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- Author or Editor: Tobias Bayr x
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Abstract
This article addresses the causes of the large-scale tropical sea level pressure (SLP) changes during climate change. The analysis presented here is based on model simulations, observed trends, and the seasonal cycle. In all three cases the regional changes of tropospheric temperature (T tropos) and SLP are strongly related to each other [considerably more strongly than (sea) surface temperature and SLP]. This relationship basically follows the Bjerknes circulation theorem, with relatively low regional SLP where there is relatively high T tropos and vice versa. A simple physical model suggests a tropical SLP response to horizontally inhomogeneous warming in the tropical T tropos, with a sensitivity coefficient of about −1.7 hPa K−1. This relationship explains a large fraction of observed and predicted changes in the tropical SLP.
It is shown that in climate change model simulations the tropospheric land–sea warming contrast is the most significant structure in the regional T tropos changes relative to the tropical mean changes. Since the land–sea warming contrast exists in the absence of any atmospheric circulation changes, it can be argued that the large-scale response of tropical SLP changes is to first order a response to the tropical land–sea warming contrast. Furthermore, as the land–sea warming contrast is mostly moisture dependent, the models predict a stronger warming and decreasing SLP in the drier regions from South America to Africa and a weaker warming and increasing SLP over the wetter Indo-Pacific warm pool region. This suggests an increase in the potential for deep convection conditions over the Atlantic sector and a decrease over the Indo-Pacific warm pool region in the future.
Abstract
This article addresses the causes of the large-scale tropical sea level pressure (SLP) changes during climate change. The analysis presented here is based on model simulations, observed trends, and the seasonal cycle. In all three cases the regional changes of tropospheric temperature (T tropos) and SLP are strongly related to each other [considerably more strongly than (sea) surface temperature and SLP]. This relationship basically follows the Bjerknes circulation theorem, with relatively low regional SLP where there is relatively high T tropos and vice versa. A simple physical model suggests a tropical SLP response to horizontally inhomogeneous warming in the tropical T tropos, with a sensitivity coefficient of about −1.7 hPa K−1. This relationship explains a large fraction of observed and predicted changes in the tropical SLP.
It is shown that in climate change model simulations the tropospheric land–sea warming contrast is the most significant structure in the regional T tropos changes relative to the tropical mean changes. Since the land–sea warming contrast exists in the absence of any atmospheric circulation changes, it can be argued that the large-scale response of tropical SLP changes is to first order a response to the tropical land–sea warming contrast. Furthermore, as the land–sea warming contrast is mostly moisture dependent, the models predict a stronger warming and decreasing SLP in the drier regions from South America to Africa and a weaker warming and increasing SLP over the wetter Indo-Pacific warm pool region. This suggests an increase in the potential for deep convection conditions over the Atlantic sector and a decrease over the Indo-Pacific warm pool region in the future.
Abstract
Changes in the background climate are known to affect El Niño–Southern Oscillation (ENSO) by altering feedbacks that control ENSO’s characteristics. Here, the sensitivity of ENSO variability to the background climate is investigated by utilizing two Community Earth System Model, version 1 (CESM1), simulations in which the solar constant is altered by ±25 W m−2. The resulting stable warm and cold climate mean state simulations differ in terms of ENSO amplitude, frequency, diversity, asymmetry, and seasonality. In the warm run, ENSO reveals a larger amplitude and occurs at higher frequencies relative to the cold and control runs as well as observations. The warm run also features more eastern Pacific El Niños, an increased asymmetry, and a stronger seasonal phase locking. These changes are linked to changes in the mean state via the amplifying and damping feedbacks. In the warm run, a shallower mean thermocline results in a stronger subsurface–surface coupling, whereas the cold run reveals reduced ENSO variability due to a reduced Bjerknes feedback in accordance with a deeper mean thermocline and enhanced surface wind stress. A strong zonal advective and upwelling feedback further contribute to the large ENSO amplitude in the run with a warmer mean state. In the cold run, ENSO events are partly forced by anomalous shortwave radiation. However, in light of the large temperature contrast between the simulations of up to 6 K in the tropical Pacific, the relatively small changes in ENSO variability highlight the robustness of ENSO dynamics under vastly different climate mean states.
Abstract
Changes in the background climate are known to affect El Niño–Southern Oscillation (ENSO) by altering feedbacks that control ENSO’s characteristics. Here, the sensitivity of ENSO variability to the background climate is investigated by utilizing two Community Earth System Model, version 1 (CESM1), simulations in which the solar constant is altered by ±25 W m−2. The resulting stable warm and cold climate mean state simulations differ in terms of ENSO amplitude, frequency, diversity, asymmetry, and seasonality. In the warm run, ENSO reveals a larger amplitude and occurs at higher frequencies relative to the cold and control runs as well as observations. The warm run also features more eastern Pacific El Niños, an increased asymmetry, and a stronger seasonal phase locking. These changes are linked to changes in the mean state via the amplifying and damping feedbacks. In the warm run, a shallower mean thermocline results in a stronger subsurface–surface coupling, whereas the cold run reveals reduced ENSO variability due to a reduced Bjerknes feedback in accordance with a deeper mean thermocline and enhanced surface wind stress. A strong zonal advective and upwelling feedback further contribute to the large ENSO amplitude in the run with a warmer mean state. In the cold run, ENSO events are partly forced by anomalous shortwave radiation. However, in light of the large temperature contrast between the simulations of up to 6 K in the tropical Pacific, the relatively small changes in ENSO variability highlight the robustness of ENSO dynamics under vastly different climate mean states.
Abstract
We investigate the origin of the equatorial Pacific cold sea surface temperature (SST) bias and its link to wind biases, local and remote, in the Kiel Climate Model (KCM). The cold bias is common in climate models participating in phases 5 and 6 of the Coupled Model Intercomparison Project. In the coupled experiments with the KCM, the interannually varying NCEP/CFSR wind stress is prescribed over four spatial domains: globally, over the equatorial Pacific (EP), the northern Pacific (NP), and the southern Pacific (SP). The corresponding EP SST bias is reduced by 100%, 52%, 12%, and 23%, respectively. Thus, the EP SST bias is mainly attributed to the local wind bias, with small but not negligible contributions from the extratropical regions. Erroneous ocean circulation driven by overly strong winds causes the cold SST bias, while the surface heat flux counteracts it. Extratropical Pacific SST biases contribute to the EP cold bias via the oceanic subtropical gyres, which is further enhanced by dynamical coupling in the equatorial region. The origin of the wind biases is examined by forcing the atmospheric component of the KCM in a stand-alone mode with observed SSTs and simulated SSTs from the coupled experiments. Wind biases over the EP, NP, and SP regions originate in the atmosphere model. The cold EP SST bias substantially enhances the wind biases over all three regions, while the NP and SP SST biases support local amplification of the wind bias. This study suggests that improving surface wind stress, at and off the equator, is a key to improve mean-state equatorial Pacific SST in climate models.
Abstract
We investigate the origin of the equatorial Pacific cold sea surface temperature (SST) bias and its link to wind biases, local and remote, in the Kiel Climate Model (KCM). The cold bias is common in climate models participating in phases 5 and 6 of the Coupled Model Intercomparison Project. In the coupled experiments with the KCM, the interannually varying NCEP/CFSR wind stress is prescribed over four spatial domains: globally, over the equatorial Pacific (EP), the northern Pacific (NP), and the southern Pacific (SP). The corresponding EP SST bias is reduced by 100%, 52%, 12%, and 23%, respectively. Thus, the EP SST bias is mainly attributed to the local wind bias, with small but not negligible contributions from the extratropical regions. Erroneous ocean circulation driven by overly strong winds causes the cold SST bias, while the surface heat flux counteracts it. Extratropical Pacific SST biases contribute to the EP cold bias via the oceanic subtropical gyres, which is further enhanced by dynamical coupling in the equatorial region. The origin of the wind biases is examined by forcing the atmospheric component of the KCM in a stand-alone mode with observed SSTs and simulated SSTs from the coupled experiments. Wind biases over the EP, NP, and SP regions originate in the atmosphere model. The cold EP SST bias substantially enhances the wind biases over all three regions, while the NP and SP SST biases support local amplification of the wind bias. This study suggests that improving surface wind stress, at and off the equator, is a key to improve mean-state equatorial Pacific SST in climate models.
Abstract
El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual climate variability on the planet, with far-reaching global impacts. It is therefore key to evaluate ENSO simulations in state-of-the-art numerical models used to study past, present, and future climate. Recently, the Pacific Region Panel of the International Climate and Ocean: Variability, Predictability and Change (CLIVAR) Project, as a part of the World Climate Research Programme (WCRP), led a community-wide effort to evaluate the simulation of ENSO variability, teleconnections, and processes in climate models. The new CLIVAR 2020 ENSO metrics package enables model diagnosis, comparison, and evaluation to 1) highlight aspects that need improvement; 2) monitor progress across model generations; 3) help in selecting models that are well suited for particular analyses; 4) reveal links between various model biases, illuminating the impacts of those biases on ENSO and its sensitivity to climate change; and to 5) advance ENSO literacy. By interfacing with existing model evaluation tools, the ENSO metrics package enables rapid analysis of multipetabyte databases of simulations, such as those generated by the Coupled Model Intercomparison Project phases 5 (CMIP5) and 6 (CMIP6). The CMIP6 models are found to significantly outperform those from CMIP5 for 8 out of 24 ENSO-relevant metrics, with most CMIP6 models showing improved tropical Pacific seasonality and ENSO teleconnections. Only one ENSO metric is significantly degraded in CMIP6, namely, the coupling between the ocean surface and subsurface temperature anomalies, while the majority of metrics remain unchanged.
Abstract
El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual climate variability on the planet, with far-reaching global impacts. It is therefore key to evaluate ENSO simulations in state-of-the-art numerical models used to study past, present, and future climate. Recently, the Pacific Region Panel of the International Climate and Ocean: Variability, Predictability and Change (CLIVAR) Project, as a part of the World Climate Research Programme (WCRP), led a community-wide effort to evaluate the simulation of ENSO variability, teleconnections, and processes in climate models. The new CLIVAR 2020 ENSO metrics package enables model diagnosis, comparison, and evaluation to 1) highlight aspects that need improvement; 2) monitor progress across model generations; 3) help in selecting models that are well suited for particular analyses; 4) reveal links between various model biases, illuminating the impacts of those biases on ENSO and its sensitivity to climate change; and to 5) advance ENSO literacy. By interfacing with existing model evaluation tools, the ENSO metrics package enables rapid analysis of multipetabyte databases of simulations, such as those generated by the Coupled Model Intercomparison Project phases 5 (CMIP5) and 6 (CMIP6). The CMIP6 models are found to significantly outperform those from CMIP5 for 8 out of 24 ENSO-relevant metrics, with most CMIP6 models showing improved tropical Pacific seasonality and ENSO teleconnections. Only one ENSO metric is significantly degraded in CMIP6, namely, the coupling between the ocean surface and subsurface temperature anomalies, while the majority of metrics remain unchanged.
