CMIP5 Models Research Articles

Constraining the Pattern and Magnitude of Projected Extreme Precipitation Change in a Multimodel Ensemble

Abstract Projections of precipitation extremes over land are crucial for socioeconomic risk assessments, yet model discrepancies limit their application. Here we use a pattern-filtering technique to identify low-frequency changes in individual members of a multimodel ensemble to assess discrepancies across models in the projected pattern and magnitude of change. Specifically, we apply low-frequency component analysis (LFCA) to the intensity and frequency of daily precipitation extremes over land in 21 CMIP-6 models. LFCA brings modest but statistically significant improvements in the agreement between models in the spatial pattern of projected change, particularly in scenarios with weak greenhouse forcing. Moreover, we show that LFCA facilitates a robust identification of the rates at which increasing precipitation extremes scale with global temperature change within individual ensemble members. While these rates approximately match expectations from the Clausius-Clapeyron relation on average across models, individual models exhibit considerable and significant differences. Monte Carlo simulations indicate that these differences contribute to uncertainty in the magnitude of projected change at least as much as differences in the climate sensitivity. Last, we compare these scaling rates with those identified from observational products, demonstrating that virtually all climate models significantly underestimate the rates at which increases in precipitation extremes have scaled with global temperatures historically. Constraining projections with observations therefore amplifies the projected intensification of precipitation extremes as well as reducing the relative error of their distribution.

Uncertainty reduction for precipitation prediction in North America.

Large differences in projected future annual precipitation increases in North America exists across 27 CMIP6 models under four emission scenarios. These differences partly arise from weak representations of land-atmosphere interactions. Here we demonstrate an emergent constraint relationship between annual growth rates of future precipitation and growth rates of historical temperature. The original CMIP6 projections show 0.49% (SSP126), 0.98% (SSP245), 1.45% (SSP370) and 1.92% (SSP585) increases in precipitation per decade. Combining observed warming trends, the constrained results show that the best estimates of future precipitation increases are more likely to reach 0.40-0.48%, 0.83-0.93%, 1.29-1.45% and 1.70-1.87% respectively, implying an overestimated future precipitation increases across North America. The constrained results also are narrow the corresponding uncertainties (standard deviations) by 13.8-31.1%. The overestimated precipitation growth rates also reveal an overvalued annual growth rates in temperature (6.0-13.2% or 0.12-0.37°C) and in total evaporation (4.8-14.5%) by the original models' predictions. These findings highlight the important role of temperature for accurate climate predictions, which is important as temperature from current climate models' simulations often still have systematic errors.

The Heat Budget of the Tropical Pacific Mixed Layer during Two Types of El Niño Based on Reanalysis and Global Climate Model Data

The heat budget of the equatorial Pacific mixed layer during El Niño formation was studied based on reanalysis (GLORYS2V4) and model data for the modern climate. The focus of the study is on the so-called El Niño diversity, i.e., the existence of different types of events that are characterized by different locations and intensities, as well as significantly different teleconnection all around the world. The analysis of the processes that participate in the formation of different El Niño types may serve for a better understanding of the El Niño dynamic and contribute to improving its forecast. Two classifications, based on the location and intensity of the events, were considered: strong/moderate and Eastern Pacific (EP)/Central Pacific (CP). The analysis did not reveal a significant difference in the heat budget of the mixed layer between strong and EP El Niño events, as well as between moderate and CP events. The major difference in the generation mechanism of strong (EP) and moderate (CP) El Niño events consists of the magnitude of heating produced by ocean heat budget components with higher heating rates for strong (EP) events. The evolution of sea surface temperature anomalies (SSTA) is governed primarily by oceanic advection. The vertical advection (due to the thermocline feedback) is the main contributor to SSTA growth in the eastern Pacific regardless of El Niño’s type. In the Central Pacific, horizontal advection is more important than vertical one, with a stronger impact of meridional processes for both strong and moderate regimes. Furthermore, the evaluation of the CMIP5 model’s skill in the simulation of the processes responsible for the formation of different El Niño types was carried out. The analysis of the heat budget of the mixed layer in the CMIP5 ensemble demonstrated that the most successful models are CCSM4, CESM1-BGC, CMCC-CMS, CNRM-CM5, GFDL-ESM2M, and IPSL-CM5B-LR. They are capable of reproducing the most important contribution of the advection terms in the SSTA tendency, keeping the major role of the thermocline feedback (and vertical advection) in the eastern Pacific, and do not overestimate the contribution of zonal advective feedback. These models are recommended to be used for the analysis of El Niño mechanism modification in the future climate.

The sensitivity of the equatorial pacific ODZ to particulate organic matter remineralization in a climate model under pre-industrial conditions

Marine oxygen plays a fundamental role in regulating the transfer of organic carbon and nutrients to their dissolved inorganic forms, serving as the terminal electron acceptor for heterotrophic respiration. Oxygen can become limiting to these processes in coastal and open-ocean oxygen deficient zones (ODZs). The maintenance of ODZs depends on the balance between physical processes such as ventilation and biogeochemical processes such as remineralization. These two processes act in opposing directions on ODZs, with ventilation responsible for transporting oxygen from the surface, where it is near saturation with the partial pressure of O2 in the atmosphere, to deeper oxygen-depleted layers, and remineralization acting to consume oxygen through biogeochemical processes such as microbial degradation throughout the water column. Remineralization is represented in all CMIP6 models, but the actual parameterizations, as well as their magnitude, are widely varying.In this study, we examine the sensitivity to remineralization of the equatorial Pacific ODZ (O2<60μmol/kg) in a model; the NASA GISS Ocean Biogeochemical Model (NOBM-G) embedded in the NASA-GISS coupled atmosphere-ocean model. We find that increasing the remineralization rate shoals the ODZ onset depth (i.e. the regionally averaged upper bound of the ODZ), and decreases ODZ volume and regional-averaged thickness. Changes in biological consumption and vertical convergence of oxygen are identified as the primary contributors to changes in ODZ onset depth. ODZ thickness is mainly influenced by the shoaling of the bottom boundary of the ODZ, which decreases with maximum remineralization rate due to decreasing oxygen consumption and increasing horizontal oxygen convergence in deep waters. On the other hand, vertically-averaged ODZ area has a more complex, non-monotonic relationship with maximum remineralization rate. While these findings suggest an important role for remineralization in determining the shape of the ODZ in our model, the relative importance of remineralization vs. other physical parameterizations remains to be established. While our results reflect the structure of our ocean biogeochemical model, the relationship of remineralization to ODZ characteristics in other models should be examined to better inform model inter-comparisons.

Effect of quasi‐biennial oscillation on the intensity of the South China Sea summer monsoon

AbstractThe interannual relationship between the quasi‐biennial oscillation (QBO) and the South China Sea (SCS) summer monsoon intensity (SCSSMI) is investigated using the ERA5 data set in the period of 1953–2020. The QBO–SCSSMI connection is weak in El Niño‐Southern Oscillation (ENSO)‐related summer, but significant in neutral ENSO summer. In neutral ENSO summer, there is significant positive correlation between QBO and SCSSMI, that is, the SCSSMI is enhanced in the QBO westerly phase, while it is weakened in the QBO easterly phase with larger amplitude of the SCSSMI anomaly. QBO may modulate the SCSSMI in three possible ways. The QBO‐related temperature anomalies downward to the lower stratosphere directly affect the SCS monsoon convection by modulating the upper‐tropospheric stability and tropopause height over the SCS region. Moreover, the QBO‐related anomalous zonal wind downward to the lower stratosphere may regulate cross‐tropopause vertical wind shear and then modulate the convection development. Besides, the QBO‐related zonal wind and temperature signals propagating downward into the upper troposphere can affect the horizontal divergence by influencing the vertical vorticity and the geopotential via thermal forcing. Through those three processes, QBO directly affects the deep convection activities and then modulates the SCSSMI via internal dynamic adjustment. In the southern SCS, cross‐tropopause vertical wind shear makes a main contribution to deep convections, while upper‐tropospheric static stability makes a major contribution to deep convection in the northern SCS. The conclusions are also generally verified by the CMIP6 model simulation.

Uncertainty on Atlantic Niño Variability Projections

AbstractSources of uncertainty (i.e., internal variability, model and scenario) in Atlantic Niño variability projections were quantified in 49 models participating in the Coupled Model Intercomparison Phases 5 (CMIP5) and 6 (CMIP6). By the end of the twenty‐first century, the ensemble mean change in Atlantic Niño variability is −0.07 ± 0.10˚C, with 80% of CMIP models projecting a decrease, and representing a 16% reduction relative to the 1981–2005 ensemble mean. Models' projections depict a large spread, with variability changes ranging from 0.23˚C to −0.50˚C. Internal variability is the main source of uncertainty until 2045 but model uncertainty dominates thereafter, eventually explaining up to 80% of the total uncertainty. The scenario uncertainty remains low (&lt;1%) throughout the twenty‐first century. The total uncertainty on Atlantic Niño variability projections is not improved when considering only CMIP models with a realistic zonal equatorial Atlantic sea surface temperature gradient.

Future electricity production at Mutriku Wave Energy Plant estimated from CMIP6 wave climate projections (2015–2100)

Renewable marine energy sources are rapidly developing worldwide. With numerous operational marine power plants in existence, it is becoming increasingly important to explore their potential. Hence, this study aims to examine the impact of climate change on the future potential of wave energy. The case study was centred on the Mutriku Wave Energy Plant, situated in the Bay of Biscay in the northern region of the Iberian Peninsula.To accomplish this, this study investigated changes in wave energy from 2015 to 2100 by employing ERA5 data and an ensemble of CSIRO wave projections driven by six distinct models derived from CMIP6 model runs. Two were associated with the SSP1-2.6 pathway while the other four corresponded to the SSP5-8.5 pathway. The unidimensional wave variables were bias-corrected using the Quantile Matching (QM) technique, whereas the bidimensional variables were corrected using the Multivariate Bias Correction N-pdf (MBC N-pdf) technique. Subsequently, a self-organising map (SOM) technique was employed to classify daily sea type frequencies and power. Additionally, the Smirnov test was employed to determine whether the probability density functions derived from different datasets exhibited significant differences at a significance level of 0.05.The conclusions obtained indicate that energy production in the Bay of Biscay will remain stable in the late 21st Century. The daily frequencies of the sea type and power did not change significantly. This stability ensures consistent power generation, enabling the location to provide a reliable and consistent source of energy both currently and in the future.

Impact of climate change on irrigation water requirements for coffee plants in the fruit development stage: a case study of Dak Lak and Gia Lai provinces in the Central Highlands of Vietnam

Abstract This research investigates the impact of climate change on the irrigation water requirements for coffee plants in the fruit development stage in Gia Lai and Dak Lak provinces, Vietnam. Observation data were collected from four meteorological stations, namely, An Khe, Pleiku, Buon Ma Thuot, and Buon Ho. To simulate future scenarios, five CMIP6 models (BBC-CSM2-MR, CanESM5, GFDL-ESM4, MIROC6, and MPIESM1.2-HR) were used with the SSP24.5 scenario for the years 2030-2049 and 20802099. The irrigation water requirements were calculated by the Cropwat model version 8.0. For 2030-2049, the simulation results of BCC-CSM2-MR at An Khe showed the highest increase of approximately 90 mm, while CanESM5 displayed only a 4 mm rise. On the other hand, both MIROC6 and MPI-ESM1.2-HR indicated a slight reduction, ranging from 4 to 10 mm at all stations. For 20802099, the BBC-CSM2-MR model at An Khe recorded the highest increase of 100 mm, whereas the GFDL-ESM4 model showed a decline of 90 mm at Buon Ho. Other models showed a fluctuation increase from 40 to 60 mm. This means that climate change has a significant impact on the irrigation water requirements for coffee plants in the Central Highlands of Vietnam.

Decadal prediction of Northeast Asian winter precipitation with CMIP6 models

This study evaluates the decadal prediction skill of 13 forecast systems in predicting winter precipitation over Eurasia, contributing to the Decadal Climate Prediction Project of the Coupled Model Intercomparison Project Phase 6. Northeast Asia stands out as a region with improved decadal prediction skill for forecast years 2–5 due to the initialization. Observations show anticyclonic and cyclonic wind anomalies over the North Pacific and Northeast Asia, respectively, with southwesterly flow to the east of Northeast Asia. Ten forecast systems reproduce such circulation anomalies favoring abundant winter precipitation in Northeast Asia. The significant positive (negative) correlations between the detrended Northeast Asian precipitation (NEAP) and AMV (PDO-like) time series are reproduced by seven (nine) forecast systems. However, most forecast systems underestimate the correlation between the NEAP and the AMV, and have relatively low skill in predicting the PDO. Further improvements in these aspects will help to improve the decadal prediction skill of winter precipitation over Northeast Asia. The multi-model ensemble (MME) is able to reproduce both links of NEAP with AMV and PDO-like variability. The MME demonstrates significant skill and outperforms the individual forecast systems in predicting the NEAP for all 4-year averaged periods in the range of 1–8 years, demonstrating the benefits of using the ensemble mean of multiple models.

Temperature and precipitation trend analysis using the CMIP6 model in the Upper East region of Ghana

Temperature and precipitation trend analysis using the CMIP6 model in the Upper East region of Ghana

Projections of Greenland climate change from CMIP5 and CMIP6

As the Arctic continues to warm faster than the global average, the Greenland ice sheet faces a warmer future without restrictions on greenhouse gas emissions. In this study, 24 CMIP5 and 21 CMIP6 models were used to analyze projections of precipitation, 2 m or near-surface air temperature (T2m), and 500 hPa geopotential height (Z500) over Greenland under medium and high emission scenarios. The multi-model ensemble means indicate that CMIP6 is a warmer output with more precipitation in Greenland under medium and high emission scenarios relative to CMIP5. At the end of the 21st century (2071–2100), annual integrated precipitation in Greenland under RCP8.5 (SSP58.5) is projected to increase by 277.9 (281.3) Gt relative to the 1986–2005 climatological mean, with a significant positive trend of 38.5 (42.9) Gt/dec (p < 0.05) post-2020, approximately twice that under RCP4.5 (SSP24.5). Under high emission scenarios, T2m will steadily increase by 5.2 (5.7) °C at the end of the 21st century and the northern amplifying effect of warming is more intense. In this context, northeast Greenland will increase the highest precipitation, with a trend of >30 mm/dec during 2020–2100. The Z500 rises higher from southeast to northwest Greenland due to global warming under medium and high emission scenarios. Model and scenario uncertainties are major sources of uncertainty at the end of the 21st century. Compared to CMIP5, the model uncertainty of CMIP6 decreases more but remained higher at the end of this century. The projections of northern Greenland are more reliable than those of other regions, with a lower total fractional uncertainty and a greater signal-to-noise ratio. Furthermore, we did not find an additional T2m increase due to internal variability. Under medium and high emission scenarios, Greenland near surface air warming is major owed to global warming. Considering the importance of current circulation anomalies and after removing the artificial increase in Z500, the decrease in ΔZ500 indicates that future warming has been underestimated under medium and high emission scenarios.

Assessment of CMIP5 and CMIP6 AMIP Simulated Clouds and Surface Shortwave Radiation Using ARM Observations over Different Climate Regions

Abstract The simulations of clouds and surface radiation from 10 CMIP6 models and their CMIP5 predecessors are compared to the ARM ground-based observations over different climate regions. Compared to the ARM radar-lidar derived total cloud fractions (CFT) and cloud fraction vertical distributions over the six selected sites, both CMIP5 and CMIP6 significantly underestimated CFT and low-level CF over the Northern Hemispheric midlatitude sites (SGPC1 and ENAC1), although the biases are generally smaller in CMIP6. Over the tropical oceanic site (TWPC2), 5 out of 10 CMIP6 models better simulated low-level CF than their CMIP5 predecessors. CMIP6 simulations generally agreed well with the ARM observations in CFT and cloud fraction vertical distributions over the tropical continental (MAOM1) and coastal (TWPC3) sites but missed the transitions between dry and wet seasons, similar to CMIP5 simulations. The improvements in downwelling shortwave fluxes (SWdn) at the surface from the majority of CMIP6 compared to CMIP5 primarily resulted from the improved cloud fraction simulations, especially over the SGPC1, ENAC1, and TWPC3 sites. By contrast, both CMIP5 and CMIP6 models exhibited diverse performances of clouds and shortwave radiation over the Arctic site (NSAC1), where CMIP6 models produced more clouds than CMIP5 models, especially for the low-level clouds. The comparisons between observations and CMIP5 and CMIP6 simulations provide valuable quantitative assessments of the accuracy of mean states and variabilities in the model simulations and shed light on general directions to improve climate models in different regions.

Analysis of Precipitation Diurnal Cycle and Variance in Multiple Observations, CMIP6 Models, and a Series of GFDL-AM4.0 Simulations

Abstract The diurnal cycle of precipitation and precipitation variances at different time scales are analyzed in this study based on multiple high-resolution 3-h precipitation datasets. The results are used to evaluate nine CMIP6 models and a series of GFDL-AM4.0 model simulations, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these two precipitation features. It is found that although diurnal amplitudes are reasonably simulated, models generally generate too early diurnal peaks over land, with a diurnal phase peaking around noon instead of the observed late afternoon (or early evening) peak. As for precipitation variances, irregular subdaily fluctuations dominate the total variance, followed by variance of daily mean precipitation and variance associated with the mean diurnal cycle. While the spatial and zonal distributions of precipitation variances are generally captured by the models, significant biases are present in tropical regions, where large mean precipitation biases are observed. The comparisons based on AM4.0 model simulations demonstrate that the inclusion of ocean coupling, adoption of a new microphysics scheme, and increasing of horizontal resolution have limited impacts on these two simulated features, emphasizing the need for future investigation into these model deficiencies at the process level. Conducting routine examinations of these metrics would be a crucial first step toward better simulation of precipitation intermittence in future model development. Last, distinct differences in these two features are found among observational datasets, highlighting the urgent need for a detailed evaluation of precipitation observations, especially at subdaily time scales, as model evaluation heavily relies on high-quality observations. Significance Statement High-frequency precipitation data, such as 3-hourly or finer resolution, provide detailed and precise information about the intensity, timing, and location of individual precipitation events. This information is essential for evaluating physically based numerical weather and climate models, which are important tools for understanding and predicting precipitation changes. We compared several global high-resolution observation datasets with nine CMIP6 GCMs and a series of GFDL-AM4.0 model simulations to evaluate the precipitation diurnal cycle and variance, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these metrics. Despite the impact of these factors on the simulated precipitation diurnal cycle and variance being evident, our results also show that they are not consistently aligned with observed features. This highlights the need for further investigation into model deficiencies at the process level. Therefore, conducting routine examinations of these metrics could be a crucial first step toward improving the simulation of precipitation intermittency in future model development. Additionally, given the large uncertainties, there is an urgent need for a detailed evaluation of observational precipitation products, particularly at subdaily time scales.

Synergistic Effect of Warming in the Tropical Indian Ocean and North Tropical Atlantic on the Central-Pacific Type of La Niña Based on Observations and CMIP5

Abstract Previous studies have indicated that boreal winter-to-spring sea surface temperature anomalies (SSTA) over the tropical Atlantic or Indian Ocean can trigger the central-Pacific (CP) type of ENSO in the following winter due to winds over the western Pacific. Here, with the aid of observational data and CMIP5 model simulations, we demonstrate that the ability of the winter-to-spring north tropical Atlantic (NTA) SSTA or Indian Ocean Basin (IOB) mode to initiate CP ENSO events in the following winter may strongly depend on each other. Most warming events of the IOB and NTA, which are followed by CP La Niña events, are concomitant. The synergistic effect of the IOB and NTA SSTA may produce greater CP ENSO events in the subsequent winter via Walker circulation adjustments. The impacts between warming and cooling events of the IOB and NTA SSTA are asymmetric. IOB and NTA warmings appear to contribute to the subsequent CP La Niña development, which is much greater than IOB and NTA cooling contributing to CP El Niño. Overall, a combination of the IOB and NTA SSTA precursors may improve predictions of La Niña events. Significance Statement Although boreal winter-to-spring sea surface temperature anomalies over the tropical Atlantic or Indian Ocean can trigger central-Pacific (CP) ENSO in the following winter, it is not yet clear whether the effects of these two basins are independent. The purpose of this study is to better understand the joint effect of these two basins on CP ENSO events. We demonstrate that the ability of the north tropical Atlantic (NTA) SSTA to initiate CP ENSO events in the following winter may strongly depend on the state of the Indian Ocean Basin mode (IOB). The synergistic impact of these two basins may produce stronger CP ENSO events. These results highlight the role of three-ocean interactions in ENSO diversity and prediction.

Relationship between the AMOC and the Multidecadal Variability of the Midlatitude Southern Indian Ocean

Abstract Air–sea coupling system in the southwestern Indian Ocean (SWIO; 35°–55°S, 40°–75°E) exhibits predominant multidecadal variability that is the strongest during austral summer. It is characterized by an equivalent barotropic atmospheric high (low) pressure over warm (cold) sea surface temperature (SST) anomalies and a poleward (equatorward) shift of the westerlies during the positive (negative) phase. In this study, physical processes of this multidecadal variability are investigated by using observations/reanalysis and CMIP6 model simulations. Results suggest that the multidecadal fluctuation can be explained by the modulation of the Atlantic meridional overturning circulation (AMOC) and the local air–sea positive feedback in the SWIO. In both observations/reanalysis and CMIP6 model simulations, the AMOC fluctuation presents a significantly negative correlation with the multidecadal SST variation in the SWIO when the AMOC is leading by about a decade. The mechanisms are that the preceding AMOC variation can cause an interhemispheric dipolar pattern of SST anomalies in the Atlantic Ocean. Subsequently, the SST anomalies in the midlatitudes of the South Atlantic can propagate to the SWIO by the oceanic Rossby wave under the influence of the Antarctic Circumpolar Current (ACC). Once the SST anomalies reach the SWIO, these SST anomalies in the oceanic front can affect the baroclinicity in the lower troposphere to influence the synoptic transient eddy and then cause the atmospheric circulation anomaly via the eddy–mean flow interaction. Subsequently, the anomalous atmospheric circulation over the SWIO can significantly strengthen the SST anomalies through modifying the oceanic meridional temperature advection and latent and sensible heat flux.

Weak interannual variability of sea–air carbon flux in the Tropical Pacific Ocean simulated by CMIP6 models

Air–sea carbon fluxes (FCO2) simulated by 20 CMIP6 models in the pre-industrial control experiments are analyzed. 13 models can simulate the dominant role of the equatorial Pacific (EP) in the interannual variation of global FCO2, which is related to ENSO. Compared with CMIP5, CMIP6 has improved the simulation performance of interannual variation of global FCO2, while for the simulation of FCO2 in the tropical Pacific, most CMIP6 models inherit some biases of their CMIP5 versions. For example, the simulated interannual fluctuation of precipitation leads to excessive interannual variations of the dissolved inorganic carbon (DIC) concentration and then of the partial pressure of CO2 at the sea surface (pCO2sea) in the western tropical Pacific, especially in the warm pool. Another inherited disadvantage is the underestimated interannual fluctuation of FCO2 in the central tropical Pacific from 120ºW to 180ºW. The underestimation includes two aspects: the simulated intensity of the fluctuation is weak, and the negative response region of CO2 outgassing to the ENSO index is narrow. There are two main reasons for the weak interannual fluctuation strength of FCO2 in the models. One is that most models cannot represent the strong interannual variation of 10 m wind speed (vm10) in the central tropical Pacific, and the other is that the interannual fluctuation of pCO2sea in the central tropical Pacific in most models is too weak, which is related to the overestimated interannual variation of sea surface temperature (SST) here. The underestimated spatial range of negative response of CO2 outgassing to the ENSO index is mainly induced by the simulated interannual variations of the vm10 and of the vertical transport of DIC which is related to the vertical upwelling rate. According to the simulation results of CMIP6, the improvement of the simulation performance of the interannual variations of the global and tropical Pacific FCO2 is still dependent on the improvement of the simulation performance of physical climate, such as wind fields, SST, and the entrainment velocity at the bottom of the mixed layer.

Evaluation of CMIP6 model performance in simulating the PDO and its future change

This study comprehensively assesses the model performance in simulating the variation of the Pacific Decadal Oscillation (PDO) in the winter time (November to March) during the historical period of 1870–2014 using 40 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6). Its future change is also investigated under four Shared Socioeconomic Pathway scenarios on the basis of 17 CMIP6 models. Results show that CMIP6 models can reasonably reproduce the observed sea surface temperature anomaly mode associated with the PDO, with high spatial correlation coefficients of above 0.7. However, CMIP6 models generally perform poorly in simulating the variability of the PDO, with low correlation coefficients for most models, except FGOALS-g3 (0.57), GISS-E2-1-H (0.42), and NESM3 (0.37), which present a relatively greater correlation. The multi-model ensemble (MME) outperforms the individual models and can capture the dominant periodicity of the PDO (∼50 years), but it fails to capture the periodicity of ∼20 years. Additionally, the MME result underestimates the variability of the PDO. In response to future warming, the PDO is expected to transfer from a negative to positive phase around the 2050s. The 50-year periodicity of the PDO is projected to decrease compared to the historical period.摘要本文基于40个CMIP6模式输出结果, 系统评估了模式对历史时期太平洋年代际振荡(PDO)的模拟性能, 并利用其中17个模式的4种共享社会经济路径情景数据预估了PDO未来可能变化趋势. 结果表明, CMIP6模式能够合理再现PDO相关海表温度异常的空间模态, 但模式模拟PDO位相演变的能力普遍较弱. 多模式集合能够合理再现PDO的50年左右周期, 但无法模拟出其20年左右周期, 并且低估了PDO的变化幅度. 在未来变暖情景下, PDO可能在2050左右出现由负位相向正位相的转变, 同时其50年左右周期也将明显缩短.

Parameterized orographic gravity wave drag and dynamical effects in CMIP6 models

Orographic gravity waves (OGWs) are an important mechanism for coupling of the free atmosphere with the surface, mediating the momentum and energy transport and influencing the dynamics and circulation especially in the middle-atmosphere. Current global climate models are not able to resolve a large part of the OGW spectrum and hence, OGW effects have to be parameterized in the models. Typically, the only parameterized effect is the OGW induced drag. Despite producing the same quantity as an output and relying on similar assumptions (e.g. instantaneous vertical propagation), the individual OGW parameterization schemes differ in many aspects such as handling of the orography, the inclusion of non-linear effects near the surface and the tuning of the emergent free parameters. In this study, we have reviewed 7 different parameterizations, which are used in 9 different CMIP6 models. We report pronounced differences in the vertical distribution and magnitude of the parameterized OGW drag between the models and study to what extent the inter-model differences can be traced back to the difference in the type and tuning of the schemes. Finally, we demonstrate how the OGW drag differences project to the intermodel differences in the stratospheric dynamics. The study can pave the way for a more systematic research of the OGW parameterizations in the future, with an ultimate goal of lowering the amount of uncertainty of the future climate projections connected with the parameterized effects of unresolved orography.

Salinity data curation using CMIP6 projections and artificial neural network for the Bay of Bengal

ABSTRACT This study addresses the underrepresentation of the Bay of Bengal (BoB) in CMIP6 models, despite its significance in climate change research. Our objective is to propose a methodology to bridge this gap. Through skill score analysis, we identify CIESM as the optimal CMIP6 model for accurately estimating sea surface salinity in the northern BoB. Furthermore, we introduce the novel application of machine learning, specifically artificial neural networks, to enhance predictions from CIESM. This study pioneers the use of ML techniques to improve CMIP6 data for the BoB. Moreover, we examine the relationship between salinity and pH in the BoB, considering the influence of freshwater discharge from multiple rivers. Our findings demonstrate a positive correlation between salinity and pH, highlighting the role of freshwater influx in suppressing acidification. As the intensity of freshwater spread increases, the rate of acidification diminishes. This analysis method presents an efficient approach to curate oceanic parameter data like salinity, contributing to a better understanding of the BoB’s dynamics.

A meta-analysis of peatland microbial diversity and function responses to climate change

Climate change threatens the capacity of peatlands to continue storing carbon (C) belowground. Microorganisms are crucial in regulating the peatland C sink function, but how climate change affects the richness, biomass and functions of peatland microbiomes still remains uncertain. Here, we conducted a global meta-analysis of the response of peatland bacterial, fungal and micro-eukaryote communities to climate change by synthesizing data from 120 climate change experiments. We show that climate drivers such as warming, drought and warming-induced vegetation shift strongly affect microbial diversity, community composition, trophic structure and functions. Using meta-analytic structural equation modelling, we developed a causal understanding among the different strands of microbial properties. We found that climate drivers influenced microbial metabolic rates, such as CO2 fixation and respiration, methane production and oxidation, directly through physiological effects, and indirectly, through microbial species turnover and shifts in the trophic structure of microbial communities. In particular, we found that the response of microbial CO2 fixation increased for each degree in air temperature gained, while the response of microbial CO2 respiration tended to decline. When extrapolated at the global peatland scale using the CMIP6 model under the SSP5-8.5 scenario, our findings suggest that the increasingly positive response of microbial CO2 fixation to temperature anomalies in northern latitudes might compensate to some extent for the possible loss of C from microbial CO2 respiration, possibly allowing peatlands to remain C sinks on long-term. Our findings have crucial implications for advancing our understanding of carbon-climate feedback from peatlands in a warming world.