CMIP5 Multi-model Ensemble Research Articles

Daily snowfall events on the Eurasian continent: CMIP6 models evaluation and projection

AbstractThis study evaluated the performance of 23 models participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating snowfall variation across the Eurasian continent. The results show that the CMIP6 multi‐model ensemble (MME) can reasonably capture the climate means of snowfall indices for 1995–2014 period and has high pattern correlations with observations. However, most of CMIP6 models generally overestimate snowfall amounts, snowfall days and snowfall duration, and the overestimation is much clearer in spring. The performance of these models in simulating snowfall events with different grades (including light snowfall, moderate snowfall, heavy snowfall and snowstorm) becomes gradually poorer with higher snowfall levels. On the whole, the comprehensive model ranks implied that NorESM2‐MM, NorESM2‐LM and CESM2‐WACCM models showed the best performances. In addition, the CMIP6 MME outperforms individual models in terms of both spatial–temporal features and interannual variability; thus, the MME is used to project the snowfall change in the 21st century. Further evaluations show that by the end of this century, snowfall will decrease in most of Eurasia with an average value of 16.7% (31.3%) under the SSP2‐4.5 (SSP5‐8.5) scenarios, and snowfall days will decrease by 12.6 days (25.0 days); the snowfall duration will shorten further due to the first date of snowfall occurrence will postpone by 12.7 days (23.0 days) and the last date further advance by 15.0 days (26.7 days). Additionally, heavy snowfall, snowstorms and their number of days will increase over part of Northern Asia, the Tibetan Plateau and Central Asia, where the risk of snowfall extremes will increase. However, large inter‐model and scenario uncertainties also exist in the CMIP6 model projection of snowfall events, which requires further improvement.

Substantial increase in daytime-nighttime compound heat waves and associated population exposure in China projected by the CMIP6 multimodel ensemble

This paper presents the projected changes in daytime-nighttime compound heat waves (HWs) (i.e. concurrent occurrence of HWs both in daytime and nighttime) and associated population exposure in China under the shared socioeconomic pathway (SSP)2-4.5 and SSP5-8.5 scenarios based on the Coupled Model Intercomparison Project phase 6 simulations. A comparison with the changes in daytime HWs (i.e. occurring only in daytime) or nighttime HWs (i.e. occurring only in nighttime) is also conducted. The results generally indicate an aggravated risk of compound HWs in China in the future under warmer scenarios. On the national average, the compound HWs are projected to increase persistently toward the end of the 21st century, with larger increase under SSP5-8.5 than that under SSP2-4.5. The greatest changes occur in northwest China and southern China. Compared with the daytime or nighttime HWs, the projected increase of compound HWs is the greatest. Accordingly, the proportion of compound HWs to the total HW events tends to increase and that of daytime HWs tends to decrease toward the end of the 21st century. The substantial increases in the frequency of compound HWs are expected to cause a significant increase in population exposure across the entire country. The projected increase of nationally averaged population exposure is 12.2-fold (7.9-fold) of the current in the mid-century (2046–2065) and further enhances to 16.3-fold (12.4-fold) in the end-century (2081–2100) under SSP5-8.5 (SSP2-4.5). The largest increases are distributed in western China and southern China. These findings raise the necessity and urgency for policy-makers and the public to develop measurements to address compound HW risks.

Projections of Future Drought by CMIP5 Multimodel Ensembles in Central Asia

Future changes in drought characteristics in Central Asia are projected at the regional scale using 21 climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Based on the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI), drought characteristics were characterized by drought frequency at 1-, 3-, and 12-month timescales. The drought duration was analyzed based on SPI1 and SPEI1. Drought indices were calculated by the multimodel ensemble (MME) from 21 CMIP5 models. The varimax rotation method was used to identify drought conditions for the entire area and seven drought subregions. In general, the projection results of future drought in Central Asia are related to the choice of drought index, and SPI and SPEI show different results. The drought frequency based on SPEI1, SPEI3, and SPEI12 showed an increasing trend in the future periods, that is, the drought frequency based on monthly, seasonal, and annual timescales will show an increase trend in the future periods. However, for SPI1, SPI3, and SPI12, the drought frequency will decrease in the future. SPI projected that the duration of drought will decrease in the future, while SPEI mainly showed an increasing trend. The results of the study should be of sufficient concern to policymakers to avoid land degradation, crop loss, water resource deficit, and economic loss.

The fractional energy balance equation for climate projections through 2100

Abstract. We produce climate projections through the 21st century using the fractional energy balance equation (FEBE): a generalization of the standard energy balance equation (EBE). The FEBE can be derived from Budyko–Sellers models or phenomenologically through the application of the scaling symmetry to energy storage processes, easily implemented by changing the integer order of the storage (derivative) term in the EBE to a fractional value. The FEBE is defined by three parameters: a fundamental shape parameter, a timescale and an amplitude, corresponding to, respectively, the scaling exponent h, the relaxation time τ and the equilibrium climate sensitivity (ECS). Two additional parameters were needed for the forcing: an aerosol recalibration factor α to account for the large aerosol uncertainty and a volcanic intermittency correction exponent ν. A Bayesian framework based on historical temperatures and natural and anthropogenic forcing series was used for parameter estimation. Significantly, the error model was not ad hoc but rather predicted by the model itself: the internal variability response to white noise internal forcing. The 90 % credible interval (CI) of the exponent and relaxation time were h=[0.33, 0.44] (median = 0.38) and τ=[2.4, 7.0] (median = 4.7) years compared to the usual EBE h=1, and literature values of τ typically in the range 2–8 years. Aerosol forcings were too strong, requiring a decrease by an average factor α=[0.2, 1.0] (median = 0.6); the volcanic intermittency correction exponent was ν=[0.15, 0.41] (median = 0.28) compared to standard values α=ν=1. The overpowered aerosols support a revision of the global modern (2005) aerosol forcing 90 % CI to a narrower range [−1.0, −0.2] W m−2. The key parameter ECS in comparison to IPCC AR5 (and to the CMIP6 MME), the 90 % CI range is reduced from [1.5, 4.5] K ([2.0, 5.5] K) to [1.6, 2.4] K ([1.5, 2.2] K), with median value lowered from 3.0 K (3.7 K) to 2.0 K (1.8 K). Similarly we found for the transient climate response (TCR), the 90 % CI range shrinks from [1.0, 2.5] K ([1.2, 2.8] K) to [1.2, 1.8] K ([1.1, 1.6] K) and the median estimate decreases from 1.8 K (2.0 K) to 1.5 K (1.4 K). As often seen in other observational-based studies, the FEBE values for climate sensitivities are therefore somewhat lower but still consistent with those in IPCC AR5 and the CMIP6 MME. Using these parameters, we made projections to 2100 using both the Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP) scenarios, and compared them to the corresponding CMIP5 and CMIP6 multi-model ensembles (MMEs). The FEBE historical reconstructions (1880–2020) closely follow observations, notably during the 1998–2014 slowdown (“hiatus”). We also reproduce the internal variability with the FEBE and statistically validate this against centennial-scale temperature observations. Overall, the FEBE projections were 10 %–15 % lower but due to their smaller uncertainties, their 90 % CIs lie completely within the GCM 90 % CIs. This agreement means that the FEBE validates the MME, and vice versa.

Assessing Canada's Renewable Energy Potential under Climate Change through a CMIP6 Multi-Model Ensemble Approach

Assessing Canada's Renewable Energy Potential under Climate Change through a CMIP6 Multi-Model Ensemble Approach

Model calibration using ESEm v1.1.0 – an open, scalable Earth system emulator

Abstract. Large computer models are ubiquitous in the Earth sciences. These models often have tens or hundreds of tuneable parameters and can take thousands of core hours to run to completion while generating terabytes of output. It is becoming common practice to develop emulators as fast approximations, or surrogates, of these models in order to explore the relationships between these inputs and outputs, understand uncertainties, and generate large ensembles datasets. While the purpose of these surrogates may differ, their development is often very similar. Here we introduce ESEm: an open-source tool providing a general workflow for emulating and validating a wide variety of models and outputs. It includes efficient routines for sampling these emulators for the purpose of uncertainty quantification and model calibration. It is built on well-established, high-performance libraries to ensure robustness, extensibility and scalability. We demonstrate the flexibility of ESEm through three case studies using ESEm to reduce parametric uncertainty in a general circulation model and explore precipitation sensitivity in a cloud-resolving model and scenario uncertainty in the CMIP6 multi-model ensemble.

Structure of the Pacific Walker Circulation Depicted by the Reanalysis and CMIP6

The Pacific Walker circulation (PWC) is one of the most important components of large-scale tropical atmospheric circulations. The PWC and its influences have been studied extensively by numerical models and reanalysis. The newly released ERA5 and NCEP2 are the most widely used reanalysis datasets and serve as benchmarks for evaluation of model simulations. If the results of these datasets differ significantly, this could lead to a bias in projected long-term climate knowledge. For better understanding of future climate change, it is necessary to evaluate PWC reanalysis productions. As a result, we compared the PWC structures between the ERA5 and NCEP2 datasets from month to seasonal time scales. We used the zonal mass streamfunction (ZMS) over the equatorial Pacific to indicate the strength of the PWC. The PWC’s average monthly or seasonal cycle peaks around July. From February to June, the NCEP2 shows a higher PWC intensity, whereas the ERA5 shows greater intensity from July to December. The circulation center in the NCEP2 is generally stronger and wider than in the ERA5. The ERA5, however, revealed that the PWC’s west edge (zero line of ZMS over the western Pacific) had moved 10 degrees westward in comparison to the NCEP2. In addition, we compared the PWC mean state in the reanalysis and CMIP6 models; the mean state vertical structures of the tropical PWC in the CMIP6 multi-model ensemble (MME) are similar to those of the reanalyses in structure but weaker and wider than in the two reanalysis datasets. The PWC is broader in CMIP6, and the western boundary is 7 and 17 degrees farther west than in the ERA5 and NCEP2, respectively. This study suggests that, when using reanalysis datasets to evaluate PWC structural changes in intensity and western edge, extreme caution should be exercised.

Projections of meteorological drought based on CMIP6 multi-model ensemble: A case study of Henan Province, China

Future changes in drought events are critical for risk assessment and associated policymaking. In this study, the future changes in meteorological droughts in Henan Province, China are explored. Random forests downscaling model is first constructed based on ERA5 reanalysis data and meteorological observations. The model is validated using evaluation indices such as R2 and RMSE, and is shown to be able to capture the relationship between large-scale predictors and monthly precipitation. The validated random forests downscaling model is driven by multiple global climate models (GCMs) from the Phase 6 of the Coupled Model Intercomparison Project (CMIP6) under three emission scenarios for projecting three future drought characteristics (duration, frequency, and intensity). Results show that drought frequency decreases in most areas of Henan while drought duration and intensity increase in various degrees. Some differences are also observed among different emission scenarios, especially under SSP2–4.5, where the magnitudes of changes in drought duration and intensity are lower relative to other scenarios. The decrease in drought frequency in most areas is found to be caused by increases in monthly mean precipitation in this study. Changes in drought duration and intensity are related to a combination of increases in precipitation mean and variability.

Comparison of CMIP5 and CMIP6 Multi-Model Ensemble for Precipitation Downscaling Results and Observational Data: The Case of Hanjiang River Basin

Evaluating global climate model (GCM) outputs is essential for accurately simulating future hydrological cycles using hydrological models. The GCM multi-model ensemble (MME) precipitation simulations of the Climate Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6, respectively) were spatially and temporally downscaled according to a multi-site statistical downscaling method for the Hanjiang River Basin (HRB), China. Downscaled precipitation accuracy was assessed using data collected from 14 meteorological stations in the HRB. The spatial performances, temporal performances, and seasonal variations of the downscaled CMIP5-MME and CMIP6-MME were evaluated and compared with observed data from 1970–2005. We found that the multi-site downscaling method accurately downscaled the CMIP5-MME and CMIP6-MME precipitation simulations. The downscaled precipitation of CMIP5-MME and CMIP6-MME captured the spatial pattern, temporal pattern, and seasonal variations; however, precipitation was slightly overestimated in the western and central HRB and precipitation was underestimated in the eastern HRB. The precipitation simulation ability of the downscaled CMIP6-MME relative to the downscaled CMIP5-MME improved because of reduced biases. The downscaled CMIP6-MME better simulated precipitation for most stations compared to the downscaled CMIP5-MME in all seasons except for summer. Both the downscaled CMIP5-MME and CMIP6-MME exhibit poor performance in simulating rainy days in the HRB.

Evaluation of Long‐Term Temperature Trend and Variability in CMIP6 Multimodel Ensemble

AbstractThis study conducts a robust assessment of the Coupled Model Intercomparison Project Phase 6 to capture the observed temperature trends and variability at global and regional scales. The warming rate in the second half of the twentieth century (0.19°C/decade) is twice as large as in the full analysis period (1901–2014; 0.10°C/decade). Multidecadal climate variability results in considerable uncertainties in the regional temperature trend, but the multidecadal variability does not represent a statistically significant trend. Globally, the spatial pattern of trends is most similar among ensemble members of the same model, then among climate models, and the least similar between models and observations. The structural uncertainty and internal variability of climate models provide a range of temperature trends that generally encompass the regional scale observations. Some single model large ensembles also have variability comparable to the multimodel large ensemble, encompassing the regional scale observations.

Comparison of CMIP6 historical climate simulations and future projected warming to an empirical model of global climate

Abstract. The sixth phase of the Coupled Model Intercomparison Project (CMIP6) is the latest modeling effort for general circulation models to simulate and project various aspects of climate change. Many of the general circulation models (GCMs) participating in CMIP6 provide archived output that can be used to calculate effective climate sensitivity (ECS) and forecast future temperature change based on emissions scenarios from several Shared Socioeconomic Pathways (SSPs). Here we use our multiple linear regression energy balance model, the Empirical Model of Global Climate (EM-GC), to simulate and project changes in global mean surface temperature (GMST), calculate ECS, and compare to results from the CMIP6 multi-model ensemble. An important aspect of our study is a comprehensive analysis of uncertainties due to radiative forcing of climate from tropospheric aerosols (AER RF) in the EM-GC framework. We quantify the attributable anthropogenic warming rate (AAWR) from the climate record using the EM-GC and use AAWR as a metric to determine how well CMIP6 GCMs replicate human-driven global warming over the last 40 years. The CMIP6 multi-model ensemble indicates a median value of AAWR over 1975–2014 of 0.221 ∘C per decade (range of 0.151 to 0.299 ∘C per decade; all ranges given here are for 5th and 95th confidence intervals), which is notably faster warming than our median estimate for AAWR of 0.157 ∘C per decade (range of 0.120 to 0.195 ∘C per decade) inferred from the analysis of the Hadley Centre Climatic Research Unit version 5 data record for GMST. Estimates of ECS found using the EM-GC assuming that climate feedback does not vary over time (best estimate 2.33 ∘C; range of 1.40 to 3.57 ∘C) are generally consistent with the range of ECS of 1.5 to 4.5 ∘C given by the IPCC's Fifth Assessment Report. The CMIP6 multi-model ensemble exhibits considerably larger values of ECS (median 3.74 ∘C; range of 2.19 to 5.65 ∘C). Our best estimate of ECS increases to 3.08 ∘C (range of 2.23 to 5.53 ∘C) if we allow climate feedback to vary over time. The dominant factor in the uncertainty for our empirical determinations of AAWR and ECS is imprecise knowledge of AER RF for the contemporary atmosphere, though the uncertainty due to time-dependent climate feedback is also important for estimates of ECS. We calculate the likelihood of achieving the Paris Agreement target (1.5 ∘C) and upper limit (2.0 ∘C) of global warming relative to pre-industrial for seven of the SSPs using both the EM-GC and the CMIP6 multi-model ensemble. In our model framework, SSP1-2.6 has a 53 % probability of limiting warming at or below the Paris target by the end of the century, and SSP4-3.4 has a 64 % probability of achieving the Paris upper limit. These estimates are based on the assumptions that climate feedback has been and will remain constant over time since the prior temperature record can be fit so well assuming constant climate feedback. In addition, we quantify the sensitivity of future warming to the curbing of the current rapid growth of atmospheric methane and show that major near-term limits on the future growth of methane are especially important for achievement of the 1.5 ∘C goal of future warming. We also quantify warming scenarios assuming climate feedback will rise over time, a feature common among many CMIP6 GCMs; under this assumption, it becomes more difficult to achieve any specific warming target. Finally, we assess warming projections in terms of future anthropogenic emissions of atmospheric carbon. In our model framework, humans can emit only another 150±79 Gt C after 2019 to have a 66 % likelihood of limiting warming to 1.5 ∘C and another 400±104 Gt C to have the same probability of limiting warming to 2.0 ∘C. Given the estimated emission of 11.7 Gt C per year for 2019 due to combustion of fossil fuels and deforestation, our EM-GC simulations suggest that the 1.5 ∘C warming target of the Paris Agreement will not be achieved unless carbon and methane emissions are severely curtailed in the next 10 years.

Assessment and Ranking of Climate Models in Arctic Sea Ice Cover Simulation: From CMIP5 to CMIP6

AbstractThe capability of 36 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) and their 24 CMIP5 counterparts in simulating the mean state and variability of Arctic sea ice cover for the period 1979–2014 is evaluated. In addition, a sea ice cover performance score for each CMIP5 and CMIP6 model is provided that can be used to reduce the spread in sea ice projections through applying weighted averages based on the ability of models to reproduce the historical sea ice state. Results show that the seasonal cycle of the Arctic sea ice extent (SIE) in the multimodel ensemble (MME) mean of the CMIP6 simulations agrees well with observations, with a MME mean error of less than 15% in any given month relative to the observations. CMIP6 has a smaller intermodel spread in climatological SIE values during summer months than its CMIP5 counterpart. In terms of the monthly SIE trends, the CMIP6 MME mean shows a substantial reduction in the positive bias relative to the observations compared with that of CMIP5. The spread of September SIE trends is very large, not only across different models but also across different ensemble members of the same model, indicating a strong influence of internal variability on SIE evolution. Based on the assumptions that the simulations of CMIP6 models are from the same distribution and that models have no bias in response to external forcing, we can infer that internal variability contributes to approximately 22% ± 5% of the September SIE trend over the period 1979–2014.

Change in soil organic carbon and its climate drivers over the Tibetan Plateau in CMIP5 earth system models

Soil organic carbon (SOC) is the largest carbon pool in the terrestrial carbon cycle, which is closely linked to climate change and global warming feedbacks. Based on proxy observation data and outputs from the fifth phase of the Coupled Model Inter-comparison Project (CMIP5), we analyzed quantitatively the spatial distribution and temporal change for SOC using boosted regression trees (BRT) over the Tibetan Plateau (TP). The ensemble proxy observation SOC stock data indicated a decreasing spatial distribution from southeast to northwest over the TP. We used data from ten CMIP5 earth system models (ESMs) for SOC, which exhibited differences. However, the CMIP5 multi-model ensemble (MME) result presented a similar spatial distribution pattern to the ensemble proxy observation SOC, while SOC storage and turnover times from the CMIP5 MME model were less than the ensemble proxy observation. The BRT results indicated that air temperature (Ta) accounted for 44.81% of the relative contribution and was the most influential variable on MME SOC. This was followed by net primary production (NPP), with a 19.09% relative contribution. The relative influence of the top 10 cm soil temperature, total soil water, and precipitation on MME SOC was 13.55%, 12.68%, and 9.87%, respectively. Using the BRT method to determine the spatial distribution of relative contribution of SOC for these five variables demonstrated that Ta was mainly higher over the central and northwestern regions of the TP, and NPP was higher over the western central regions and along the plateau’s eastern edge. The statistical frequency of maximum relative contribution to SOC for the five variables indicated that the relative contribution of NPP covered the largest area, with 32% of the total grid numbers, followed by Ta, with 25%.

Assessment of GCMs simulation performance for precipitation and temperature from CMIP5 to CMIP6 over the Tibetan Plateau

AbstractGeneral circulation models (GCMs) are indispensable for climate change adaptive study over the Tibetan Plateau (TP), which is the potential trigger and amplifier in global climate fluctuations. With the release of Coupled Model Intercomparison Project Phase 6 (CMIP6), 24 GCMs from CMIP5 and CMIP6 were comparatively evaluated for precipitation and air temperature simulations based on the China Meteorological Forcing Dataset (CMFD). Rank score results showed that CMIP6 models generally performed better than CMIP5 for precipitation and surface air temperature over the TP. According to multimodel ensembles (MMEs) of the optimal GCMs for each climate variable, the overestimation of precipitation was both present in CMIP5 and CMIP6, but the results of CMIP6 MMEs were relatively lower in the mid‐west and northern edge of the TP. Furthermore, CMIP6 offered a better performance of precipitation in summer and autumn. For temperature, CMIP6 MMEs were able to reduce the relatively large cold bias that appeared in CMIP5 MMEs in northwest areas to about 1°C and had a smaller bias in spring and winter. Moreover, the investigation into the simulation effects of precipitation at different elevation zones demonstrated that the improved ability of CMIP6 MMEs to reduce bias was mainly concentrated in the elevation zones of 2,000–3,000 m and over 5,000 m, where the precipitation bias was more than 200%. Additionally, CMIP6 MMEs of temperature were able to reduce the bias to less than 2°C in each elevation zone, with the minimum bias of −0.22°C distributed in the region with altitudes from 3,000 to 4,000 m, while the biases of CMIP5 MMEs in the region of 4,000–5,000 m and over 5,000 m were smaller than those of CMIP6 MMEs. Findings obtained in this study could provide a scientific reference for related climate change research over the TP. GCMs of CMIP6 perform better than those of CMIP5 for precipitation and temperature over the TP. Multimodel ensembles (MMEs) of CMIP6 effectively reduce the overestimation of precipitation from CMIP5 MMEs by 40 mm at the annual scale. Improved ability of CMIP6 MMEs shows a significant elevation dependency, especially in elevation zones of 2,000–3,000 m and over 5,000 m for precipitation.

Historical fidelity and future change of Amundsen Sea Low under 1.5 °C–4 °C global warming in CMIP6

The realistic simulation and projection of the Amundsen Sea Low (ASL) are essential for understanding the Antarctic climate and global climate change. Using 14 models that participated in phase 6 of the Coupled Model Intercomparison Project (CMIP6), this study evaluates the climatological characteristics of ASL with comparison to the ERA5 reanalysis and their CMIP5 versions and assesses the future change of ASL under 1.5 °C–4 °C global warming. The climatological spatial distribution of ASL is captured reasonably but with underestimated intensity by CMIP6 multi-model ensemble (MME). Among the CMIP6 models, EC-Earth3 has most accurate representation of ASL according to the pattern correlation and biases. The seasonal variation of the ASL depth and location are found to be reasonably reproduced by the CMIP6 models. CMIP6 MME has higher skills in simulating the seasonal cycle of absolute depth and zonal migration of the ASL center. The relative central pressure of ASL is underestimated in all seasons and there is a 4-degree northward shift bias of the ASL center in austral winter, which were also evident in the CMIP5. The semiannual cycle of ASL absolute depth with two deepest pressure in April and October is also captured by CMIP6 MME. However, the observed peak of pressure between the two months occurs in June, while it delays one month and appears until July in CMIP6 MME. Compared with CMIP5, CMIP6 MME exhibit evident reduced uncertainties and overall improvement in simulating absolute depth and location of the ASL center, which might be attributed to models' capability of representing the location of Southern Hemisphere westerlies, while the biases in relative depth become even large in CMIP6 MME. In response to future warming from 1.5 °C to 4 °C above pre-industrial levels, the absolute depth of ASL will very likely deepen with larger amplitude in all seasons, while the relative depth might enhance only under high-level warmer world in austral autumn to winter. The CMIP6 MME also projects that the ASL will shift poleward constantly in austral summer and migrate southwestward during austral autumn with the rising global mean temperature. Among all the seasons, the most prominent future changes in intensity and location of ASL are found in autumn. The enhancement and poleward movement of ASL could also be identified during the Ross Sea ice advance season under 1.5 °C–4 °C global warming. The results reveal the potential of CMIP6 models in the ASL study and the impact of ASL on Antarctic climate under different global warming levels.

Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble

Extreme precipitation is the leading cause of the flood, soil erosion, and drought with significant socioeconomic impacts on human resources. Therefore, projecting future precipitation changes, especially the intensity of extreme precipitation (IEP) in the future, is very important. This study’s main objective is IEP projection in Iran based on CMIP6 bias-correction (BC) multi-model ensemble (MME). The daily precipitation data of five CMIP6 BC models with 0.5 horizontal resolution was used under three shared socioeconomic pathways; SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios. Simple Daily Intensity (SDII) and maximum consecutive 1-day precipitation (RX1day) were used to measure IEP changes. Two Kling Gupta efficiency (KGE) and percent bias (PBIAS) methods evaluate the performance of the models, and the independence weighted mean (IWM) method was applied for ensemble averaging. Among the studied CMIP6 bias-correction models, the IPSL-CM6A-LR model has more underestimation than other models with a KGE score of 0.751 has the lowest performance and the MPI-ESM1-2-HR model (0.768) showed the highest performance. In general, the study results showed the uncertainties in CMIP6 models for precipitation, according to which no single model is reliable even in the BC method. The PBIAS in the region affected by Asian summer monsoon (ASM) in Iran is about 10%, based on which the bias of the mentioned models for monsoon precipitation is high. SDII and RX1day anomalies in all climatic zones of Iran except for the RX1day index in Cfa climatic zones in other zones and scenarios during the two periods 2021-2060 and 2061-2100 are positive. Also, the trend and slope of the IEP are increasing in all zones except for BWh, BWk, and Cfa zones for SSP1-2.6 and SSP3-7.0 scenarios. Investigation of trend IEP changes showed that the maximum of these changes will occur in the BWh climate zone, and the minimum would occur in the cold and mountainous climate zone of Iran (Dsc).

CMIP5/6 models project little change in the statistical characteristics of sudden stratospheric warmings in the 21st century

Using state-of-the-art models from the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5/6), future changes of sudden stratospheric warming (SSW) events under a moderate emission scenario (RCP45/SSP245) and a strong emissions scenario (RCP85/SSP585) are evaluated with respect to the historical simulations. Changes in four characteristics of SSWs are examined in 54 models: the SSW frequency, the seasonal distribution, stratosphere–troposphere coupling, and the persistency of the distorted or displaced polar vortex. The composite results show that none of these four aspects will change robustly. An insignificant (though positive) change in the SSW frequency from historical simulations to RCP45/SSP245 and then to RCP85/SSP585 is consistently projected by CMIP5 and CMIP6 multimodel ensembles in most wintertime months (December–March). This increase in the SSW frequency is most pronounced in mid- (late-) winter in CMIP6 (CMIP5). No shift in the seasonality of SSWs is simulated especially in the CMIP6 future scenarios. Both the reanalysis and CMIP5/6 historical simulations exhibit strong stratosphere–troposphere coupling during SSWs, and the coupling strength is nearly unchanged in the future scenario simulations. The near surface responds immediately after the onset of SSWs in both historical and future scenarios experiments, denoted by the deep downward propagation of zonal-mean easterly anomalies from the stratosphere to the troposphere. On average, the composite circumpolar easterly winds persist for 8 d in the reanalysis and CMIP5/6 historical experiments, which are projected to remain unchanged in both the moderate and strong emissions scenarios, implying the lifecycle of SSWs will not change.

The Role of an Indian Ocean Heating Dipole in the ENSO Teleconnection to the North Atlantic European Region in Early Winter during the Twentieth Century in Reanalysis and CMIP5 Simulations

AbstractIn this study the role of an Indian Ocean heating dipole anomaly in the transition of the North Atlantic–European (NAE) circulation response to El Niño–Southern Oscillation (ENSO) from early to late winter is analyzed using a twentieth-century reanalysis and simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5). It is shown that in early winter a warm (cold) ENSO event is connected through an atmospheric bridge with positive (negative) rainfall anomalies in the western Indian Ocean and negative (positive) anomalies in the eastern Indian Ocean. The early winter heating dipole, forced by a warm (cold) ENSO event, can set up a wave train emanating from the subtropical South Asian jet region that reaches the North Atlantic and leads to a response that spatially projects onto the positive (negative) phase of the North Atlantic Oscillation. The Indian Ocean heating dipole is partly forced as an atmospheric teleconnection by ENSO, but can also exist independently and is not strongly related to local Indian Ocean sea surface temperature (SST) forcing. The Indian Ocean heating dipole response to ENSO is much weaker in late winter (i.e., February and March) and not able to force significant signals in the North Atlantic region. CMIP5 multimodel ensemble reproduces the early winter Indian Ocean heating dipole response to ENSO and its transition in the North Atlantic region to some extent, but with weaker amplitude. Generally, models that have a strong early winter ENSO response in the subtropical South Asian jet region along with tropical Indian Ocean heating dipole also reproduce the North Atlantic response.

Temperature and rainfall extremes change under current and future global warming levels across Indian climate zones

Mean surface temperature is projected to rise by about 4.4 °C by the end of the century compared to the period between 1976 and 2005 when following the most extreme scenario of the greenhouse gas emissions pathway (Krishnan et al., 2020). With this rise in mean temperature, there is a lot of uncertainty on how weather and climate extremes would unfold, especially for various climate zones of India. It is therefore essential that the potential changes in both magnitude and direction of weather and climate extremes at the regional level when the global temperature reaches the different warming levels from 1 °C to 3 °C be established to allow for informed policy formulation.The present study explores the potential changes in the Expert Team on Climate Change Detection and Indices of rainfall and temperature estimated from the coupled model inter-comparison project CMIP5 multi-model ensemble over different climatic zones of India at 1 °C, 1.5 °C, 2 °C, 2.5 °C and 3 °C global temperature rise relative to pre-industrial levels under two Representative Concentration Pathways, RCP4.5 and RCP8.5. Projected changes in temperature extremes indicate significant changes at all warming levels across the nine climate zones of India. Hot temperature extremes are expected to increase while cold temperature extremes decrease. For India, country average at 3 °C under the RCP8.5 and 2 °C under the RCP4.5 scenarios, ensemble median shows that Warm Spell Duration Index will increase by 131 days and 66 days; hot days increase by 44% and 52%, warm nights increase by 23% and 13%; cold days decrease by 10% and 9%, and cold nights decrease by 13% and 12% relative to pre-industrial levels. The greatest changes in temperature based indices are projected in the colder northern parts of the country followed by the arid zone. Ensemble median for rainfall indices shows an increase in high precipitation indices, though with large model spread indicating the large uncertainties in the projections. Annual total precipitation and heavy rainfall related extreme indices show statistically significant increases in the tropical, temperate and semi-arid regions of India, moving from 1 °C to 3 °C warming level under RCP8.5 scenario whereas there is generally no significant change in the maximum number of consecutive dry and wet days.Moreover, the potential changes in climate extremes at the regional level are expected to have far-reaching impacts on the social and economic statuses of the respective climate zones. This information at a regional scale also calls attention to the national and state action plan on climate change and adaptation to be more responsive in order to take coherent and integrated policy decisions.

Sea Level Pressure Trends: Model-Based Assessment of Detection, Attribution, and Consistency with CMIP5 Historical Simulations

AbstractObserved sea level pressure (SLP) trends for 1901–10, 1951–10, and 1981–2010 are assessed using two observed data sources (HadSLP2_lowvar and 20CRv3) compared to a CMIP5 multimodel ensemble. The CMIP5 simulations include runs with (i) no external forcing (Control runs), (ii) natural external forcing only (Natural-Forcing), or (iii) natural plus anthropogenic forcings combined (All-Forcings). We assess whether the CMIP5 All-Forcing ensemble is consistent with observations and whether there is model-based evidence for detectable anthropogenic influence for the observed SLP trends. For the 1901–2010 and 1951–2010 trends, a robustly detectable anthropogenic signal in both observational data products is a zonal band of SLP increase extending over much of the Southern Hemisphere extratropics (30°–50°S). In contrast, the HadSLP2_lowvar and 20CRv3 observed data products disagree on the sign of the century-scale trends in SLP over much of the low-latitude region 25°N–25°S. These differences will limit confident detection/attribution/consistency conclusions for lower-latitude regions, at least until the observational data product discrepancies are better reconciled. The Northern Hemisphere extratropics remains a difficult region for identifying any detectable anthropogenic influence for annual- or seasonal-mean SLP trends. Overall, our results highlight the difficulty in detecting and attributing anthropogenic signals in SLP for relatively short time scales. The observed 1981–2010 regional trends typically have a different pattern and magnitude from the simulated externally forced trends. Consequently, our results suggest that internal variability is likely the dominant driver of most observed 1981–2010 regional trend features, including the pronounced increase in SLP over the central and eastern equatorial Pacific.