Process-oriented Diagnostics in CMIP6 Models and Beyond
Description:
There is growing community interest in moving beyond typical model evaluation metrics to process-oriented diagnostics. These diagnostics better constrain poorly-represented physics components in climate models, provide actionable feedback to model developers, and are expected to play a key role in advancing the next-generation climate and earth system models.
The scope of this collection encompasses studies developing new process-oriented diagnostics—and the underlying understanding of climate system processes—as well as those applying existing diagnostics to climate models. Of particular interest are applications to models participating in the Phase 6 of the Coupled Model Intercomparison Project (CMIP6) models but the scope is open to diagnostics of models beyond CMIP6, including higher-resolution models.
The special collection solicits studies from all realms of the climate system, and therefore spans several American Meteorological Society (AMS) journals. The special collection is organized by members of the NOAA Model Diagnostics Force (MDTF). The collection contains contributions from current task force members as well as community-wide contributions.
Organizers:
J David Neelin, University of California, Los Angeles
John Krasting, Geophysical Fluid Dynamics Laboratory
Fiaz Ahmed, University of California, Los Angeles
Allison Wing, Florida State University
Eric Maloney, Colorado State University
Process-oriented Diagnostics in CMIP6 Models and Beyond
Abstract
The Indian Ocean Dipole (IOD), generated in the tropical Indian Ocean, fluctuates irregularly between its positive and negative phases. Given its profound impacts on regional and global climate patterns, it is crucial to understand potential IOD changes under climate change. This study investigates how the IOD pattern is modulated under global warming based on simulations from phase 6 of the Coupled Model Intercomparison Project. There is ∼6% decrease in sea surface temperature (SST) anomaly amplitude off the Sumatra coast and ∼14% increase along the equatorial eastern Indian Ocean under a high emission scenario. The reduced SST anomaly amplitude mainly stems from decreased thermodynamic air-sea feedback efficiency during positive IOD events, related to reduced mean rainfall in the east. Despite this, stronger SST anomalies along the equator are projected, due to increased zonal mixed-layer temperature advection by the mean current. For negative IOD events, increased amplitude is expected in the east, which can be attributed to enhanced zonal temperature advection and Ekman pumping term (linked to a more stratified upper ocean). These SST anomaly changes imply possible changes in IOD teleconnections and potential risks from IOD events for society and ecosystems in the face of greenhouse warming.
Abstract
The Indian Ocean Dipole (IOD), generated in the tropical Indian Ocean, fluctuates irregularly between its positive and negative phases. Given its profound impacts on regional and global climate patterns, it is crucial to understand potential IOD changes under climate change. This study investigates how the IOD pattern is modulated under global warming based on simulations from phase 6 of the Coupled Model Intercomparison Project. There is ∼6% decrease in sea surface temperature (SST) anomaly amplitude off the Sumatra coast and ∼14% increase along the equatorial eastern Indian Ocean under a high emission scenario. The reduced SST anomaly amplitude mainly stems from decreased thermodynamic air-sea feedback efficiency during positive IOD events, related to reduced mean rainfall in the east. Despite this, stronger SST anomalies along the equator are projected, due to increased zonal mixed-layer temperature advection by the mean current. For negative IOD events, increased amplitude is expected in the east, which can be attributed to enhanced zonal temperature advection and Ekman pumping term (linked to a more stratified upper ocean). These SST anomaly changes imply possible changes in IOD teleconnections and potential risks from IOD events for society and ecosystems in the face of greenhouse warming.
Abstract
Understanding the relationship between soil moisture (SM) and latent heat flux (LE) is crucial for forecasting seasonal extremes like heat waves and droughts and grasping long-term climate systems. Despite its importance, substantial disparities exist among various models’ interpretations of this relationship. This study defines four SM–LE indicators: wilting point, critical SM, saturated LE, and SM–LE slope, characterizing SM–LE regimes and corresponding breakpoints delineating the regimes using segmented regression, and scrutinizes model disparities by contrasting conventional coupled simulations with matching offline land model (LM) simulations within phase 6 of the Coupled Model Intercomparison Project (CMIP6) framework. It is revealed that those indicators have lower variability among ensemble members from the same model than among different models. Moreover, all indicators except for the SM–LE slope demonstrate strong correlations between the coupled and uncoupled simulations using the same LMs. While the spatial distributions of breakpoint values correspond only moderately well with the indicators’ values, they improve after adjusting for model atmospheric biases of precipitation and downward radiation. The impact of the atmospheric fields on the four SM–LE indicators is well correlated between the coupled and offline experiments, except for the relationship between precipitation and the wilting point. Consequently, this indicates a significant constraint of the SM–LE relationship in coupled model simulations determined by the LM’s behavior. Thus, one may determine much about the land–atmosphere coupling behavior parsimoniously in a model system without running the fully coupled model, justifying a hierarchical approach for model development and assessment.
Abstract
Understanding the relationship between soil moisture (SM) and latent heat flux (LE) is crucial for forecasting seasonal extremes like heat waves and droughts and grasping long-term climate systems. Despite its importance, substantial disparities exist among various models’ interpretations of this relationship. This study defines four SM–LE indicators: wilting point, critical SM, saturated LE, and SM–LE slope, characterizing SM–LE regimes and corresponding breakpoints delineating the regimes using segmented regression, and scrutinizes model disparities by contrasting conventional coupled simulations with matching offline land model (LM) simulations within phase 6 of the Coupled Model Intercomparison Project (CMIP6) framework. It is revealed that those indicators have lower variability among ensemble members from the same model than among different models. Moreover, all indicators except for the SM–LE slope demonstrate strong correlations between the coupled and uncoupled simulations using the same LMs. While the spatial distributions of breakpoint values correspond only moderately well with the indicators’ values, they improve after adjusting for model atmospheric biases of precipitation and downward radiation. The impact of the atmospheric fields on the four SM–LE indicators is well correlated between the coupled and offline experiments, except for the relationship between precipitation and the wilting point. Consequently, this indicates a significant constraint of the SM–LE relationship in coupled model simulations determined by the LM’s behavior. Thus, one may determine much about the land–atmosphere coupling behavior parsimoniously in a model system without running the fully coupled model, justifying a hierarchical approach for model development and assessment.
Abstract
The impact of ocean model resolution on sea level projections in the Southern Ocean is investigated using eddy-rich (ER) and eddy-parameterized configurations of the Max Planck Institute Earth System Model under the Shared Socioeconomic Pathway (SSP) 5-8.5 scenario. We employ the Flux-Anomaly Forced Model Intercomparison Project (FAFMIP) experiment—heat, stress, and freshwater perturbations—at both resolutions to pinpoint the sources of these differences. South of 55°S, we found that the changes in thermosteric and halosteric sea levels vary substantially between resolutions due to different responses to freshwater perturbations. In the eddy-parameterized model, the resulting increase in stratification suppresses the mixing of salt and heat from the Circumpolar Deep Water with surface layers. These cause differences in the response of surface fluxes and meridional transports yielding an increase in thermosteric sea levels and a decrease in halosteric sea levels. In the eddy-rich configuration, the main driver of eddy-induced warming and salinification between 40° and 44°S is wind stress perturbations. The efficiency of direct eddy effects in ER is restricted to small areas such as the Agulhas Retroflection, the Brazil–Malvinas confluence zone, the Tasman Sea, and, to some extent, the Antarctic Circumpolar Current (ACC). Contrary to expectations, ACC transport increases in the eddy-rich model while decreasing in the eddy-parameterized model under the SSP5-8.5 scenario. FAFMIP results reveal that this decrease is a result of the overcompensation of wind-induced changes by freshwater flux forcing. These results underscore the critical importance of high-resolution models for capturing the processes in sea level projections in the Southern Ocean and beyond.
Significance Statement
We studied how ocean model resolution affects sea level projections in the Southern Ocean using Max Planck Institute Earth System Model simulations. Higher-resolution models provide a more accurate representation of ocean circulation and its response to changing forcings. We examined how surface heat, momentum, and water fluxes, both separately and combined, shape ocean dynamics. In a strong global warming scenario, significant differences in steric sea level change were observed south of 55°S between the model that simulates eddies and the one that has their effects parameterized. The response to surface freshwater forcing is the primary cause of these differences. Our findings emphasize the critical role of ocean model resolution in accurately understanding and predicting future sea level changes, which is essential for effectively addressing our needs for adaptation.
Abstract
The impact of ocean model resolution on sea level projections in the Southern Ocean is investigated using eddy-rich (ER) and eddy-parameterized configurations of the Max Planck Institute Earth System Model under the Shared Socioeconomic Pathway (SSP) 5-8.5 scenario. We employ the Flux-Anomaly Forced Model Intercomparison Project (FAFMIP) experiment—heat, stress, and freshwater perturbations—at both resolutions to pinpoint the sources of these differences. South of 55°S, we found that the changes in thermosteric and halosteric sea levels vary substantially between resolutions due to different responses to freshwater perturbations. In the eddy-parameterized model, the resulting increase in stratification suppresses the mixing of salt and heat from the Circumpolar Deep Water with surface layers. These cause differences in the response of surface fluxes and meridional transports yielding an increase in thermosteric sea levels and a decrease in halosteric sea levels. In the eddy-rich configuration, the main driver of eddy-induced warming and salinification between 40° and 44°S is wind stress perturbations. The efficiency of direct eddy effects in ER is restricted to small areas such as the Agulhas Retroflection, the Brazil–Malvinas confluence zone, the Tasman Sea, and, to some extent, the Antarctic Circumpolar Current (ACC). Contrary to expectations, ACC transport increases in the eddy-rich model while decreasing in the eddy-parameterized model under the SSP5-8.5 scenario. FAFMIP results reveal that this decrease is a result of the overcompensation of wind-induced changes by freshwater flux forcing. These results underscore the critical importance of high-resolution models for capturing the processes in sea level projections in the Southern Ocean and beyond.
Significance Statement
We studied how ocean model resolution affects sea level projections in the Southern Ocean using Max Planck Institute Earth System Model simulations. Higher-resolution models provide a more accurate representation of ocean circulation and its response to changing forcings. We examined how surface heat, momentum, and water fluxes, both separately and combined, shape ocean dynamics. In a strong global warming scenario, significant differences in steric sea level change were observed south of 55°S between the model that simulates eddies and the one that has their effects parameterized. The response to surface freshwater forcing is the primary cause of these differences. Our findings emphasize the critical role of ocean model resolution in accurately understanding and predicting future sea level changes, which is essential for effectively addressing our needs for adaptation.
Abstract
Oceanic intraseasonal Kelvin waves (KWs) help modulate upper-ocean thermal characteristics, providing feedbacks to important coupled air–sea phenomena in the tropics. The recent availability of daily thermocline depth fields from several phase 6 of the Coupled Model Intercomparison Project (CMIP6) models makes it possible to evaluate the performance of KWs and identify potential sources of bias. Most models fail to simulate a realistic spatial distribution of KW variability. Models simulate a large variability of KWs in the western or eastern Pacific rather than in the central Pacific as observed. The modeled KWs propagate slowly (about 1.5 m s−1) compared to observations (about 2.5 m s−1). This slow propagation is also identified in wavenumber–frequency spectra for KWs and meridional KW structures, which is more consistent with a second baroclinic mode structure in models compared to the first baroclinic mode structure in observations. An analysis of the relative contributions of the vertical wavenumber and background ocean stability to KW phase speeds indicates that the high vertical wavenumber bias in models contributes most to the slow propagation, in which the higher-than-observed vertical wavenumbers imply the biased incorporation of higher baroclinic modes in the model KW structure. This finding is further supported by the results of vertical mode decomposition that incorporates background density profiles. These results indicate that a realistic representation of the KW vertical structure is essential to produce realistic KW propagations in models.
Significance Statement
Oceanic intraseasonal Kelvin waves (KWs) play a significant role in regulating the heat content and temperature of the ocean, which provides feedbacks to coupled ocean–atmosphere phenomena in the tropics such as El Niño–Southern Oscillation (ENSO). Consequently, identifying the biases in KWs and the potential sources of those biases in state-of-the-art models is essential to improve simulations of ENSO and its diversity and advance forecasts of weather extremes around the globe induced by ENSO. We find that KWs in the models propagate slower than observations, mainly due to biases in their vertical structure, with secondary effects due to biases in model ocean stability. These issues may be potential sources for current imperfect ENSO model simulations and predictions.
Abstract
Oceanic intraseasonal Kelvin waves (KWs) help modulate upper-ocean thermal characteristics, providing feedbacks to important coupled air–sea phenomena in the tropics. The recent availability of daily thermocline depth fields from several phase 6 of the Coupled Model Intercomparison Project (CMIP6) models makes it possible to evaluate the performance of KWs and identify potential sources of bias. Most models fail to simulate a realistic spatial distribution of KW variability. Models simulate a large variability of KWs in the western or eastern Pacific rather than in the central Pacific as observed. The modeled KWs propagate slowly (about 1.5 m s−1) compared to observations (about 2.5 m s−1). This slow propagation is also identified in wavenumber–frequency spectra for KWs and meridional KW structures, which is more consistent with a second baroclinic mode structure in models compared to the first baroclinic mode structure in observations. An analysis of the relative contributions of the vertical wavenumber and background ocean stability to KW phase speeds indicates that the high vertical wavenumber bias in models contributes most to the slow propagation, in which the higher-than-observed vertical wavenumbers imply the biased incorporation of higher baroclinic modes in the model KW structure. This finding is further supported by the results of vertical mode decomposition that incorporates background density profiles. These results indicate that a realistic representation of the KW vertical structure is essential to produce realistic KW propagations in models.
Significance Statement
Oceanic intraseasonal Kelvin waves (KWs) play a significant role in regulating the heat content and temperature of the ocean, which provides feedbacks to coupled ocean–atmosphere phenomena in the tropics such as El Niño–Southern Oscillation (ENSO). Consequently, identifying the biases in KWs and the potential sources of those biases in state-of-the-art models is essential to improve simulations of ENSO and its diversity and advance forecasts of weather extremes around the globe induced by ENSO. We find that KWs in the models propagate slower than observations, mainly due to biases in their vertical structure, with secondary effects due to biases in model ocean stability. These issues may be potential sources for current imperfect ENSO model simulations and predictions.
Abstract
The biases generated by state-of-the-art climate models in simulating dust optical depth (DOD) remain to be detailed. Here, a site-scale DOD dataset in March–August over northern China (NC) during 1980–2001 was reconstructed using the empirical relationship between MODIS-retrieved DOD and dust-event frequencies during 2001–21. Then, through the combined use of MODIS-based and reconstructed DOD, we evaluated the reproducibility of DOD from 10 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the historical period (1980–2001 and 2002–14) and under different Shared Socioeconomic Pathways (SSPs) during 2015–21. The results demonstrate that CMIP6 models and multimodel ensemble mean (MEM) are capable of capturing the spatial pattern of DOD, but with considerable uncertainty and intermodel variability in magnitude. Regionally averaged DOD is underestimated by 56.09% during 1980–2001 and overestimated by 30.97% during 2002–14 in MEM over NC. Simultaneously, the intermodel standard deviations are greater than MEM during 2002–14, suggesting large discrepancies among individual models. Very few models accurately capture the trends in DOD, which can mainly be attributed to the different trends in simulated wind speed (WS), soil moisture, and vegetation cover, and their contributions to dust evolution. Under four SSPs, despite the best correlation between SSP1-2.6-modeled and MODIS DOD over Gobi Desert (GD), overestimation of DOD is still observed. More models under SSP1-2.6 capture the positive DOD trend, mainly attributable to positive changes in simulated WS over GD.
Abstract
The biases generated by state-of-the-art climate models in simulating dust optical depth (DOD) remain to be detailed. Here, a site-scale DOD dataset in March–August over northern China (NC) during 1980–2001 was reconstructed using the empirical relationship between MODIS-retrieved DOD and dust-event frequencies during 2001–21. Then, through the combined use of MODIS-based and reconstructed DOD, we evaluated the reproducibility of DOD from 10 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the historical period (1980–2001 and 2002–14) and under different Shared Socioeconomic Pathways (SSPs) during 2015–21. The results demonstrate that CMIP6 models and multimodel ensemble mean (MEM) are capable of capturing the spatial pattern of DOD, but with considerable uncertainty and intermodel variability in magnitude. Regionally averaged DOD is underestimated by 56.09% during 1980–2001 and overestimated by 30.97% during 2002–14 in MEM over NC. Simultaneously, the intermodel standard deviations are greater than MEM during 2002–14, suggesting large discrepancies among individual models. Very few models accurately capture the trends in DOD, which can mainly be attributed to the different trends in simulated wind speed (WS), soil moisture, and vegetation cover, and their contributions to dust evolution. Under four SSPs, despite the best correlation between SSP1-2.6-modeled and MODIS DOD over Gobi Desert (GD), overestimation of DOD is still observed. More models under SSP1-2.6 capture the positive DOD trend, mainly attributable to positive changes in simulated WS over GD.
Abstract
Westerly wind events (WWEs) are anomalously strong, long-lasting westerlies over the Indian or Pacific Oceans that are capable of forcing oceanic wave modes, which in turn can impact the evolution of coupled ocean–atmosphere phenomena such as El Niño–Southern Oscillation (ENSO). This work examines the fidelity of equatorial WWEs over the Pacific Ocean in 30 CMIP6 historical simulations against observations. WWEs are identified using equatorially averaged zonal wind stress anomaly duration, zonal extent, and intensity criteria. Most simulations correctly place the majority of WWEs over the west Pacific although they are skewed westward and generally occur less frequently compared to observations. Simulated WWEs tend to be weaker than observations for a given duration and zonal extent with several models having shorter durations and zonal extents than observations. Biases in simulated WWEs are associated with biases in Madden–Julian oscillation (MJO) and convectively coupled Rossby wave (CRW) variability. Models that underpredict WWE forcing in the west Pacific also severely underpredict MJO and CRW variance. Further, the multimodel mean shows a smaller fraction of WWEs associated with both the MJO and CRW than observations.
Abstract
Westerly wind events (WWEs) are anomalously strong, long-lasting westerlies over the Indian or Pacific Oceans that are capable of forcing oceanic wave modes, which in turn can impact the evolution of coupled ocean–atmosphere phenomena such as El Niño–Southern Oscillation (ENSO). This work examines the fidelity of equatorial WWEs over the Pacific Ocean in 30 CMIP6 historical simulations against observations. WWEs are identified using equatorially averaged zonal wind stress anomaly duration, zonal extent, and intensity criteria. Most simulations correctly place the majority of WWEs over the west Pacific although they are skewed westward and generally occur less frequently compared to observations. Simulated WWEs tend to be weaker than observations for a given duration and zonal extent with several models having shorter durations and zonal extents than observations. Biases in simulated WWEs are associated with biases in Madden–Julian oscillation (MJO) and convectively coupled Rossby wave (CRW) variability. Models that underpredict WWE forcing in the west Pacific also severely underpredict MJO and CRW variance. Further, the multimodel mean shows a smaller fraction of WWEs associated with both the MJO and CRW than observations.
Abstract
The Indian Ocean dipole (IOD) is a prominent interannual phenomenon in the tropical Indian Ocean (TIO), influencing weather and climate globally, particularly during extreme IOD events. The IOD shows notable amplitude asymmetry in both observations and historical simulations from the phase 6 of Coupled Model Intercomparison Project (CMIP6), with positive events having a greater magnitude than negative events, mainly due to the negative nonlinear dynamical heating. However, simulations under the shared socioeconomic pathway 5-8.5 (SSP5-8.5) scenario indicate a notable reduction in IOD asymmetry. It shows that this reduction points to an increased frequency of extreme negative IOD events under global warming. The primary cause of this reduced IOD asymmetry is less negative nonlinear dynamical heating in future simulations, especially the nonlinear zonal advection. Under global warming, the increased atmospheric static stability weakens the large-scale atmospheric response to sea surface temperature (SST) anomalies forcing. This leads to reduced strength of nonlinear zonal advection, resulting in a decreased IOD asymmetry. Nevertheless, nonlinear vertical advection, another key factor in IOD asymmetry, remains comparable due to the increased upper-ocean stratification in the eastern TIO. The reduced inhibition of negative nonlinear zonal advection and the increased SST response to deepening thermocline contribute to the increased frequency of extreme negative IOD events. These changes underscore the potential risks associated with negative IOD events in a warming world, emphasizing the importance of understanding IOD dynamics for improved climate impact prediction and future preparedness.
Abstract
The Indian Ocean dipole (IOD) is a prominent interannual phenomenon in the tropical Indian Ocean (TIO), influencing weather and climate globally, particularly during extreme IOD events. The IOD shows notable amplitude asymmetry in both observations and historical simulations from the phase 6 of Coupled Model Intercomparison Project (CMIP6), with positive events having a greater magnitude than negative events, mainly due to the negative nonlinear dynamical heating. However, simulations under the shared socioeconomic pathway 5-8.5 (SSP5-8.5) scenario indicate a notable reduction in IOD asymmetry. It shows that this reduction points to an increased frequency of extreme negative IOD events under global warming. The primary cause of this reduced IOD asymmetry is less negative nonlinear dynamical heating in future simulations, especially the nonlinear zonal advection. Under global warming, the increased atmospheric static stability weakens the large-scale atmospheric response to sea surface temperature (SST) anomalies forcing. This leads to reduced strength of nonlinear zonal advection, resulting in a decreased IOD asymmetry. Nevertheless, nonlinear vertical advection, another key factor in IOD asymmetry, remains comparable due to the increased upper-ocean stratification in the eastern TIO. The reduced inhibition of negative nonlinear zonal advection and the increased SST response to deepening thermocline contribute to the increased frequency of extreme negative IOD events. These changes underscore the potential risks associated with negative IOD events in a warming world, emphasizing the importance of understanding IOD dynamics for improved climate impact prediction and future preparedness.
Abstract
Conditional instability and the buoyancy of plumes drive moist convection but have a variety of representations in model convective schemes. Vertical thermodynamic structure information from Atmospheric Radiation Measurement (ARM) sites and reanalysis (ERA5), satellite-derived precipitation (TRMM3b42), and diagnostics relevant for plume buoyancy are used to assess climate models. Previous work has shown that CMIP6 models represent moist convective processes more accurately than their CMIP5 counterparts. However, certain biases in convective onset remain pervasive among generations of CMIP modeling efforts. We diagnose these biases in a cohort of nine CMIP6 models with subdaily output, assessing conditional instability in profiles of equivalent potential temperature, θe , and saturation equivalent potential temperature, θes , in comparison to a plume model with different mixing assumptions. Most models capture qualitative aspects of the θes vertical structure, including a substantial decrease with height in the lower free troposphere associated with the entrainment of subsaturated air. We define a “pseudo-entrainment” diagnostic that combines subsaturation and a θes measure of conditional instability similar to what entrainment would produce under the small-buoyancy approximation. This captures the trade-off between larger θes lapse rates (entrainment of dry air) and small subsaturation (permits positive buoyancy despite high entrainment). This pseudo-entrainment diagnostic is also a reasonable indicator of the critical value of integrated buoyancy for precipitation onset. Models with poor θe /θes structure (those using variants of the Tiedtke scheme) or low entrainment runs of CAM5, and models with low subsaturation, such as NASA-GISS, lie outside the observational range in this diagnostic.
Abstract
Conditional instability and the buoyancy of plumes drive moist convection but have a variety of representations in model convective schemes. Vertical thermodynamic structure information from Atmospheric Radiation Measurement (ARM) sites and reanalysis (ERA5), satellite-derived precipitation (TRMM3b42), and diagnostics relevant for plume buoyancy are used to assess climate models. Previous work has shown that CMIP6 models represent moist convective processes more accurately than their CMIP5 counterparts. However, certain biases in convective onset remain pervasive among generations of CMIP modeling efforts. We diagnose these biases in a cohort of nine CMIP6 models with subdaily output, assessing conditional instability in profiles of equivalent potential temperature, θe , and saturation equivalent potential temperature, θes , in comparison to a plume model with different mixing assumptions. Most models capture qualitative aspects of the θes vertical structure, including a substantial decrease with height in the lower free troposphere associated with the entrainment of subsaturated air. We define a “pseudo-entrainment” diagnostic that combines subsaturation and a θes measure of conditional instability similar to what entrainment would produce under the small-buoyancy approximation. This captures the trade-off between larger θes lapse rates (entrainment of dry air) and small subsaturation (permits positive buoyancy despite high entrainment). This pseudo-entrainment diagnostic is also a reasonable indicator of the critical value of integrated buoyancy for precipitation onset. Models with poor θe /θes structure (those using variants of the Tiedtke scheme) or low entrainment runs of CAM5, and models with low subsaturation, such as NASA-GISS, lie outside the observational range in this diagnostic.
Abstract
The Indian Ocean dipole (IOD) is the dominant mode of interannual variability in the tropical Indian Ocean (TIO), characterized by warming (cooling) in western TIO and cooling (warming) in eastern TIO during its positive (negative) phase. Observed IOD events exhibit distinct amplitude asymmetry in relation to negative nonlinear dynamic heating. Nearly all models in phase 5 of the Coupled Model Intercomparison Project (CMIP) simulate a less-skewed IOD than observed, but 6 out of 20 CMIP6 models can reproduce realistic high skewness. Analysis of less-skewed models indicates that the positive IOD-like biases in the mean state, which can be traced back to their weaker simulations of the preceding Indian summer monsoon, reduce the convective response to positive sea surface temperature anomalies in the western TIO, resulting in a weaker zonal wind response and weaker nonlinear zonal advection during positive IOD events. Besides, ocean stratification in the eastern TIO influences the IOD skewness: stronger stratification leads to larger mixed-layer temperature response to thermocline changes, contributing to larger anomalous vertical temperature gradient, larger nonlinear vertical advection, and thus stronger positive IOD skewness. Our findings underscore the importance of reducing Indian summer monsoon biases and eastern TIO stratification biases, for properly representing the IOD in Earth system models.
Abstract
The Indian Ocean dipole (IOD) is the dominant mode of interannual variability in the tropical Indian Ocean (TIO), characterized by warming (cooling) in western TIO and cooling (warming) in eastern TIO during its positive (negative) phase. Observed IOD events exhibit distinct amplitude asymmetry in relation to negative nonlinear dynamic heating. Nearly all models in phase 5 of the Coupled Model Intercomparison Project (CMIP) simulate a less-skewed IOD than observed, but 6 out of 20 CMIP6 models can reproduce realistic high skewness. Analysis of less-skewed models indicates that the positive IOD-like biases in the mean state, which can be traced back to their weaker simulations of the preceding Indian summer monsoon, reduce the convective response to positive sea surface temperature anomalies in the western TIO, resulting in a weaker zonal wind response and weaker nonlinear zonal advection during positive IOD events. Besides, ocean stratification in the eastern TIO influences the IOD skewness: stronger stratification leads to larger mixed-layer temperature response to thermocline changes, contributing to larger anomalous vertical temperature gradient, larger nonlinear vertical advection, and thus stronger positive IOD skewness. Our findings underscore the importance of reducing Indian summer monsoon biases and eastern TIO stratification biases, for properly representing the IOD in Earth system models.
Abstract
Previous studies have hypothesized that climatologically thick salinity-stratified barrier layers (BLs) in the north Indian Ocean (NIO) influence the upper ocean heat budget, sea surface temperature (SST), and monsoons. Here, we investigate how state-of-the-art Coupled Model Intercomparison Project phase 6 (CMIP6) climate models simulate the NIO barrier layer thickness (BLT). CMIP6 models generally reproduce the BLT seasonal cycle and spatial distribution, but with shallow November–February (NDJF) biases in regions with thick observed BLT: the eastern equatorial Indian Ocean (EEIO), Bay of Bengal (BoB), and southeastern Arabian Sea (SEAS). We show that the intensity of the CMIP6 equatorial easterly wind bias controls the EEIO shallow isothermal layer depth (ILD) and BLT biases. It also controls the BoB shallow BLT bias, both through the propagation of the EEIO shallow ILD bias into the NIO coastal waveguide and because it is linked to the BoB dry and cold bias through the Bjerknes feedback, hence also controlling the mixed layer depth (MLD) deep bias there. Finally, the SEAS shallow BLT bias is due to a too-deep MLD, in response to subdued monsoonal currents around India, which do not bring enough BoB low-salinity water. The BL insulating effect mentioned in literature does not seem to dominate in CMIP6. Rather, the CMIP6 salinity-related deep MLD biases diminish the BoB cooling rate by winter upward surface heat fluxes, reducing cold SST biases. This suggests that salinity effects alleviate the easterly equatorial wind, cold, and dry BoB biases that develop through the positive Bjerknes feedback loop in CMIP6.
Abstract
Previous studies have hypothesized that climatologically thick salinity-stratified barrier layers (BLs) in the north Indian Ocean (NIO) influence the upper ocean heat budget, sea surface temperature (SST), and monsoons. Here, we investigate how state-of-the-art Coupled Model Intercomparison Project phase 6 (CMIP6) climate models simulate the NIO barrier layer thickness (BLT). CMIP6 models generally reproduce the BLT seasonal cycle and spatial distribution, but with shallow November–February (NDJF) biases in regions with thick observed BLT: the eastern equatorial Indian Ocean (EEIO), Bay of Bengal (BoB), and southeastern Arabian Sea (SEAS). We show that the intensity of the CMIP6 equatorial easterly wind bias controls the EEIO shallow isothermal layer depth (ILD) and BLT biases. It also controls the BoB shallow BLT bias, both through the propagation of the EEIO shallow ILD bias into the NIO coastal waveguide and because it is linked to the BoB dry and cold bias through the Bjerknes feedback, hence also controlling the mixed layer depth (MLD) deep bias there. Finally, the SEAS shallow BLT bias is due to a too-deep MLD, in response to subdued monsoonal currents around India, which do not bring enough BoB low-salinity water. The BL insulating effect mentioned in literature does not seem to dominate in CMIP6. Rather, the CMIP6 salinity-related deep MLD biases diminish the BoB cooling rate by winter upward surface heat fluxes, reducing cold SST biases. This suggests that salinity effects alleviate the easterly equatorial wind, cold, and dry BoB biases that develop through the positive Bjerknes feedback loop in CMIP6.
