Coupled Climate Model Research Articles

Semi‐Automatic Tuning of Coupled Climate Models With Multiple Intrinsic Timescales: Lessons Learned From the Lorenz96 Model

AbstractThe objective of this study is to evaluate the potential for History Matching (HM) to tune a climate system with multi‐scale dynamics. By considering a toy climate model, namely, the two‐scale Lorenz96 model and producing experiments in perfect‐model setting, we explore in detail how several built‐in choices need to be carefully tested. We also demonstrate the importance of introducing physical expertise in the range of parameters, a priori to running HM. Finally we revisit a classical procedure in climate model tuning, that consists of tuning the slow and fast components separately. By doing so in the Lorenz96 model, we illustrate the non‐uniqueness of plausible parameters and highlight the specificity of metrics emerging from the coupling. This paper contributes also to bridging the communities of uncertainty quantification, machine learning and climate modeling, by making connections between the terms used by each community for the same concept and presenting promising collaboration avenues that would benefit climate modeling research.

Projected Impacts of Antarctic Meltwater Anomalies over the Twenty-First Century

Abstract Antarctic margin and Southern Ocean surface freshening has been observed in recent decades and is projected to continue over the twenty-first century. Surface freshening due to precipitation and sea ice changes is represented in coupled climate models; however, Antarctic ice sheet/shelf meltwater contributions are not. Because Antarctic melting is projected to accelerate over the twenty-first century, this constitutes a fundamental shortcoming in present-day projections of high-latitude climate. Southern Ocean surface freshening has been shown to cause surface cooling by reducing both ocean convection and the entrainment of warm subsurface waters to the surface. Over the twenty-first century, Antarctic meltwater is expected to alter the pattern of projected surface warming as well as having other climatic effects. However, there remains considerable uncertainty in projected Antarctic meltwater amounts, and previous findings could be model dependent. Here, we use the ACCESS-ESM1.5 coupled model to investigate global climate responses to low and high Antarctic meltwater additions over the twenty-first century under a high-emissions climate scenario. Our high-meltwater simulations produce anomalous surface cooling, increased Antarctic sea ice, subsurface ocean warming, and hemispheric differences in precipitation. Our low-meltwater simulations suggest that the magnitude of surface temperature and Antarctic sea ice responses is strongly dependent on the applied meltwater amount. Taken together, these findings highlight the importance of constraining projections of Antarctic ice sheet/shelf melt to better project global surface climate changes over the twenty-first century. Significance Statement Antarctic ice sheets and shelves are melting, adding meltwater to the Southern Ocean and changing the ocean circulation. Antarctic meltwater stratifies the upper ocean, resulting in cooling of the surface Southern Ocean but warming at depth that could accelerate ice shelf melting. Coupled climate models used to project twenty-first-century climate do not represent ice sheets or shelves, neglecting important climate impacts. Here we conduct meltwater simulations with a coupled climate model and find that the magnitude of climate responses is strongly dependent on the applied meltwater amount. This highlights 1) the importance of constraining Antarctic meltwater projections to better project global climate over the twenty-first century and 2) that it is important that Antarctic meltwater be represented in future-generation coupled climate models.

A simple diagnostic based on sea surface height with an application to central Pacific ENSO

Abstract. We use output from a freely running NEMO model simulation for the equatorial Pacific to investigate the utility of linearly removing the local influence of vertical displacements of the thermocline from variations in sea surface height. We show that the resulting time series of residual sea surface height, denoted ηnlti, measures variations in near-surface heat content that are independent of the local vertical displacement of the thermocline and can arise from horizontal advection, surface heat flux, and diapycnal mixing processes. We find that the variance of ηnlti and its correlation with sea surface temperature are focused on the Niño4 region. Furthermore, ηnlti averaged over the Niño4 region is highly correlated with indices of central Pacific El Niño–Southern Oscillation (CP ENSO), and its variance in 21-year running windows shows a strong upward trend over the past 50 years, corresponding to the emergence of CP ENSO following the 1976/77 climate shift. We show that ηnlti can be estimated from observations, using satellite altimeter data and a linear multi-mode model. The time series of ηnlti, especially when estimated using the linear model, show pronounced westward propagation in the western equatorial Pacific, arguing for an important role for zonal advective feedback in the dynamics of CP ENSO, in particular for cold events. We also present evidence that the role of the thermocline displacement in influencing sea surface height increased strongly after 2000 in the eastern part of the Niño4 region, at a time when CP ENSO was particularly active. Finally, the diagnostic is easy to compute and can be easily applied to mooring data or coupled climate models.

Role of mean, variability and teleconnection of clouds behind Indian summer monsoon rainfall

AbstractSkilful prediction of the seasonal Indian summer monsoon (ISM) rainfall (ISMR) at least one season in advance has great socio‐economic value. The ISM is a lifeline for about a sixth of the world's population. The ISMR prediction remained a challenging problem with the subcritical skills of the dynamical models due to a limited understanding of the interaction among clouds, convection and circulation. In this study, we have analysed the seasonal mean of high cloud fraction, ice mixing ratio and ice cloud fraction from satellite and reanalysis and demonstrated their importance for ISM. The variability of the mixing ratio of cloud ice in different time scales (3–7 days, 10–20 days and 30–60 days bands) is also examined from reanalysis during ISM. Here, we have shown the teleconnection of different cloud variables over the ISM region with global sea surface temperature. We found that they are tied with slowly varying forcing (e.g., El Niño and Southern Oscillation). Besides, the correlation of cloud ice with different indices (Niño, Pacific Decadal Oscillation, North Atlantic Oscillation and Extratropics) may enhance the potential predictability of ISMR. The representation of deep convective clouds, which involve the ice‐phase processes in a coupled climate model, strongly modulates ISMR variability in association with global predictors. The results from the two sensitivity simulations using coupled global climate model (CGCM) demonstrate the importance of the cloud ice on ISM rainfall predictability. Therefore, this study provides a scientific basis for improving the simulation of the seasonal ISMR by developing the physical processes of the cloud on a subseasonal time scale and motivating further research in this direction.

Nudging allows direct evaluation of coupled climate models with in situ observations: a case study from the MOSAiC expedition

Abstract. Comparing the output of general circulation models to observations is essential for assessing and improving the quality of models. While numerical weather prediction models are routinely assessed against a large array of observations, comparing climate models and observations usually requires long time series to build robust statistics. Here, we show that by nudging the large-scale atmospheric circulation in coupled climate models, model output can be compared to local observations for individual days. We illustrate this for three climate models during a period in April 2020 when a warm air intrusion reached the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition in the central Arctic. Radiosondes, cloud remote sensing and surface flux observations from the MOSAiC expedition serve as reference observations. The climate models AWI-CM1/ECHAM and AWI-CM3/IFS miss the diurnal cycle of surface temperature in spring, likely because both models assume the snowpack on ice to have a uniform temperature. CAM6, a model that uses three layers to represent snow temperature, represents the diurnal cycle more realistically. During a cold and dry period with pervasive thin mixed-phase clouds, AWI-CM1/ECHAM only produces partial cloud cover and overestimates downwelling shortwave radiation at the surface. AWI-CM3/IFS produces a closed cloud cover but misses cloud liquid water. Our results show that nudging the large-scale circulation to the observed state allows a meaningful comparison of climate model output even to short-term observational campaigns. We suggest that nudging can simplify and accelerate the pathway from observations to climate model improvements and substantially extends the range of observations suitable for model evaluation.

Abyssal ocean overturning slowdown and warming driven by Antarctic meltwater.

The abyssal ocean circulation is a key component of the global meridional overturning circulation, cycling heat, carbon, oxygen and nutrients throughout the world ocean1,2. The strongest historical trend observed in the abyssal ocean is warming at high southern latitudes2-4, yet it is unclear what processes have driven this warming, and whether this warming is linked to a slowdown in the ocean's overturning circulation. Furthermore, attributing change to specific drivers is difficult owing to limited measurements, and because coupled climate models exhibit biases in the region5-7. In addition, future change remains uncertain, with the latest coordinated climate model projections not accounting for dynamic ice-sheet melt. Here we use a transient forced high-resolution coupled ocean-sea-ice model to show that under a high-emissions scenario, abyssal warming is set to accelerate over the next 30 years. We find that meltwater input around Antarctica drives a contraction of Antarctic Bottom Water (AABW), opening a pathway that allows warm Circumpolar Deep Water greater access to the continental shelf. The reduction in AABW formation results in warming and ageing of the abyssal ocean, consistent with recent measurements. In contrast, projected wind and thermal forcing has little impact on the properties, age and volume of AABW. These results highlight the critical importance of Antarctic meltwater in setting the abyssal ocean overturning, with implications for global ocean biogeochemistry and climate that could last for centuries.

Technical note: Unsupervised classification of ozone profiles in UKESM1

Abstract. The vertical distribution of ozone in the atmosphere, which features complex spatial and temporal variability set by a balance of production, loss, and advection, is relevant for both surface air pollution and climate via its role in radiative forcing. At present, the way in which regions of coherent ozone structure are defined relies on somewhat arbitrarily drawn boundaries. Here we consider a more general, data-driven method for defining coherent regimes of ozone structure. We apply an unsupervised classification technique called Gaussian mixture modeling (GMM), which represents the underlying distribution of ozone profiles as a linear combination of multi-dimensional Gaussian functions. In doing so, GMM identifies coherent groups or subpopulations of the ozone profile distribution. As a proof-of-concept study, we apply GMM to ozone profiles from three subsets of the UKESM1 coupled climate model runs carried out for CMIP6: specifically, the seasonal mean of a historical subset (2009–2014) and two subsets from two different future climate projections (i.e., SSP1-2.6 and SSP5-8.5). Despite not being given any spatiotemporal information, GMM identifies several spatially coherent regions of ozone structure. Using a combination of statistical guidance and post hoc judgment, we select a six-class representation of global ozone, consisting of two tropical classes and four mid-to-high-latitude classes. The tropical classes feature a relatively high-altitude tropopause, while the higher-latitude classes feature a lower-altitude tropopause and low values of tropospheric ozone, as expected based on broad patterns observed in the atmosphere. Both of the future projections feature lower ozone concentrations at 850 hPa than the historical benchmark, with signatures of ozone hole recovery. We find that the area occupied by the tropical classes is expanded in both future projections, which are most prominent during austral summer. Our results suggest that GMM may be a useful method for identifying coherent ozone regimes, particularly in the context of model analysis.

Coupled and Stand-Alone Regional Climate Modeling of Intensive Storms in Western Canada

A coupled atmospheric-hydrologic system models the complex interactions between the land surface and the atmospheric boundary layer and the water-energy cycle from groundwater across the land surface to the top of the atmosphere. A regional climate model called weather research forecasting (WRF) was coupled with a land-surface scheme (Noah) to simulate intensive storms in Alberta of Canada. Accounting for the land-atmosphere feedback enhances the predictability of the fine-tuned WRF-Noah system. Soil moisture, vegetation, and land-surface temperature influence latent and sensible heat fluxes and modulate both thermal and dynamical characteristics of land and the lower atmosphere. WRF was set up in a two-way, 3-domain nested framework so that the output of the outermost domain (D1) was used to run the 2nd domain (D2), and the output of D2 was used to run the innermost, 3rd domain (D3). By this 2-way nesting, D3 and D2 provide the feedback to their outer domains (D2 and D1), respectively. D3 was set at a 3-km resolution adequate to simulate convective storms. WRF-Noah was forced with climate outputs from global climate models (GCMs) for the baseline period 1980–2005. A regional frequency analysis and a quantile-quantile bias correction method were applied to develop intensity-duration-frequency (IDF) curves using precipitation data simulated by WRF-Noah. The simulated baseline precipitation of central Alberta agrees well with observed data from a 13-rain gauge network of the City of Edmonton. The 5th-generation mesoscale atmospheric model (MM5) of National Center for Atmospheric Research was also set up in a 3-domain, but 1-way nesting configuration. As expected, after bias correction, precipitation simulated by MM5 was less accurate than that simulated by WRF-Noah. For storms of short durations and return periods of more than 25 years, both MM5 driven by special reports on emission scenario climate scenarios of coupled model inter-comparison project (CMIP3) and WRF-Noah driven by representative concentration pathway climate scenarios of CMIP5 projected storm intensities in central Alberta to increase from the base period to the 2050s and to the 2080s.

Revisiting Interior Water Mass Responses to Surface Forcing Changes and the Subsequent Effects on Overturning in the Southern Ocean

AbstractTwo coupled climate models, differing primarily in horizontal resolution and treatment of mesoscale eddies, were used to assess the impact of perturbations in wind stress and Antarctic ice sheet (AIS) melting on the Southern Ocean meridional overturning circulation (SO MOC), which plays an important role in global climate regulation. The largest impact is found in the SO MOC lower limb, associated with the formation of Antarctic Bottom Water (AABW), which in both models is enhanced by wind and weakened by AIS meltwater perturbations. Even though both models under the AIS melting perturbation show similar AABW transport reductions of 4–5 Sv (50%–60%), the volume deflation of AABW south of 30°S is four times greater in the higher resolution simulation (−20 vs. −5 Sv). Water mass transformation (WMT) analysis reveals that surface‐forced dense water formation on the Antarctic shelf is absent in the higher resolution and reduced by half in the lower resolution model in response to the increased AIS melting. However, the decline of the AABW volume (and its inter‐model difference) far exceeds the surface‐forced WMT changes alone, which indicates that the divergent model responses arise from interactions between changes in surface forcing and interior mixing processes. This model divergence demonstrates an important source of uncertainty in climate modeling, and indicates that accurate shelf processes together with scenarios accounting for AIS melting are necessary for robust projections of the deep ocean's response to anthropogenic forcing.

A deep learning–based U-Net model for ENSO-related precipitation responses to sea surface temperature anomalies over the tropical Pacific

SST–precipitation feedback plays an important role in ENSO evolution over the tropical Pacific and thus it is critically important to realistically represent precipitation-induced feedback for accurate simulations and predictions of ENSO. Typically, in hybrid coupled modeling for ENSO predictions, statistical atmospheric models are adopted to determine linear precipitation responses to interannual SST anomalies. However, in current coupled climate models, the observed precipitation–SST relationship is not well represented. In this study, a data-driven deep learning-based U-Net model was used to construct a nonlinear response model of interannual precipitation variability to SST anomalies. It was found that the U-Net model outperformed the traditional EOF-based method in calculating the precipitation variability. Particularly over the western-central tropical Pacific, the mean-square error (MSE) of the precipitation estimates in the U-Net model was smaller than that in the EOF model. The performance of the U-Net model was further improved when additional tendency information on SST and precipitation variability was also introduced as input variables, leading to a pronounced MSE reduction over the ITCZ.摘要SST–降水反馈过程在热带太平洋ENSO演变过程中起着重要作用, 能否真实地在数值模式中表征SST–降水年际异常之间的关系及相关反馈过程, 对于准确模拟和预测ENSO至关重要. 例如, 在一些模拟ENSO的混合型耦合模式中, 通常采用大气统计模型 (如经验正交函数; EOF) 来表征降水 (海气界面淡水通量的一个重要分量) 对SST年际异常的线性响应. 然而在当前的耦合模式中, 真实观测到的降水–SST统计关系还不能被很好地再现出来, 从而引起 ENSO模拟误差和不确定性. 在本研究中, 使用基于深度学习的U-Net模型来构建热带太平洋降水异常场对SST年际异常的非线性响应模型. 研究发现: U-Net模型的性能优于传统的基于EOF方法的模型. 特别是在热带西太平洋海区, U-Net模型估算的降水误差远小于EOF模型的模拟. 此外, 当SST和降水异常的趋势信息作为输入变量也被同时引入以进一步约束模式训练时, U-Net模型的性能可以进一步提高, 如能使热带辐合带区域的误差显著降低.

Direct Comparison Between a Non‐Orographic Gravity Wave Drag Scheme and Constant Level Balloons

AbstractThe parameterization scheme that represents gravity waves due to convection in LMDz‐6A, the atmospheric components of the IPSL coupled climate model (IPSLCM6), is directly compared to Strateole‐2 balloon observations made in the lower tropical stratosphere from November 2019 to February 2020. The input meteorological fields necessary to run the parameterization offline are extracted from the ERA5 reanalysis and correspond to the instantaneous meteorological conditions found underneath the balloons. In general, we find a fair agreement between measurements of the momentum fluxes due to waves with periods less than 1 hr and the parameterization. The correlation of the daily values between the observations and the results of the parameterization is around 0.4, which is statistically elevated considering that we analyze around 600 days of data and surprisingly good considering that the parameterization has not been tuned: the scheme is just the standard one that helps producing a quasi‐biennial oscillation (QBO) in the IPSLCM6 model. Online simulations also show that the measured values of momentum fluxes are well representative of the zonally and averaged values of momentum fluxes needed in LMDz‐6A to simulate a QBO. The observations also show that longer waves with periods smaller than a day carry about twice as much flux as waves with periods smaller than an hour, which is a challenge since the low period waves that make the difference are potentially in the “gray zone” of most climate models.

Multiyear dry periods in Southern Africa

AbstractCharacteristics and physical features related to low precipitation across many years in Southern Africa that lead to societal disruptions are diagnosed using observed analyses and an ensemble of historical coupled climate model simulations during 1921 to 2014. Four regions are evaluated, as identified through a hierarchical clustering algorithm applied to the Standardized Precipitation Index (SPI) during the October–April precipitation season. Although dryness spanning many October–April occurs periodically in each region, they seldom occur simultaneously, consistent with largely insignificant SPI cross‐correlations between them. However, characteristics relevant to low precipitation across many years are generalizable between the four regions, including the serial persistence of October–April precipitation, the likelihood of consecutive dry October–April, and the likelihood of dry October–April in temporal extents of up to 10 consecutive such 7‐month seasons. Systematic precipitation persistence is not a feature in any of the four Southern Africa regions, as serial correlations of October–April SPI are not statistically significant at any time lags. It follows that there is an exponential‐folding decay in the likelihood of consecutive October–April for various SPI thresholds and that there is a large spread in the likelihood of low October–April SPI across many years. In terms of physical features, low October–April SPI in each Southern Africa region is closely related to local atmospheric circulations; however, they are not as closely related to sea surface temperatures (SSTs). These results suggest that dryness spanning many years is determined primarily by persistent local circulations related to atmospheric variability and to a lesser extent variability related to SST anomalies, including the El Niño–Southern Oscillation.

Climate Responses Under an Extreme Quiet Sun Scenario

AbstractFundamental understanding of the climate responses to solar variability is obscured by the large and complex climate variability. This long‐standing issue is addressed here by examining climate responses under an extreme quiet sun (EQS) scenario, obtained by making the sun void of all magnetic fields. It is used to drive a coupled climate model with whole atmosphere and ocean components. The simulations reveal significant responses, and elucidate aspects of the responses to changes of troposphere/surface forcing and stratospheric forcing that are similar and those that are different. Planetary waves (PWs) play a key role in both regional‐scale responses and the mean circulation changes. Intermediate scale stationary waves and regional climate respond to solar forcing changes in the troposphere and stratosphere in a similar way, due to similar subtropical wind changes in the upper troposphere. The patterns of these changes are similar to those found in a warming climate, but with opposite signs. Responses of the largest scale PW during northern hemisphere and southern hemisphere winters differ, leading to hemispheric differences in the interplay between dynamical and radiative processes. The analysis exposes remarkable general similarities between climate responses in EQS simulations and those under nominal solar minimum conditions, even though the latter may not always appear to be statistically significant.

The Impact of Stochastic Mesoscale Weather Systems on the Atlantic Ocean

Abstract The ocean is forced by the atmosphere on a range of spatial and temporal scales. In numerical models the atmospheric resolution sets a limit on these scales and for typical climate models mesoscale (<500 km) atmospheric forcing is absent or misrepresented. Previous studies have demonstrated that mesoscale forcing significantly affects key ocean circulation systems such as the North Atlantic subpolar gyre (SPG) and the Atlantic meridional overturning circulation (AMOC). Here we present ocean model simulations that demonstrate that the addition of realistic mesoscale atmospheric forcing leads to coherent patterns of change: a cooler sea surface in the tropical and subtropical Atlantic Ocean and deeper mixed layers in the subpolar North Atlantic in autumn, winter, and spring. These lead to robust statistically significant increases in the volume transport of the North Atlantic SPG by 10% and the AMOC by up to 10%. Our simulations use a novel stochastic parameterization—based on a cellular automata algorithm—to represent spatially coherent weather systems realistically over a range of scales, including down to the smallest resolvable by the ocean grid (∼10 km). Convection-permitting atmospheric models predict changes in the intensity and frequency of mesoscale weather systems due to climate change, so representing them in coupled climate models would bring higher fidelity to future climate projections.

Long-term temporal evolution of extreme temperature in a warming Earth.

We present a new approach to modeling the future development of extreme temperatures globally and on the time-scale of several centuries by using non-stationary generalized extreme value distributions in combination with logistic functions. The statistical models we propose are applied to annual maxima of daily temperature data from fully coupled climate models spanning the years 1850 through 2300. They enable us to investigate how extremes will change depending on the geographic location not only in terms of the magnitude, but also in terms of the timing of the changes. We find that in general, changes in extremes are stronger and more rapid over land masses than over oceans. In addition, our statistical models allow for changes in the different parameters of the fitted generalized extreme value distributions (a location, a scale and a shape parameter) to take place independently and at varying time periods. Different statistical models are presented and the Bayesian Information Criterion is used for model selection. It turns out that in most regions, changes in mean and variance take place simultaneously while the shape parameter of the distribution is predicted to stay constant. In the Arctic region, however, a different picture emerges: There, climate variability is predicted to increase rather quickly in the second half of the twenty-first century, probably due to the melting of ice, whereas changes in the mean values take longer and come into effect later.

The Radiative and Cloud Responses to Sea Salt Aerosol Engineering in GFDL Models

AbstractMarine cloud brightening is a proposal to counteract global warming by increasing sea salt aerosol emissions. In theory, this increases the cloud droplet number concentration of subtropical marine stratocumulus decks, increasing cloud brightness and longevity. However, this theoretical progression remains uncertain in coupled climate models, especially the response of liquid water path and cloud fraction to aerosol seeding. We use the GFDL CM4 climate model to simulate marine cloud brightening following the published G4sea‐salt protocol, in which sea salt aerosol emissions are uniformly increased over 30 S–30 N in addition to standard forcings from a SSP2‐4.5 future warming scenario. The perturbed radiative and cloud responses are temporally stable though spatially heterogeneous, and direct scattering by the added sea salt predominates over changes to cloud reflectance. In fact, feedbacks in the coupled simulation lead to a net warming, rather than cooling, response by clouds.

How Well Can Current Climate Models Simulate the Connection of the Early Spring Aleutian Low to the Following Winter ENSO?

Abstract A recent study revealed an impact of the intensity of early spring Aleutian low (AL) on the succeeding winter ENSO. This study examines the ability of 41 climate models that participated in CMIP6 in simulating the early spring AL–winter ENSO connection. It is shown that there exists a large diversity among the models in simulating this AL–ENSO linkage. A number of models capture well the observed AL–ENSO connection and the associated physical processes. However, the AL–ENSO relation in several models is opposite to the observed. Diversity of the AL–ENSO connection is related to the spread in the spatial structure of AL-related atmospheric anomalies over the North Pacific. In the models that capture the observed AL–ENSO connection, weakened AL induces an anomalous anticyclone over the northern middle and high latitudes and an anomalous cyclone over the subtropical North Pacific. The resultant westerly wind anomalies over the tropical western-central Pacific (TWCP) induce an El Niño sea surface temperature (SST) anomaly pattern in the following winter. By contrast, in the models with the AL–ENSO relation opposite to the observations, the AL-associated anomalous anticyclone over the North Pacific extends too southward. As such, the subtropical North Pacific is dominated by northeasterly wind anomalies and SST cooling. The subtropical North Pacific SST cooling induces easterly wind anomalies over the TWCP via wind–evaporation–SST feedback, and leads to a La Niña anomaly pattern in the following winter. The spread in the spatial structure of the AL-associated atmospheric anomalies over the North Pacific is partly due to the diversity in the amplitude of the climatological mean flow. Significance Statement A recent study suggested that variation of the AL intensity in early spring could exert a significant impact on the following winter ENSO. It indicated that inclusion of the early spring AL signal could improve the prediction of ENSO and to some extent help reduce the spring predictability barrier of ENSO. To employ the AL as a predictor in the ENSO prediction and forecast, the current climate model should have the ability to simulate realistically the early spring AL variation as well as the physical process linking the early spring AL with the subsequent winter ENSO. Hence, this study examines the performance of the current coupled climate models that participated in the phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating the linkage between the early spring AL and the following winter ENSO. We show that there exists a large diversity among the CMIP6 models in simulating the early spring AL–winter ENSO connection. A number of models capture well the observed AL–ENSO connection and the associated physical processes. However, the AL–ENSO relation in several models is opposite to the observed. The factors leading to the spread are further examined. Results of this study would have implications in improving our understanding of the impact of extratropical atmospheric forcing on the ENSO and improving the seasonal forecasting of the ENSO.

Life Cycle of Major Sudden Stratospheric Warmings in the Southern Hemisphere from a Multimillennial GCM Simulation

Abstract Sudden stratospheric warmings (SSWs) are rare in the Southern Hemisphere (SH), making it difficult to study possible precursors or subsequent impacts. Using a multimillennial coupled climate model simulation producing 161 SSWs in the SH, we present a detailed study of their life cycle. We show that SH SSWs are predominantly displacement events forced by wave-1 planetary waves, and that a surface signature similar to the negative phase of the Southern Annular Mode (SAM) is detectable up to two months before the onset date, but there is a tendency for a transition from wave 1 before to zonally symmetric anomalies after onset. We identify a strong weakening of the Amundsen Sea low as one of the most prominent precursors, which weakens the climatological wave-2 and wave-3 stationary waves and strengthens wave-1 forcing. Compared to their northern counterparts, SH SSWs generally have a longer time scale, and while there is evidence of pre-onset forcing related to tropical sea surface temperatures, the Indian Ocean dipole is more important than El Niño–Southern Oscillation. Significance Statement Sudden stratospheric warmings (SSWs) are extreme events where the winter polar stratosphere warms within a few days to temperatures usually only experienced in summer. These events are rare in the Southern Hemisphere. Therefore, both the observational record and standard climate model simulations are not enough to understand how SSWs develop, or how they might change surface weather. Here we use very long global climate simulations that produce a large number of SSWs in the Southern Hemisphere to study the development and impact of these events. This includes possible precursors as well as the influence they have on surface weather after they occur.

Zonal current structure of the Indian Ocean in CMIP6 models

The Indian Ocean circulation system significantly affects local and global climate, with zonal currents as one of the most critical components. Zonal currents bridge the water mass and energy pathways between the eastern and western Indian Oceans. Nevertheless, their representation in the latest generation of coupled climate models has not been thoroughly evaluated. In this study, we examined the representation of the mean structure of the Indian Ocean zonal currents in 29 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We confirmed that the CMIP6 ensemble represents the Indian Ocean zonal current structure but with a shallower Equatorial Undercurrent (EUC) core and an overestimated South Equatorial Current (SEC). Further, we investigated the physical properties in the Indian Ocean to understand simulated biases and inter-model spread in CMIP6 models, and found that the simulated biases of zonal currents are accompanied by hydrography and wind forcing simulation biases. This study compares the performance of CMIP6 models for simulating the Indian Ocean zonal currents and provides a reference for further studies on the Indian Ocean circulation system using coupled climate models.

Exploring the nonstationarity of coastal sea level probability distributions

Abstract Studies agree on a significant global mean sea level rise in the 20th century and its recent 21st century acceleration in the satellite record. At regional scale, the evolution of sea level probability distributions is often assumed to be dominated by changes in the mean. However, a quantification of changes in distributional shapes in a changing climate is currently missing. To this end, we propose a novel framework quantifying significant changes in probability distributions from time series data. The framework first quantifies linear trends in quantiles through quantile regression. Quantile slopes are then projected onto a set of four orthogonal polynomials quantifying how such changes can be explained by independent shifts in the first four statistical moments. The framework proposed is theoretically founded, general, and can be applied to any climate observable with close-to-linear changes in distributions. We focus on observations and a coupled climate model (GFDL-CM4). In the historical period, trends in coastal daily sea level have been driven mainly by changes in the mean and can therefore be explained by a shift of the distribution with no change in shape. In the modeled world, robust changes in higher order moments emerge with increasing $ {\mathrm{CO}}_2 $ concentration. Such changes are driven in part by ocean circulation alone and get amplified by sea level pressure fluctuations, with possible consequences for sea level extremes attribution studies.