Explore the vast range of titles available.

A too cold and too strong Antarctic stratospheric polar vortex (ASPV) in spring (the so‐called cold‐pole bias) is a common model problem. This study investigates the impact of the cold‐pole bias on Southern Hemisphere springtime stratosphere‐troposphere coupling and how this impact is affected by interactive ozone using a pair of Goddard Earth Observing System (GEOS) simulations with and without interactive chemistry. The cold‐pole bias in the GEOS simulations delays the poleward and downward progression of the ASPV and stratosphere‐troposphere coupling in spring by 1–2 months, causing severe underestimation of stratosphere‐troposphere coupling in October–November. Consequently, the simulations poorly capture or completely miss the observed springtime tropospheric predictability from ASPV conditions in late winter/early spring. Compared to the prescribed ozone simulation, interactive ozone exacerbates the cold‐pole bias by overpredicting Antarctic ozone loss, leading to degradation of springtime stratosphere‐troposphere coupling and loss of tropospheric predictability.

Most global circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulate a delayed onset of the tropical Asian summer monsoon of 3–6 pentads when compared with the observations. However, a clear explanation of this model bias has yet to be developed. This study indicates that 23 of the 31 of CMIP6 models generate both the Tibetan Plateau (TP) cold bias and the delayed monsoon onset across tropical Asia. The aloft TP cold air temperature associated with these models tends to reduce the land–sea thermal contrast and monsoon circulation, and hence it generates a delayed onset for the tropical summer monsoon. Two sensitivity experiments based on a coupled ocean–atmosphere–land GCM, together with additional data analysis, further confirm the underlying connection between monsoon onset and temperature anomaly over the TP. Therefore, it is of great importance that we attempt to reduce the model bias associated with the simulation of monsoon onset by improving the physical process parameterization scheme related to the TP temperatures.

The Tibetan Plateau (TP) directly heats the middle tropospheric atmosphere, and accurate simulation of its surface temperature is of great concern for improving climatic prediction and projection capabilities, but climate models always exhibit a cold bias. Based on the Coupled Model Intercomparison Project Phase 6 (CMIP6) models and in-situ observations during 1981–2014, this study elucidates the impact of the snow overestimation on the temperature simulation over the TP in CMIP6 from the perspective of local radiation processes and atmospheric circulation. On the one hand, more snow in the CMIP6 models not only directly cools the surface more, but also makes the surface receive less shortwave radiation due to the higher surface albedo, and thus has lower ground surface temperature (GST), and the more snow/albedo-low temperature process is particularly evident in the westerly region due to more uncertainty at high elevations. This process contributes 87% to the annual GST cold bias. Lower GST corresponds to less latent heat transfer and thereby lower surface air temperature (SAT). In addition, the more snow in the CMIP6 models leads to the weaker the South Asian summer monsoon and the westerlies, and brings less warm and moist air (less integrated water vapor flux), as well as less clear-sky downward longwave radiation (less water vapor amount and lower tropospheric air temperature) to the TP (contributing 58% to the annual GST cold bias). These processes will result in less both precipitation and surface latent heat loss, which offsets a 35% annual GST cold bias. Besides, the physical mechanism of snow on GST and SAT differs with season over the westerly and monsoon regions of the TP. The research highlights the importance of topography and snow parameterization schemes for optimizing CMIP6 models.

Most state-of-the-art climate models substantially underestimate the near-surface air temperature (SAT) over the Tibetan Plateau (TP), especially for the cold season. While previous studies have attributed this cold bias to local factors such as the elevation difference, coarse resolution, and excessive snow cover, this investigation addresses the potential contributions of the systematic bias of tropical sea surface temperature (SST) to the TP cold bias. Experiments with the NCAR Community Atmosphere Model demonstrate that tropical SST bias results in an apparent cold bias over the TP, especially in boreal winter, and explains about 40% of the deviation in multi-model mean SAT over the TP relative to station observations. Forced by the tropical SST bias, heat flux exhibits an anomalous divergence over the plateau, causing a cooling center in the mid- and lower-troposphere over the TP. This atmospheric cooling in turn leads to a reduction of the downward longwave radiative fluxes reaching the surface, less energy supply, and thus a cold bias over the TP.

The temperature biases of 28 CMIP5 AGCMs are evaluated over the Tibetan Plateau（TP） for the period 1979–2005. The results demonstrate that the majority of CMIP5 models underestimate annual and seasonal mean surface 2-m air temperatures(Tas) over the TP. In addition, the ensemble of the 28 AGCMs and half of the individual models underestimate annual mean skin temperatures（Ts） over the TP. The cold biases are larger in Tas than in Ts, and are larger over the western TP. By decomposing the Ts bias using the surface energy budget equation, we investigate the contributions to the cold surface temperature bias on the TP from various factors, including the surface albedo-induced bias, surface cloud radiative forcing, clear-sky shortwave radiation, clear-sky downward longwave radiation, surface sensible heat flux, latent heat flux,and heat storage. The results show a suite of physically interlinked processes contributing to the cold surface temperature bias.Strong negative surface albedo-induced bias associated with excessive snow cover and the surface heat fluxes are highly anticorrelated, and the cancelling out of these two terms leads to a relatively weak contribution to the cold bias. Smaller surface turbulent fluxes lead to colder lower-tropospheric temperature and lower water vapor content, which in turn cause negative clear-sky downward longwave radiation and cold bias. The results suggest that improvements in the parameterization of the area of snow cover, as well as the boundary layer, and hence surface turbulent fluxes, may help to reduce the cold bias over the TP in the models.

Most climate models show systematic cold biases during snow‐covered period over the Tibetan Plateau (TP), which is associated with snow and surface albedo overestimations. In this work, a snow cover fraction (SCF) scheme and a recently developed albedo scheme for shallow snow are implemented in the Noah‐MP land surface model coupled with the Weather Research and Forecasting (WRF) model. The SCF scheme introduces subgrid orographic variability to reduce the SCF, and the shallow‐snow albedo scheme parameterizes the fresh‐snow albedo as a function of the snow depth (SD). Evaluations by remote sensing data show that both schemes can effectively alleviate the overestimation of the simulated surface albedo, SCF, snow water equivalent, and SD over the TP. The reductions in the modeled SCF and snow albedo directly lead to lower surface albedo values and thus more surface solar radiation absorption, which accelerates snow melting and causes surface warming effects. Further comparisons with Moderate Resolution Imaging Spectroradiometer data and station observations show that both schemes can significantly reduce the cold biases in the surface skin temperature (from −4.39°C to 0.19°C for the TP mean) and 2‐m air temperature (from −4.48°C to −1.05°C for the station mean) during the cold season (October to May of next year) in the study region. This work provides guidance for advancing the snow‐related physics in climate models and the improved WRF model could facilitate weather forecasting and climate prediction for the plateau region. The cold bias of the Weather Research and Forecasting model over the Tibetan Plateau is significantly reduced by implementing a snow coverage‐topography relationship and a fresh snow albedo scheme. With the introduction of the subgrid orographic variability in parameterizing the snow cover fraction and a shallow‐snow albedo scheme in parameterizing the fresh‐snow albedo, less snow and a lower surface albedo are simulated. Thus, more solar radiation is absorbed by the land surface, leading to a surface warming effect. As a result, the cold biases in the surface skin temperature and 2‐m air temperature are significantly reduced when evaluated by Moderate Resolution Imaging Spectroradiometer data and station observations. A snow coverage‐topography relationship and a fresh snow albedo scheme are implemented in Weather Research and Forecasting and applied to the Tibetan Plateau (TP) The overestimation in the simulated snow cover, snow depth (SD) and albedo over the TP is significantly alleviated The modeled cold biases over the TP are significantly reduced due to the enhanced surface net solar radiation induced by albedo reduction

We analyze the publicly released outputs of the simulations performed by climate models (CMs) in preindustrial (PI) and Special Report on Emissions Scenarios A1B (SRESA1B) conditions. In the PI simulations, most CMs feature biases of the order of 1 W m−2 for the net global and the net atmospheric, oceanic, and land energy balances. This does not result from transient effects but depends on the imperfect closure of the energy cycle in the fluid components and on inconsistencies over land. Thus, the planetary emission temperature is underestimated, which may explain the CMs' cold bias. In the PI scenario, CMs agree on the meridional atmospheric enthalpy transport's peak location (around 40°N/S), while discrepancies of ∼20% exist on the intensity. Disagreements on the oceanic transport peaks' location and intensity amount to ∼10° and ∼50%, respectively. In the SRESA1B runs, the atmospheric transport's peak shifts poleward, and its intensity increases up to ∼10% in both hemispheres. In most CMs, the Northern Hemispheric oceanic transport decreases, and the peaks shift equatorward in both hemispheres. The Bjerknes compensation mechanism is active both on climatological and interannual time scales. The total meridional transport peaks around 35° in both hemispheres and scenarios, whereas disagreements on the intensity reach ∼20%. With increased CO2 concentration, the total transport increases up to ∼10%, thus contributing to polar amplification of global warming. Advances are needed for achieving a self‐consistent representation of climate as a nonequilibrium thermodynamical system. This is crucial for improving the CMs' skill in representing past and future climate changes.

The ability of an ensemble of five regional climate models (hereafter RCMs) under Coordinated Regional Climate Downscaling Experiments-South Asia (hereafter, CORDEX-SA) in simulating the key features of present day near surface mean air temperature (Tmean) climatology (1970–2005) over the Himalayan region is studied. The purpose of this paper is to understand the consistency in the performance of models across the ensemble, space and seasons. For this a number of statistical measures like trend, correlation, variance, probability distribution function etc. are applied to evaluate the performance of models against observation and simultaneously the underlying uncertainties between them for four different seasons. The most evident finding from the study is the presence of a large cold bias (−6 to −8 °C) which is systematically seen across all the models and across space and time over the Himalayan region. However, these RCMs with its fine resolution perform extremely well in capturing the spatial distribution of the temperature features as indicated by a consistently high spatial correlation (greater than 0.9) with the observation in all seasons. In spite of underestimation in simulated temperature and general intensification of cold bias with increasing elevation the models show a greater rate of warming than the observation throughout entire altitudinal stretch of study region. During winter, the simulated rate of warming gets even higher at high altitudes. Moreover, a seasonal response of model performance and its spatial variability to elevation is found.

The atmosphere‐ocean is a highly coupled system with significant diurnal and hourly variations. However, current coupled models usually lack sub‐diurnal scale processes at the air‐sea interface due to the finite vertical resolution for ocean discretization. Previous modeling studies showed that sub‐diurnal scale air‐sea interaction processes are important for ocean mixing. Here, by designing an integrated sub‐diurnal parameterization (ISDP) scheme which combines different temperature profiling functions, we stress sub‐diurnal air‐sea interactions to better represent the local ocean mixing. This scheme has been implemented into two coupled models which contributed to the Climate Model Intercomparison Project (CMIP), referenced by the Intergovernmental Panel on Climate Change—Community Earth System Model and Coupled Model version 2. The results show that the ISDP scheme improves model simulations with better climatology and more realistic spectra, especially in the tropics and North Pacific Ocean. With the scheme, the tropical cold tongue bias is significantly relaxed by reducing the overestimation of ocean upper mixing, and the cold bias of North Pacific Ocean is reduced due to the improvement on currents and net heat fluxes. Our scheme may help better the simulation and prediction skills of coupled models when their horizontal resolution becomes fine but vertical resolution remains relatively coarse as it describes high‐frequency air‐sea interactions more realistically. Plain Language Summary The atmosphere and ocean interact with each other. In these interactions, changes may occur over different time periods. Some changes take years or months, while others happen in days or hours. The atmosphere changes quickly throughout the day, such as air temperature, wind, and rain. These quick changes in the atmosphere can affect the ocean rapidly. Similarly, there are quick changes in the ocean. These quick changes also affect the atmosphere in return. Understanding these quick changes is important. However, it is difficult to perfectly capture the quick changes in the climate models because they do not always represent the ocean behavior accurately. In this study, we developed a new scheme (named the sub‐diurnal scale parameterization, ISDP) to better represent these quick changes. We added this new scheme into two widely‐used climate models. These models are important tools for studying climate change. Our new scheme represents better the ocean mixing and interaction based on the local weather conditions. When we use ISDP, the climate models get better at simulating climate. They're more accurate in showing ocean temperatures in the tropics. These models make the tropical oceans seem colder than they really are, but our scheme, to some extent, fixes this problem. The results also show the more accurate ocean temperatures in the North Pacific Ocean. Our new scheme has a great potential to make climate models more accurate. Key Points An integrated sub‐diurnal scale parameterization (ISDP) scheme has a more appropriate representation for local ocean mixing The ISDP scheme has been implemented into two models which contributed to the Climate Model Intercomparison Project, referenced by IPCC: Community Earth System Model and Coupled Model version 2 The model tropical cold tongue bias with ISDP is relaxed by reducing the overestimation of ocean upper mixing

Common problems in state-of-the-art climate models are a cold sea surface temperature (SST) bias in the equatorial Pacific and the underestimation of the two most important atmospheric feedbacks operating in the El Niño/Southern Oscillation (ENSO): the positive, i.e. amplifying wind-SST feedback and the negative, i.e. damping heat flux-SST feedback. To a large extent, the underestimation of those feedbacks can be explained by the cold equatorial SST bias, which shifts the rising branch of the Pacific Walker Circulation (PWC) too far to the west by up to 30°, resulting in an erroneous convective response during ENSO events. Based on simulations from the Kiel Climate Model (KCM) and the 5th phase of Coupled Model Intercomparison Project (CMIP5), we investigate how well ENSO dynamics are simulated in case of underestimated ENSO atmospheric feedbacks (EAF), with a special focus on ocean–atmosphere coupling over the equatorial Pacific. While models featuring realistic atmospheric feedbacks simulate ENSO dynamics close to observations, models with underestimated EAF exhibit fundamental biases in ENSO dynamics. In models with too weak feedbacks, ENSO is not predominantly wind-driven as observed; instead ENSO is driven significantly by a positive shortwave radiation feedback. Thus, although these models simulate ENSO, which in terms of simple indices is consistent with observations, it originates from very different dynamics. A too weak oceanic forcing on the SST via the positive thermocline, the Ekman and the zonal advection feedback is compensated by weaker atmospheric heat flux damping. The latter is mainly caused by a biased shortwave-SST feedback that erroneously is positive in most climate models. In the most biased models, the shortwave-SST feedback contributes to the SST anomaly growth to a similar degree as the ocean circulation. Our results suggest that a broad continuum of ENSO dynamics can exist in climate models and explain why climate models with less than a half of the observed EAF strength can still depict realistic ENSO amplitude.