Climate Model Biases Research Articles

Age of Stratospheric Air: Progress on Processes, Observations, and Long‐Term Trends

AbstractAge of stratospheric air is a well established metric for the stratospheric transport circulation. Rooted in a robust theoretical framework, this approach offers the benefit of being deducible from observations of trace gases. Given potential climate‐induced changes, observational constraints on stratospheric circulation are crucial. In the past two decades, scientific progress has been made in three main areas: (a) Enhanced process understanding and the development of process diagnostics led to better quantification of individual transport processes from observations and to a better understanding of model deficits. (b) The global age of air climatology is now well constrained by observations thanks to improved quality and quantity of data, including global satellite data, and through improved and consistent age calculation methods. (c) It is well established and understood that global models predict a decrease in age, that is, an accelerating stratospheric circulation, in response to forcing by greenhouse gases and ozone depleting substances. Observational records now confirm long‐term forced trends in mean age in the lower stratosphere. However, in the mid‐stratosphere, uncertainties in observational records are too large to confirm or disprove the model predictions. Continuous monitoring of stratospheric trace gases and further improved methods to derive age from those tracers will be crucial to better constrain variability and long‐term trends from observations. Future work on mean age as a metric for stratospheric transport will be important due to its potential to enhance the understanding of stratospheric composition changes, address climate model biases, and assess the impacts of proposed climate geoengineering methods.

A Forensic Investigation of Climate Model Biases in Teleconnections: The Case of the Relationship Between ENSO and the Northern Stratospheric Polar Vortex

AbstractTeleconnections are crucial in shaping climate variability and regional climate change. The fidelity of teleconnections in climate models is important for reliable climate projections. As the observed sample size is limited, scientific judgment is required when models disagree with observed teleconnections. We illustrate this using the example of the relationship between El Niño‐Southern Oscillation (ENSO) and the northern stratospheric polar vortex (SPV), where the MIROC6 large ensemble exhibits an ENSO‐SPV correlation opposite in sign to observations. Yet the model well captures the upward planetary‐wave propagation pathway through which ENSO is known to affect the SPV. We show that the discrepancy arises from the model showing an additional linkage related to horizontal stratospheric wave propagation. Observations do not provide strong statistical evidence for or against the existence of this linkage. Thus, depending on the research purpose, a choice has to be made in how to use the model simulations. Under the assumption that the additional linkage is spurious, a physically‐based bias adjustment is applied to the SPV, which effectively aligns the modeled ENSO‐SPV relationship with the observations, and thereby removes the model‐observations discrepancy in the surface air temperature response. However, if one believed that the additional linkage was genuine and was undersampled in the observations, a different approach could be taken. Our study emphasizes that caution is needed when concluding that a model is not suitable for studying teleconnections. We propose a forensic approach and argue that it helps to better understand model performance and utilize climate model data more effectively.

Elevation-dependent biases of raw and bias-adjusted EURO-CORDEX regional climate models in the European Alps

Data from the EURO-CORDEX ensemble of regional climate model simulations and the CORDEX-Adjust dataset were evaluated over the European Alps using multiple gridded observational datasets. Biases, which are here defined as the difference between models and observations, were assessed as a function of the elevation for different climate indices that span average and extreme conditions. Moreover, we assessed the impact of different observational datasets on the evaluation, including E-OBS, APGD, and high-resolution national datasets. Furthermore, we assessed the bi-variate dependency of temperature and precipitation biases, their temporal evolution, and the impact of different bias adjustment methods and bias adjustment reference datasets. Biases in seasonal temperature, seasonal precipitation, and wet-day frequency were found to increase with elevation. Differences in temporal trends between RCMs and observations caused a temporal dependency of biases, which could be removed by detrending both observations and RCMs. The choice of the reference observation datasets used for bias adjustment turned out to be more relevant than the choice of the bias adjustment method itself. Consequently, climate change assessments in mountain regions need to pay particular attention to the choice of observational dataset and, furthermore, to the elevation dependence of biases and the increasing observational uncertainty with elevation in order to provide robust information on future climate.

UFNet: Joint U-Net and Fully Connected Neural Network to Bias Correct Precipitation Predictions from Climate Models

Abstract Precipitation values produced by climate models are biased due to the parameterization of physical processes and limited spatial resolution. Current bias-correction approaches usually focus on correcting lower-order statistics (mean and standard deviation), which make it difficult to capture precipitation extremes. However, accurate modeling of extremes is critical for policymaking to mitigate and adapt to the effects of climate change. We develop a deep learning framework, leveraging information from key dynamical variables impacting precipitation to also match higher-order statistics (skewness and kurtosis) for the entire precipitation distribution, including extremes. The deep learning framework consists of a two-part architecture: a U-Net convolutional network to capture the spatiotemporal distribution of precipitation and a fully connected network to capture the distribution of higher-order statistics. The joint network, termed UFNet, can simultaneously improve the spatial structure of the modeled precipitation and capture the distribution of extreme precipitation values. Using climate model simulation data and observations that are climatologically similar but not strictly paired, the UFNet identifies and corrects the climate model biases, significantly improving the estimation of daily precipitation as measured by a broad range of spatiotemporal statistics. In particular, UFNet significantly improves the underestimation of extreme precipitation values seen with current bias-correction methods. Our approach constitutes a generalized framework for correcting other climate model variables which improves the accuracy of the climate model predictions, while utilizing a simpler and more stable training process.

South Asian Summer Monsoon Precipitation Is Sensitive to Southern Hemisphere Subtropical Radiation Changes

AbstractWe study the sensitivity of South Asian Summer Monsoon (SASM) precipitation to Southern Hemisphere (SH) subtropical Absorbed Solar Radiation (ASR) changes using Community Earth System Model 2 simulations. Reducing positive ASR biases over the SH subtropics impacts SASM, and is sensitive to the ocean basin where changes are imposed. Radiation changes over the SH subtropical Indian Ocean (IO) shifts rainfall over the equatorial IO northward causing 1–2 mm/day drying south of equator, changes over the SH subtropical Pacific increases precipitation over northern continental regions by 1–2 mm/day, and changes over the SH subtropical Atlantic have little effect on SASM precipitation. Radiation changes over the subtropical Pacific impacts the SASM through zonal circulation changes, while changes over the IO modify meridional circulation to bring about changes in precipitation over northern IO. Our findings suggest that reducing SH subtropical radiation biases in climate models may also reduce SASM precipitation biases.

Controls on the Strength and Structure of the Atlantic Meridional Overturning Circulation in Climate Models

AbstractState‐of‐the‐art climate models simulate a large spread in the mean‐state Atlantic meridional overturning circulation (AMOC), with strengths varying between 12 and 25 Sv. Here, we introduce a framework for understanding this spread by assessing the balance between the thermal‐wind expression and surface water mass transformation in the North Atlantic. The intermodel spread in the mean‐state AMOC strength is shown to be related to the overturning scale depth: climate models with a larger scale depth tend to have a stronger AMOC. We present a physically motivated scaling relationship that links intermodel variations in the scale depth to surface buoyancy fluxes and stratification in the North Atlantic, and thus connects North Atlantic surface processes to the interior overturning circulation. Climate models with a larger scale depth tend to have stronger surface buoyancy loss and weaker stratification in the North Atlantic. These results offer a framework for reducing mean‐state AMOC biases in climate models.

Understanding the Cascade: Removing GCM Biases Improves Dynamically Downscaled Climate Projections

AbstractPolarization surrounding bias correction (BC) in creating climate projections arises from its lack of physicality. Here, we perform and analyze 18 dynamical downscaling simulations (with and without BC) to better understand the physical impacts of BC, applied before downscaling, on regional climate output across the western United States. Without BC, downscaled precipitation is systematically and unrealistically wet biased compared to a hierarchy of observationally based datasets over the 1980–2014 period due to cascading mean‐state Global Climate Model (GCM) biases: (a) overly strong lower‐tropospheric lapse rates (5 K/km), (b) overly cold (2 K) tropospheric temperatures, and (c) anomalous mid‐tropospheric cyclonic vorticity advection. With BC, downscaled precipitation (snow) biases are virtually eliminated (halved). Identified GCM biases are common to the broader Coupled Model Intercomparison Project ensemble. Physical effects of BC on the quality of the regionalized projections, pending an evaluation of BC's distortion of the downscaled climate response, may motivate its broader application by dynamical downscalers.

Sources of low-frequency variability in observed Antarctic sea ice

Abstract. Antarctic sea ice has exhibited significant variability over the satellite record, including a period of prolonged and gradual expansion, as well as a period of sudden decline. A number of mechanisms have been proposed to explain this variability, but how each mechanism manifests spatially and temporally remains poorly understood. Here, we use a statistical method called low-frequency component analysis to analyze the spatiotemporal structure of observed Antarctic sea ice concentration variability. The identified patterns reveal distinct modes of low-frequency sea ice variability. The leading mode, which accounts for the large-scale, gradual expansion of sea ice, is associated with the Interdecadal Pacific Oscillation and resembles the observed sea surface temperature trend pattern that climate models have trouble reproducing. The second mode is associated with the central Pacific El Niño–Southern Oscillation (ENSO) and the Southern Annular Mode and accounts for most of the sea ice variability in the Ross Sea. The third mode is associated with the eastern Pacific ENSO and Amundsen Sea Low and accounts for most of the pan-Antarctic sea ice variability and almost all of the sea ice variability in the Weddell Sea. The third mode is also related to periods of abrupt Antarctic sea ice decline that are associated with a weakening of the circumpolar westerlies, which favors surface warming through a shoaling of the ocean mixed layer and decreased northward Ekman heat transport. Broadly, these results suggest that climate model biases in long-term Antarctic sea ice and large-scale sea surface temperature trends are related to each other and that eastern Pacific ENSO variability is a key ingredient for abrupt Antarctic sea ice changes.

Life cycle dynamics of Greenland blocking from a potential vorticity perspective

Abstract. Blocking over Greenland stands out in comparison to blocking in other regions, as it favors accelerated Greenland Ice Sheet melting and has substantial impacts on surface weather in adjacent regions, particularly in Europe and North America. Climate models notoriously underestimate the frequency of blocking over Greenland in historical periods, but the reasons for this are not entirely clear, as we are still lacking a full dynamical understanding of Greenland blocking from formation through maintenance to decay. This study investigates the dynamics of blocking life cycles over Greenland based on ERA5 reanalysis data from 1979–2021. A year-round weather regime definition allows us to identify Greenland blocking as consistent life cycles with an objective onset, maximum, and decay stage. By applying a new quasi-Lagrangian potential vorticity (PV) perspective, following the negative, upper-tropospheric PV anomalies (PVAs−) associated with the block, we examine and quantify the contribution from different physical processes, including dry and moist dynamics, to the evolution of the PVA− amplitude. We find that PVAs− linked to blocking do not form locally over Greenland but propagate into the region along two distinct pathways (termed “upstream” and “retrogression”) during the days before the onset. The development of PVAs− differs more between the pathways than between seasons. Moist processes play a key role in the amplification of PVAs− before the onset and are linked to midlatitude warm conveyor belts. Interestingly, we find moist processes supporting the westward propagation of retrograding PVAs− from Europe, too, previously thought to be a process dominated by dry-barotropic Rossby wave propagation. After onset, moist processes remain the main contribution to PVA− amplification and maintenance. However, moist processes weaken markedly after the maximum stage, and dry processes, i.e., barotropic, nonlinear wave dynamics, dominate the decay of the PVAs− accompanied by a general decrease in blocking area. Our results corroborate the importance of moist processes in the formation and maintenance of Greenland blocking and suggest that a correct representation of moist processes might help reduce forecast errors linked to blocking in numerical weather prediction models and blocking biases in climate models.

What Is the Contribution of Convergence Zones to Global Precipitation? Assessing Observations and Climate Models Biases

AbstractConvergence zones (CZs) are known drivers of precipitation regimes from regional to planetary scales. However, there is a scarcity of accounts of the contribution of CZs to the global precipitation. In this study, we build upon a recently developed Lagrangian diagnostic to attribute precipitation to CZ events in observations and simulations submitted to the Coupled Model Intercomparison Project 6 (CMIP6). Observed CZs are identified using ERA5 reanalysis wind and attributed precipitation from observational products based on satellite estimates and rain gauges. We estimate that approximately 54% (51%–59%, depending on the precipitation product) of global precipitation falls over CZs; in some regions, such as the Intertropical Convergence Zone (ITCZ) and subtropical monsoon regions, this proportion is greater than 60%. All CMIP6 simulations analyzed here attribute about 10% more precipitation to CZ events than what the observations suggest. To investigate this overestimation, we decompose the precipitation error in terms of frequency and intensity of CZ precipitation and find that all models present a substantial positive bias in the frequency of CZ precipitation, suggesting that climate models trigger precipitation too easily in regions of airmass confluence; such positive frequency biases in CZ precipitation help explaining well‐known biases in climate models, such as the double‐ITCZ in the Pacific. We also find that models with better mass conservation present an apportionment of CZ precipitation closest to the observational estimates, demonstrating the relevance of mass conservation in advection schemes.

The Relationship between Convectively Coupled Waves and the East Pacific ITCZ

Abstract Longstanding climate model biases in tropical precipitation exist over the east Pacific (EP) Ocean, especially during boreal winter and spring when models have excessive Southern Hemisphere (SH) precipitation near the intertropical convergence zone (ITCZ). In this study, we document the impact of convectively coupled waves (CCWs) on EP precipitation and the ITCZ using observations and reanalyses. We focus on the months when SH precipitation peaks in observations: February–April (FMA). CCWs explain 93% of total precipitation variance in the SH, nearly double the percent (48%) of the NH during FMA. However, we note that these percentages are inflated as they inevitably include the background variance. We further investigate the three leading high-frequency wave bands: mixed Rossby–gravity waves and tropical depression–type disturbances (MRG–TD type), Kelvin waves, and n = 0 eastward inertia–gravity waves (IG0). Compared to their warm pool counterparts, these three CCWs have a more zonally elongated and meridionally narrower precipitation structure with circulations that resemble past observational studies and/or shallow water theory. We quantify the contribution of all CCWs to four different daily ITCZ “states”: Northern Hemisphere (NH) (nITCZ), SH (sITCZ), double (dITCZ), and equatorial (eITCZ) using a new precipitation-based ITCZ-state algorithm. We find that the percent of total precipitation variance explained by each of the CCWs is heightened for sITCZs and eITCZs and diminished for nITCZs. Last, we find that nITCZs are most prevalent weeks after strong CCW activity happens in the NH, whereas CCWs and sITCZs peak simultaneously in the SH. Significance Statement Convectively coupled atmospheric waves (CCWs) are a critical feature of tropical weather and are an important source of precipitation near the region of highest precipitation on Earth called the intertropical convergence zone (ITCZ). Given three decades of climate model biases in CCWs and ITCZ precipitation over the east Pacific (EP) Ocean during spring, few studies have examined the relationship between CCWs and the springtime EP ITCZ. We explored the CCWs and EP ITCZ relationship through calculations of the percent of precipitation that comes from CCWs. A significant portion of the tropical precipitation is associated with CCWs during spring. CCWs are even more impactful when the ITCZ is in the SH or on the equator, which are both problematic in climate models.

Persistent climate model biases in the Atlantic Ocean's freshwater transport

Abstract. The Atlantic Meridional Overturning Circulation (AMOC) is considered to be one of the most dangerous climate tipping elements. The salt–advection feedback plays an important role in AMOC tipping behaviour, and its strength is strongly connected to the freshwater transport carried by the AMOC at 34° S, below indicated by FovS. Available observations have indicated that FovS has a negative sign for the present-day AMOC. However, most climate models of the Coupled Model Intercomparison Project (CMIP, phase 3 and phase 5) have an incorrect FovS sign. Here, we analyse a high-resolution and a low-resolution version of the Community Earth System Model (CESM) to identify the origin of these FovS biases. Both CESM versions are initialised from an observed ocean state, and FovS biases quickly develop under fixed pre-industrial forcing conditions. The most important model bias is a too fresh Atlantic Surface Water, which arises from deficiencies in the surface freshwater flux over the Indian Ocean. The second largest bias is a too saline North Atlantic Deep Water and arises through deficiencies in the freshwater flux over the Atlantic Subpolar Gyre region. Climate change scenarios branched from the pre-industrial simulations have an incorrect FovS upon initialisation. Most CMIP phase 6 models have similar biases to those in the CESM. Due to the biases, the value of FovS is not in agreement with available observations, and the strength of the salt advection feedback is underestimated. Values of FovS are projected to decrease under climate change, and their response is also dependent on the various model biases. To better project future AMOC behaviour, an urgent effort is needed to reduce biases in the atmospheric components of current climate models.

Glacial inception through rapid ice area increase driven by albedo and vegetation feedbacks

Abstract. We present transient simulations of the last glacial inception using the Earth system model CLIMBER-X with dynamic vegetation, interactive ice sheets, and visco-elastic solid Earth responses. The simulations are initialized at the middle of the Eemian interglacial (125 kiloyears before present, ka) and run until 100 ka, driven by prescribed changes in Earth's orbital parameters and greenhouse gas concentrations from ice core data. CLIMBER-X simulates a rapid increase in Northern Hemisphere ice sheet area through MIS5d, with ice sheets expanding over northern North America and Scandinavia, in broad agreement with proxy reconstructions. While most of the increase in ice sheet area occurs over a relatively short period between 119 and 117 ka, the larger part of the increase in ice volume occurs afterwards with an almost constant ice sheet extent. We show that the vegetation feedback plays a fundamental role in controlling the ice sheet expansion during the last glacial inception. In particular, with prescribed present-day vegetation the model simulates a global sea level drop of only ∼ 20 m, compared with the ∼ 35 m decrease in sea level with dynamic vegetation response. The ice sheet and carbon cycle feedbacks play only a minor role during the ice sheet expansion phase prior to ∼ 115 ka but are important in limiting the deglaciation during the following phase characterized by increasing summer insolation. The model results are sensitive to climate model biases and to the parameterization of snow albedo, while they show only a weak dependence on changes in the ice sheet model resolution and the acceleration factor used to speed up the climate component. Overall, our simulations confirm and refine previous results showing that climate–vegetation–cryosphere feedbacks play a fundamental role in the transition from interglacial to glacial states characterizing Quaternary glacial cycles.

Drivers of coupled climate model biases in representing Labrador Sea convection

This study investigates the representation of ocean convection in the Labrador Sea in seven Earth System Models (ESMs) from the Coupled Model Intercomparison Project Phase 5 and 6 datasets. The relative role of the oceanic and atmospheric biases in the subpolar North Atlantic gyre are explored using regional ocean simulations where the atmospheric forcing or the ocean initial and boundary conditions are replaced by reanalysis data in the absence of interactive air-sea coupling. Commonalities and differences among model behaviors are discussed with the objective of finding a pathway forward to improve the representation of the ocean mean state and variability in a region of fundamental importance for climate variability and change. Results highlight that an improved representation of ocean stratification in the North Atlantic subpolar gyre is urgently needed to constrain future climate change projections. While improving the ocean model resolution in the North Atlantic alone may contribute a better representation of both boundary currents and propagation of heat and freshwater anomalies into the Labrador Sea, it may not be sufficient. Addressing the atmospheric heat flux bias with better resolution in the atmosphere and land topography may allow for deep convection to occur in the Labrador Sea in some of the models that miss it entirely, but the greatest priority remains improving the representation of ocean stratification.

Comparative Analysis of Climate Change Impacts on Climatic Variables and Reference Evapotranspiration in Tunisian Semi-Arid Region

Systematic biases in general circulation models (GCM) and regional climate models (RCM) impede their direct use in climate change impact research. Hence, the bias correction of GCM-RCMs outputs is a primary step in such studies. This study compares the potential of two bias correction methods (the method from the third phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3) and Detrended Quantile Matching (DQM)) applied to the raw outputs of daily data of minimum and maximum air temperatures and precipitation, in the Cap-Bon region, from eight GCM-RCM combinations. The outputs of GCM/RCM combinations were acquired from the European branch of the coordinated regional climate downscaling experiment (EURO-CORDEX) dataset for historical periods and under two representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Furthermore, the best combination of bias correction/GCM-RCM was used to assess the impact of climate change on reference evapotranspiration (ET0). Numerous statistical indicators were considered to evaluate the performance of the bias correction/historical GCM-RCMs compared to the observed data. Trends of the Hargreaves–Samani_ET0 model during the historical and projected periods were determined using the TFPMK method. A comparison of the bias correction methods revealed that, for all the studied model combinations, ISIMIP3 performs better in reducing biases in monthly precipitation. However, for Tmax and Tmin, the biases are greatly removed when the DQM bias correction method is applied. In general, better results were obtained when the HadCCLM model was used. Before applying bias correction, the set of used GCM-RCMs projected reductions in precipitation for most of the months compared to the reference period (1982–2006). However, Tmin and Tmax are expected to increase in all months and for the three studied periods. Hargreaves–Samani ET0 values obtained from the best combination (DQM/ HadCCLM) show that RCP8.5 (2075–2098) will exhibit the highest annual ET0 increase compared to the RCP4.5 scenario and the other periods, with a change rate equal to 11.85% compared to the historical period. Regarding spring and summer seasons, the change rates of ET0 are expected to reach 10.44 and 18.07%, respectively, under RCP8.5 (2075–2098). This study shows that the model can be used to determine long-term trends in ET0 patterns for diverse purposes, such as water resources planning, agricultural crop management and irrigation scheduling in the Cap-Bon region.

Assessment of thermocline depth bias in the Seychelles-Chagos Thermocline Ridge of the Southwestern Indian Ocean simulated by the CMIP6 models

The Seychelles-Chagos Thermocline Ridge (SCTR, 5°S-10°S, 50°E-80°E) is a unique open-ocean upwelling region in the southwestern Indian Ocean. Due to the negative wind stress curl between the equatorial westerlies and southeasterly trade winds, SCTR is known as a strong upwelling region with high biological productivity, providing a primary fishing zone for the surrounding countries. Given its importance in shaping the variability of the Indian Ocean climate by understanding the sea-air interaction and its dynamics, the simulation of SCTR is evaluated using outputs from the Coupled Model Intercomparison Project Phase Sixth (CMIP6). Compared to observations, 23 out of 27 CMIP6 models tend to simulate considerably deeper SCTR thermocline depth (defined as the 20°C isotherm depth (D20))– a common bias in climate models. The deep bias is related to the easterly wind bias in the equatorial to southern Indian Ocean, which is prominent in boreal summer and fall. This easterly wind bias produces a weak annual mean Ekman pumping, especially in the boreal fall. Throughout the year, the observed Ekman pumping is positive and is driven by two components: the curl term, is associated with the wind stress curl, leads to upwelling during boreal summer to fall; the beta term, is linked to planetary beta and zonal wind stress, contributes to downwelling during boreal spring to fall. However, the easterly wind bias in the CMIP6 increases both the positive curl and negative beta terms. The beta term bias offsets the curl term bias and reduces the upwelling velocity. Furthermore, the easterly wind bias is likely caused by the reduced east-west sea surface temperature (SST) difference associated with a pronounced warm bias in the western equatorial Indian Ocean, accompanied by the east-west mean sea level pressure gradient over the Indian Ocean. Furthermore, this study finds local wind-induced Ekman pumping to be a more dominant factor in thermocline depth bias than Rossby waves, despite CMIP6 models replicating Rossby wave propagation. This study highlights the importance of the beta term in the Ekman pumping simulation. Thus, reducing the boreal summer-to-fall easterly wind bias over the Indian Ocean region may improve the thermocline depth simulation over the SCTR region.

Contrasting controls on convection at latitude zones near and away from the equator for the Indian summer monsoon

Understanding controls on convection on various timescales is crucial for improved monsoon rainfall forecasting. Although the literature points to vertically homogeneous vorticity signatures preceding rainfall during the Indian summer monsoon, we show using reanalysis data that, for rainfall associated with northward propagating intraseasonal oscillations (ISOs), different controls are present at different latitude zones. For the latitude zone close to the equator (5∘N–14∘N) and including the southern Indian region, a conventional dynamical control on rainfall exists with barotropic vorticity leading ISO rainfall by about five days. In contrast, for the latitude zone away from the equator (15∘N–24∘N; covering the central Indian region), thermodynamic fields control ISO rainfall, with barotropic vorticity following rainfall by two days on average. Over central India, the pre-moistening of the boundary layer (BL) yields maximum moist static energy (MSE) about four days prior to ISO rainfall. Analyzing the statistics of individual events verifies these observations. Similar thermodynamic control is also present for the large-scale extreme rainfall events (LEREs) occurring over central India. These high rainfall events are preceded by positive MSE anomalies arising from the moisture preconditioning of the BL. The resulting convection then leads to a maximum in barotropic vorticity 12 h after the rainfall maximum. Characterizing these influences on convection occurring over various timescales can help identify the dominant mechanisms that govern monsoon convection. This can help reduce climate model biases in simulating Indian monsoon rainfall.

A review of ENSO teleconnections at present and under future global warming

AbstractThe El Niño‐Southern Oscillation (ENSO) is a major component of the Earth's climate that largely influences global climate variability through long‐distance teleconnections. Rossby wave trains emerging from the tropical convection and their propagation into extratropical regions are the key mechanism for tropical and extratropical teleconnections. Despite significant progress in the understanding of ENSO teleconnections over the recent past decades, several important issues have remained to be addressed. The global atmospheric teleconnections of ENSO vary substantially with the seasonal cycle, on the decadal timescale, and under the influence of global warming. It is essential to separate the internal decadal variability of ENSO teleconnections from changes caused by the external forcing of global warming. However, the post‐satellite observations are not long enough to compose a large number of ENSO events to distinguish the decadal variability of ENSO teleconnections from changes related to increasing greenhouse concentrations. The current climate models also suffer from common biases, such that they are unable to properly reproduce both the tropical mean state and some features of ENSO. Nevertheless, observational records can be extended back in time via reconstruction methods. Efforts have also already been made to remove some main common biases of climate models and to improve the representation of ENSO characteristics. The reliable reconstructed data along with a large number of ensemble members of the improved climate model simulations can be applied to advance our understanding of ENSO global teleconnections and their responses to internal decadal variability and externally forced global warming.This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change Climate Models and Modeling > Earth System Models Assessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change

Origins of Southern Ocean warm sea surface temperature bias in CMIP6 models

The warm sea surface temperature (SST) bias in the Southern Ocean (SO) has persisted in several generations of Coupled Model Inter-comparison Project (CMIP) models, yet the origins of such a bias remain controversial. Using the latest CMIP6 models, here we find that the warm SST bias in the SO features a zonally oriented non-uniform pattern mainly located between the northern and southern fronts of the Antarctic Circumpolar Current. This common bias is not likely to be caused by the biases in the surface heat flux or the strength of the Atlantic meridional overturning circulation (AMOC) — the two previously suggested sources of the SO bias based on CMIP5 models. Instead, it is linked to the robust common warm bias in the Northern Atlantic deep ocean through the AMOC transport as an adiabatic process. Our findings indicate that remote oceanic biases that are dynamically connected to the SO should be taken into account to reduce the SO SST bias in climate models.

Implications of Warm Pool Bias in CMIP6 Models on the Northern Hemisphere Wintertime Subtropical Jet and Precipitation

AbstractAlthough the multi‐model average compares well with observations, individually most of the latest climate models do not simulate a realistic size of the Indo‐Pacific Warm Pool in the present‐day climate. This study explores the implications of this warm pool size bias in climate models in Northern Hemisphere winter. The warm pool size bias in phase 6 of the Coupled Model Intercomparison Project models is related to the subtropical jet and precipitation distribution, both in the present‐day climate and in response to climate change, through extratropical Rossby wave trains and tropical circulation pathways. Based on these relationships, emergent constraints are developed to observationally constrain the future subtropical jet response over Asia and the Atlantic Ocean and precipitation response over North and Central America, which can help to reduce uncertainty in future projections of these features. Thus, accurate model simulation of the warm pool in the present‐day climate is important for future projections of the subtropical jet and precipitation.