the meridional temperature gradient was important for capturing the EASWJ characteristics. Further in-depth study of the causes of model differences is warranted to improve simulation results.

Projected global warming-induced terrestrial ecosystem carbon across China under SSP scenarios

Terrestrial ecosystem carbon sequestration is one of the most economically feasible and important ways to mitigate the increase of atmospheric CO2 concentration. China's terrestrial ecosystems have a huge carbon sequestration potential. Therefore, the study on the carbon sequestration potential of vegetation ecosystems is related to the smooth implementation of China's carbon neutrality strategy in 2060. Based on the CMIP6 shared socioeconomic pathways (SSPs) scenarios, changes in the vegetation aboveground biomass carbon (ABC) were estimated using the Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ) model (1981–2060) for China. Subsequently, the vegetation ABC dynamics were analyzed under three future scenarios based on the traditional Multi-Model Ensemble Mean (MME) method for different eco-regions in China. We found that the vegetation ABC density was 32.38 Mg/ha in China with an increasing trend of 53.99% from 1981 to 2014. The vegetation ABC density was higher in sub-regions I, II, V, and VI. Due to the main forest carbon sinks was appeared in Southwest, Northeast and Southeast of China. However, the water conditions are poor in sub-regions III, VII, and VIII, and mostly covered by desert and steppe vegetation where the productivity level is low. Under SSP1-2.6 and SSP2-4.5 scenarios, the vegetation ABC would show a decreasing trend from 2015 to 2040 with a reduction rate of 4.34% and 4.60%, but exhibit an increasing trend from 2041 to 2060 with a growth rate of 1.46% and 0.97%. While the vegetation ABC would show a decreasing trend during 2015 to 2060 with a reduction rate of 14.78% under SSP5-8.5 scenarios. Additionally, temperature and precipitation are main factors influencing the vegetation ABC. In the future scenario, due to higher temperatures resulted in the decrease of vegetation ABC in the cold temperate humid and temperate humid/sub-humid regions. However, in the northwestern arid region and Qinghai-Tibet Plateau region may have been due to relatively low temperatures in the region, warming may address this limitation and lead to a general increase in ABC.

Characteristics analysis of drought at multiple spatiotemporal scale and assessment of CMIP6 performance over the Huaihe River Basin

Study regionThe Huaihe River Basin is located in China’s north-south climate transition belt, where massive droughts occurred in history. Study focusTo comprehensively capture how climate extremes have behaved in the past, this paper analyzed the trend, change point, and periodicity of drought at multiple spatial-temporal scales based on the Standardized Precipitation Indexes (SPI). And the historical simulation experiments of 9 CMIP6 models were evaluated against observations for further exploring the behavior of climate extremes in future periods. New hydrological insights for the regionThe period of SPI-12 lies around 2–7 years, while the period of SPI-3 is at the time scale of 1–3 years, indicating potential teleconnections with large-scale climate events. Moreover, MIROC6 and CESM2 models exhibited better performance in simulating precipitation than individual models and multi-model ensemble mean. The uncertainties of the model ensemble mean of 3 selected models in depicting droughts have a good consistency with the observations. The projection experiments of CMIP6 derived from the selected historical simulation models can be used to predict drought in the near future and the far future under different scenarios over the Huaihe River Basin.

Evaluating observed and future spatiotemporal changes in precipitation and temperature across China based on CMIP6‐GCMs

AbstractThe present study aimed to evaluate the performance of 46 global climate models (GCMs) from the newly released Coupled Model Intercomparison Project Phase 6 (CMIP6) in the historical simulation of precipitation and temperature, and select the best performing GCMs for future projection across China and three major river basins. This study uses four shared socioeconomic pathways (SSPs), namely SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5 relative to the base period (1961–2014). Initially, 46 models were evaluated across China employing an improved Taylor diagram method. Based on relatively better performance, 10 best‐performing models (TBMs) were selected out of 46 models for further evaluation. The results show that historical temperature was well reproduced by CMIP6 over the study regions with a high correlation coefficient (CC). All the TBMs produced good CC ranging from 0.8 to 0.99 presenting the precipitation and temperature distribution well. Meanwhile, EC‐Earth3 and EC‐Earth3‐Veg well simulated the precipitation and temperature amounts as well as trends over selected three river basins. The multimodel ensemble mean (MME) underestimates temperature over China and selected three basins with bias values of −0.53, −0.21, −0.91, and −0.68°C, respectively. In contrast, MEM overestimated the simulated precipitation with the amount of 27.7, 32.4, 21.0, and 104.6% across China and selected three basins. During future projections, increased precipitation and temperature trends are projected over three selected river basins as well as all across China. The increasing trend of future precipitation over China under SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5 scenarios are 0.65, 0.86, 1.29, and 0.76 mm·a−1, whereas, the increasing trend of temperature is 0.008, 0.028, 0.050, and 0.065°C·a−1, respectively. Comparatively, the greater the radiation force, the higher projected increases in precipitation and temperature across China and three major river basins were observed. The extent of CMIP6 models over the target region and its river basins calls for further deep assessment of the attribution and possible implementation of robust methods that can accurately simulate the observed patterns for future practice.

RoCliB– bias‐corrected CORDEX RCMdataset over Romania

AbstractFour climate parameters (i.e. maximum, mean and minimum air temperature and precipitation amount) from 10 regional climate models, provided by the EURO‐CORDEX initiative, are adjusted using as reference the ROCADA gridded dataset. The adjustment was performed on a daily temporal resolution for the historical period (1971–2005), as well as for climate change scenarios based on two Representative Concentration Pathways (RCP4.5 and RCP8.5). The most accurately method for bias‐correction (BC) was selected following a 2‐fold cross‐validation approach, which was performed on historical data using two methods: Quantile Mapping (QMAP) and Multivariate Bias Correction with N‐dimensional probability (MBCn). The performances of the two methods are very similar when analysing the frequency distribution of each selected variable, whereas the comparison between (1) the intervariables correlation of the adjusted datasets, and (2) the reference dataset revealed much smaller differences for the dataset adjusted with the multivariate method, hence this was used for producing the BC scenario dataset. Based on the MBCn adjusted dataset, a climate change analysis over Romania was tested at the seasonal and annual scale. Overall, for the multimodel ensemble mean, at the country level, a substantial temperature increase is reported for both scenarios and no significant trend is revealed for precipitation amount. The adjusted RCMs are provided without any restrictions via an open‐access repository in netCDF CF‐1.4‐compliant file format. The BC climate models are archived at the 0.1° spatial resolution, in the WGS‐84 coordinate system, at a daily temporal resolution.

Projected changes in meteorological drought over East Africa inferred from bias-adjusted CMIP6 models.

The ongoing global warming has caused unprecedented changes in the climate system, leading to an increase in the intensity and frequency of weather and climate extremes. This study uses the sixth phase of Coupled Model Intercomparison Project (CMIP6) data to investigate projected changes in drought events over East Africa (EA) under four Shared Socioeconomic Pathway (SSP) emission scenarios (SSP1-2.6, SSP2-4.5, SSP3-4.0, and SSP5-8.5). The CMIP6 data are bias-corrected using a quantile mapping method, with the Climatic Research Unit's precipitation dataset as reference. Drought is quantified using the standardized precipitation index and different measures of drought are estimated: drought duration, drought frequency, drought severity, and drought intensity. Evaluating the accuracy and reliability of historical data before and after bias correction demonstrates the importance of the approach. The overall distribution after bias correction depicts a close agreement with observation. Moreover, the multi-model ensemble mean demonstrate superiority over individual Global Circulation Models. Projected future changes show enhancement in precipitation over most parts of EA in the far future under different SSP scenarios. However, the arid and semi-arid regions are expected to receive less amount of precipitation, whereas the highlands and lake regions are expected to receive a larger amount of precipitation increase. Furthermore, the dry areas of EA are likely to experience more frequent drought events with longer duration, stronger intensity, and severity in the far future. Overall, this study identifies possible drought hotspots over EA, enabling early preparation for such events.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11069-022-05341-8.

The Characteristics and Evaluation of Future Droughts across China through the CMIP6 Multi-Model Ensemble

Understanding historical and future drought patterns is crucial to acclimation and the mitigation of drought. The negative impact of China’s droughts on the social economy has attracted attention; however, there is still no comprehensive or long-term monitoring pattern for future droughts. Here we evaluated the precipitation and temperature simulation capability of Coupled Model Intercomparison Project Phase 6 (CMIP6) and evaluated the temporal and spatial pattern of droughts during 1961–2099 across China. The results show that the multi-model ensemble mean (MME) is more representative of the observed precipitation and temperatures across China than the single climate model. China experienced an overall drying trend in the historical period. After 1991, the drought frequency (DF), drought duration (DD), and drought intensity (DI) in the northwest of the Inland River Basin and in the Yangtze River Basin increased significantly. Compared with the historical period, China will suffer more frequent drought events, although the DD and DI will be weakened under SSP1-2.6 and SSP2-4.5, while China will experience longer DD and more serious drought events under SSP3-7.0 and SSP5-8.5. The Hai River Basin and Huai River Basin are expected to have more serious drought trends in summer. Compared with historical periods, the drought trend will increase by 2.9–5.7 times and 1.1–4.2 times, respectively. The results can be used for decision making regarding future drought control.

Contributions of Anthropogenic Aerosol Forcing and Multidecadal Internal Variability to Mid‐20th Century Arctic Cooling—CMIP6/DAMIP Multimodel Analysis

AbstractIn the Arctic, observed decadal mean surface air temperatures (SATs) were 0.70°C–0.95°C lower around 1970 than those around 1940. The 35‐multimodel ensemble mean of historical simulations in the Coupled Model Intercomparison Project Phase 6 (CMIP6) exhibited Arctic surface cooling trend in 1940–1970, which could be attributed to external forcings. Multimodel ensemble means of CMIP6 Detection and Attribution Model Intercomparison Project historical simulations exhibited Arctic surface cooling of −0.22°C (±0.24°C) in 1970 versus 1940 and showed that anthropogenic aerosol forcings contributed to a cooling of −0.65°C (±0.37°C), which was partially offset by a warming of 0.44°C (±0.22°C) due to well‐mixed greenhouse gases. In addition to the anthropogenic aerosol forcings, multidecadal internal variability with a magnitude of 0.47°C was the component primarily contributing to the observed Arctic cooling. We identified a spatial pattern of pan‐Arctic multidecadal cooling due to the internal variability that resembles the 1940–1970 cooling pattern.

How well do the CMIP6 models simulate dust aerosols?

Abstract. Mineral dust impacts key processes in the Earth system, including the radiation budget, clouds, and nutrient cycles. We evaluate dust aerosols in 16 models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) against multiple reanalyses and observations. We note that both the reanalyses and observations used here have their limitations and particularly that dust emission and deposition in reanalyses are poorly constrained. Most models, and particularly the multi-model ensemble mean (MEM), capture the spatial patterns and seasonal cycles of global dust processes well. However, large uncertainties and inter-model diversity are found. For example, global dust emissions, primarily driven by model-simulated surface winds, vary by a factor of 5 across models, while the MEM estimate is double the amount in reanalyses. The ranges of CMIP6 model-simulated global dust emission, deposition, burden, and optical depth (DOD) are larger than previous generations of models. Models present considerable disagreement in dust seasonal cycles over North China and North America. Here, DOD values are overestimated by most CMIP6 models, with the MEM estimate 1.2–1.7 times larger compared to satellite and reanalysis datasets. Such overestimates can reach up to a factor of 5 in individual models. Models also fail to reproduce some key features of the regional dust distribution, such as dust accumulation along the southern edge of the Himalayas. Overall, there are still large uncertainties in CMIP6 models' simulated dust processes, which feature inconsistent biases throughout the dust life cycle between models, particularly in the relationship connecting dust mass to DOD. Our results imply that modelled dust processes are becoming more uncertain as models become more sophisticated. More detailed output and dust size-resolved variables in particular, relating to the dust cycle in future intercomparison projects, are needed to enable better constraints of global dust cycles and enable the potential identification of observationally constrained links between dust cycles and optical properties.

Deciphering the projected changes in CMIP-6 based precipitation simulations over the Krishna River Basin

AbstractThe impact of climate change on the Krishna River Basin (KRB) is significant due to the semi-arid nature of the basin. Herein, 21 global climate models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) were examined to simulate the historical monthly precipitation over the 1951–2014 period in the KRB. The symmetrical uncertainty (SU) method and the multi-criteria decision method (MCDM) were employed to select the suitable GCMs for projecting possible changes in precipitation over the KRB. The biases in the climate projections were removed by using the empirical quantile mapping method. The reliability ensemble averaging (REA) method was used to generate the multi-model ensemble (MME) mean of projections and to analyse the spatio-temporal changes of precipitation under different shared socioeconomic pathways (SSPs). BCC-CSM2-MR, IPSL-CM6A-LR, MIROC6, INM-CM5-0, and MPI-ESM1-2-HR were found to be the most suitable GCMs for the KRB. The MME mean of the chosen GCMs showed significant changes in precipitation projection that occurs for a far future period (2071–2100) over the KRB. The projection changes of precipitation range from −36.72 to 83.05% and −37.68 to 95.75% for the annual and monsoon periods, respectively, for various SSPs. Monsoon climate projections show higher changes compared with the annual climate projections, which reveals that precipitation concentration is more during the monsoon period over the KRB.

The Mediterranean climate change hotspot in the CMIP5 and CMIP6 projections

Abstract. The enhanced warming trend and precipitation decline in the Mediterranean region make it a climate change hotspot. We compare projections of multiple Coupled Model Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6) historical and future scenario simulations to quantify the impacts of the already changing climate in the region. In particular, we investigate changes in temperature and precipitation during the 21st century following scenarios RCP2.6, RCP4.5 and RCP8.5 for CMIP5 and SSP1-2.6, SSP2-4.5 and SSP5-8.5 from CMIP6, as well as for the HighResMIP high-resolution experiments. A model weighting scheme is applied to obtain constrained estimates of projected changes, which accounts for historical model performance and inter-independence in the multi-model ensembles, using an observational ensemble as reference. Results indicate a robust and significant warming over the Mediterranean region during the 21st century over all seasons, ensembles and experiments. The temperature changes vary between CMIPs, CMIP6 being the ensemble that projects a stronger warming. The Mediterranean amplified warming with respect to the global mean is mainly found during summer. The projected Mediterranean warming during the summer season can span from 1.83 to 8.49 ∘C in CMIP6 and 1.22 to 6.63 ∘C in CMIP5 considering three different scenarios and the 50 % of inter-model spread by the end of the century. Contrarily to temperature projections, precipitation changes show greater uncertainties and spatial heterogeneity. However, a robust and significant precipitation decline is projected over large parts of the region during summer by the end of the century and for the high emission scenario (−49 % to −16 % in CMIP6 and −47 % to −22 % in CMIP5). While there is less disagreement in projected precipitation than in temperature between CMIP5 and CMIP6, the latter shows larger precipitation declines in some regions. Results obtained from the model weighting scheme indicate larger warming trends in CMIP5 and a weaker warming trend in CMIP6, thereby reducing the difference between the multi-model ensemble means from 1.32 ∘C before weighting to 0.68 ∘C after weighting.

Historical and future runoff changes in the Yangtze River Basin from CMIP6 models constrained by a weighting strategy

Based on the ERA5-Land datasets from 1981–2020, a decadal oscillation has been found in the variation of summer runoff in the middle and lower reaches of the Yangtze River Basin (MLYRB). The oscillation suggests that the MLYRB will experience increased runoff in the next few decades after 2020, which saw a record high runoff in the MLYRB. The decadal changes in summer runoff over the MLYRB under various climate change scenarios are then analyzed with direct runoff outputs from 28 general circulation models participating in the sixth phase of the Coupled Model Intercomparison Project. Given that the equal-weighted multi-model ensemble mean could not well represent the historical runoff changes in the MLYRB, in this paper we introduce a model weighting scheme that considers both the model skill and independence. It turns out that this scheme well constrains the models to represent the observed decadal changes of summer runoff. The weighted mean projections suggest that the summer runoff in the MLYRB during 2015–2100 under all warming scenarios will be higher than the present day; and 2021–2040 is likely to be a period with significantly increased summer runoff. Results of the present study have great implications for flood control and effective water resources management over the MLYRB in the future, and the weighting approach used in this paper can be applied to a wide range of projections at both regional and global scales.

Climate change impact assessment in a tropical headwater basin

Changes in precipitation and air temperature may produce different impacts on the hydrological regime, compromising water supply. This study focuses on climate change impacts in the Verde River Basin (VRB), a tropical headwater basin in southeast Brazil, located in the state of Minas Gerais. The Variable Infiltration Capacity model (VIC) was calibrated and validated in the Verde River Basin. The downscaling (Eta Regional Climate Model, at 20-km resolution) of three Global Circulation Models (CanESM2, HadGEM2-ES and MIROC5) were used to drive the VIC for a historical baseline (1961-2005) and three time-slices (2011-2040, 2041-2070 and 2071-2099), under RCPs 4.5 and 8.5 scenarios. The scenarios were used as input in the hydrological model after bias correction. The hydrological model (VIC) showed satisfactory statistical performance in calibration and validation, with CNS varying from 0.77 to 0.85 for daily and monthly discharges; however, it overestimated some peak flows and underestimated the recession flows. Multi-model ensemble means predict increases of the minimum and maximum monthly average temperature for the investigated area at the end of the century. The Eta-CanESM2 indicated greater warming, mainly for RCP8.5 at the end the century, whereas Eta-HadGEM2-ES showed higher reduction in the precipitation for RCP4.5 at the beginning of the century and for RCP8.5 at the end the century, negatively impacting the evapotranspiration and discharge. Among the Regional Climate Models (RCMs), the Eta-MIROC5 showed minor changes in the components of the hydrological cycle. This study suggests that Global Circulation Models represent an additional uncertainty, which should be accounted for in the climate change impact assessment.
 Keywords: climate changes, RCP4.5, RCP8.5, VIC model.

Evaluation of Soil Moisture in CMIP6 Simulations

Abstract This study employs multiple reanalysis datasets to evaluate the global shallow and deep soil moisture in Coupled Model Intercomparison Project phase 6 (CMIP6) simulations. The multimodel ensemble mean produces generally reasonable simulations for overall climatology, wet and dry centers, and annual peaks in the melt season at mid- to high latitudes and the rainy season at low latitudes. The simulation capability for shallow soil moisture depends on the relationship between soil moisture and the difference between precipitation and evaporation (P − E). Although most models produce effective simulations in regions where soil moisture is significantly related to the P − E (e.g., Europe, low-latitude Asia, and the Southern Hemisphere), considerable discrepancies between simulated conditions and reanalysis data occur at high elevations and latitudes (e.g., Siberia and the Tibetan Plateau), where cold-season processes play a driving role in soil moisture variability. These discrepancies reflect the lack of information concerning the thaw of snow and frozen ground in the reanalyzed data and the inability of models to simulate these processes. The models also perform poorly in areas of extreme aridity. On a global scale, the majority of models provide consistent and capable simulations owing to the minimal variability in deep soil moisture and limited observational information in reanalysis data. Models with higher spatial resolution do not exhibit closer agreement with the reanalysis data, indicating that spatial resolution is not the first limiting factor for CMIP6 soil moisture simulations.

Projected changes in Antarctic daily temperature in CMIP6 under different warming scenarios during two future periods

Global warming increases the frequency and intensity of climate extremes, but the changes in climate extremes over the Antarctic Ice Sheet (AIS) during different periods are unknown. Changes in surface temperature extreme indices (TN10p, TX10p, TN90p, TX90p, CSDI, WSDI, TNn, TNx, TXn, TXx and DTR) are assessed during 2021–2050 and 2071–2100 under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, based on the multi-model ensemble mean (MMEM) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The extreme indices, excluding TXn and DTR, illustrate the opposite trend in the two periods in SSP1-2.6 over the AIS. Generally, the changes in extreme indices reflect the continued warming over AIS in the future, and the warming is projected to intensify in SSP3-7.0 and SSP5-8.5. The variations in the extreme indices exhibit regional differences. The Antarctic Peninsula displays rapid changes in TNn, TXn and DTR. In SSP5-8.5, the magnitudes of all climate index tendencies are greater during 2071–2100 than 2021–2050. The variations in TX10p, TX90p, TN10p, TN90p, WSDI and CSDI are faster in the Antarctic inland than in the other regions over the AIS. However, the decrease in the DTR is concentrated along the AIS coast and extends to the interior region, whereas the increasing trend occurs in the Antarctic inland. In West AIS, TX90p and TN90p rapidly increase during 2021–2050, whereas the rapid changing signals disappear in this region in 2071–2100. The dramatic changes in TNn, TXn and DTR occur at the Ross Ice Shelf during 2071–2100, indicating an increased risk of collapse. For TNx and TXx, the degree of warming in the later part of the 21st century is divided by the transantarctic mountains, and greater changes appear on the eastern side. Generally, Antarctic amplification of TNn, TXn and DTR is observed except under SSP1-2.6. In addition, TNx and TXx amplifications occur in SSP3-7.0 and SSP5-8.5.

A strong mitigation scenario maintains climate neutrality of northern peatlands

A strong mitigation scenario maintains climate neutrality of northern peatlands

Change in Climate Indices Using Bias-Adjusted CMIP5 Models: The Case Study of the Fatick Region, Senegal

This study analyses change in rainfall and temperature indices by 2035 and 2050 in Senegal, with a focus on the Fatick region. These parameters are crucial for understanding the impacts of anthropogenic climate change on some vital socio-economic sectors such as agriculture and water resources in this region. To this end, a multi model ensemble mean of 21 bias-adjusted global climate models participating in CMIP5 has been used. We considered two Representative Concentration Pathways (RCP4.5 and RCP8.5). The results indicate an increase of 0.7˚C for maximum and minimum temperature by 2035 compared to the reference period (1976 - 2005). By 2050, an increase of 1.4˚C (2˚C) is projected for RCP4.5 (RCP8.5). These increases in temperature are statistically significant at the 90% confidence level. Conversely, the mean rainy season length decreases from 95 to 85 days by 2035 and less than 80 days by 2050. These decreases in rainy season length are mainly due to a delayed rainy season onset by 2035 and 2050, with the ensemble mean projecting an onset in the second half of July by 2050 instead of around the middle of June. The changes in both the onset and the length of the rainy season are significant at the 90% confidence level. Our results show a slight decrease in seasonal cumulated total rainfall by 2035 and 2050. However, we note a slight increase in seasonal cumulated extreme rainfall. These future changes in climate indices could induce yield reduction and water resources availability. To reduce yield losses, it would be interesting to adopt longer season varieties and also diversify income-generating activities. Concerning water resources, many actions could be done such as carrying out water retention works, treatment and reuse of non-conforming water for agriculture and livestock to reduce pressure on the resource.

A systematic analysis of climate model precipitation in southern Brazil

AbstractClimate model precipitation is the foremost input for hydrological models in climate change risk assessment. However, some aspects of precipitation (e.g., frequency, seasonality, and extremes) are usually not well represented by climate models, especially at the regional scale and in the tropics. In this study, we use a set of well‐established metrics to evaluate the marginal, temporal, and spatial aspects of CMIP5 and CMIP6 precipitation in Southern Brazil. This region is in the transition between tropical and subtropical climates with diverse rainfall generation mechanisms and complex topography. We compare the multi‐model‐ensemble mean (MME) and a constrained ensemble (CE) of CMIP5 and CMIP6 against a high‐resolution precipitation data grid. The constrained ensemble is obtained using a weighting approach that minimizes the difference between the simulated and observed cumulative distribution functions. We find that CMIP6 outperforms CMIP5 for most metrics, especially in the simulation of the seasonal cycle and the spatial distribution of precipitation. Simulated precipitation is more seasonal and more spatially dependent than the observations, with a dry bias characterized by lower precipitation amounts and higher consecutive dry days. Our analysis suggests that the models are not able to reproduce the transition between tropical and subtropical climates in this region as well as the passage of frontal systems. Future studies using CMIP6 should focus on those regional mechanisms of precipitation variability.