On The Simulation of Northeast Monsoon Rainfall Over Southern Peninsular India in CMIP5 Models

The skill of 34 CMIP5 models to simulate the mean state and interannual variability of Northeast Monsoon Rainfall (NEMR) is studied here. The mean (1979-2005) NEMR over southern Peninsular India (SPIRF), Indian Ocean and Maritime continents (10°S-30°N,40°E- 120°E) is simulated reasonably well by CMIP5 models with pattern correlation ranges from 0.6 to 0.93. Diverse behaviour in the simulation of Indian and Pacific Ocean SST is observed in the CMIP5 models. A set of models (high skill models: HSM), which shows a Negative Indian Ocean Dipole (NIOD) like mean (1979-2005) SST bias in Indian Ocean and strong La Nina like mean SST bias in the Pacific Ocean, are able to simulate the mean NEMR more realistically. Another set of models (low skill models: LSM) which shows a Positive IOD (PIOD) like mean SST bias in the Indian Ocean and weak La Nina like mean SST bias in the Pacific Ocean are not able to simulate the observed equatorial Indian Ocean westerlies, which leads to an abnormal ascending motion and unrealistic wet bias over the western Indian Ocean and dry bias over the southern Peninsular India, southeast Asia and southeast Indian Ocean. The observation analysis reveals that the establishment of South China Sea anticyclone and Bay of Bengal anticyclone during El Nino and PIOD are strongly related with the ascending motion over south peninsular India and enhances the south Peninsular Indian rainfall during NEM season. Around 70% of the CMIP5 models were not able to capture the observed positive correlation that exist between SPIRF and Nino3.4 SST as well as SPIRF and DMI. Unrealistic westward extension of South China Sea anticyclone and Bay of Bengal anticyclone (up to 70°E) in the low skill models (LSM-IAV) manifested as the abnormal descending anomalies and unrealistic dry bias over the southern Peninsular India. This leads to a negative Correlation coefficient (CC) between SPIRF and Nino 3.4 SST as well as SPIRF and DMI in the low skill models. The descending anomalies over South China Sea and ascending anomalies over the western Indian Ocean and southern Peninsular India (50°E-80°E) is well captured but with lower intensity in the high skill models (HSM-IAV) and hence it captures the observed positive CC between SPIRF and Nino3.4 SST as well as SPIRF and DMI.

Seasonal extrema of sea surface temperature in CMIP6 models

CMIP6 model sea surface temperature (SST) seasonal extrema averaged over 1981–2010 are assessed against the World Ocean Atlas (WOA18) observational climatology. We propose a mask to identify and exclude regions of large differences between three commonly-used climatologies. The biases in SST seasonal extrema are largely consistent with the annual mean SST biases. However, the amplitude and spatial pattern of SST bias vary seasonally in the 20 CMIP6 models assessed. Large seasonal variations in the SST bias occur in eastern boundary upwelling regions, polar regions, the North Pacific and eastern equatorial Atlantic. These results demonstrate the importance of evaluating model performance not simply against annual mean properties. Models with greater vertical resolution in their ocean component typically demonstrate better representation of SST extrema, particularly seasonal maximum SST. No significant relationship with horizontal ocean model resolution is found.

Atmosphere-Driven Cold SST Biases Over The Western North Pacific in The GloSea5 Seasonal Forecast System

The predictability of the sea surface temperature (SST) in seasonal forecast systems is crucial for accurate seasonal predictions. In this study, we evaluate the prediction of SST in the Global Seasonal forecast system version 5 (GloSea5) hindcast with particular interest over the western North Pacific (WNP) in which the SST can modify atmospheric convection and the East Asian weather. GloSea5 has a cold SST bias in the WNP that grows over at least 7 months. The bias originates from the surface heat flux in which the latent heat flux bias shows the biggest contribution. We identify the overestimated cloud in the first few days after initialization that causes insufficient shortwave radiation and negative bias of the surface net heat flux. Uncoupled ocean model experiments infer that the ocean model is unlikely the primary source of the SST bias.

Disentangling the effect of regional SST bias on the double-ITCZ problem

This study investigates the causes of the double intertropical convergence zone (ITCZ) bias by disentangling the individual contribution of regional sea surface temperature (SST) biases. We show that a previously suggested Southern Ocean warm bias effect in displacing the zonal-mean ITCZ southward is diminished by the southern midlatitude cold bias effect. The northern extratropical cold bias turns out to be most responsible for a southward-displaced zonal-mean precipitation, but the zonal-mean diagnostics poorly represent the spatial pattern of the tropical Pacific response. Examination of longitude-latitude structure indicates that the overall spatial pattern of tropical precipitation bias is largely shaped by the local SST bias. The southeastern tropical Pacific wet bias is driven by warm bias along the west coast of South America with negligible influence from the Southern Ocean warm bias. While our model experiments are idealized with ocean dynamics being absent, the results shed light on where preferential foci should be applied in model development to improve the certain features of tropical precipitation bias.

Recent development of a supermodel - an interactive multi-model ensemble

<p>An interactive multi-model ensemble (named as supermodel) based on three state-of-the-art earth system models (i.e., NorESM, MPIESM and CESM) is developed. The models are synchronized every month by data assimilation. The data assimilation method used is the Ensemble Optimal Interpolation (EnOI) scheme, for which the covariance matrix is constructed from a historical ensemble. The assimilated data is a weighted combination of the monthly output sea surface temperature (SST) of these individual models, but the full ocean state is constrained by the covariance matrix. The synchronization of the models during the model simulation makes this approach different from the traditional multi-model ensemble approach in which model outputs are combined a-posteriori.</p><p>We compare the different approaches to estimate the supermodel weights: equal weights, spatially varying weights based on the minimisation of the bias. The performance of these supermodels is compared to that of the individual models, and multi-model ensemble for the period 1980 to 2006. SST synchronisation is achieved in most oceans and in dynamical regimes such as ENSO. The supermodel with spatially varying weights overperforms the supermodel with equal weights. It reduces the SST bias by over 30% compare to the multi-model ensemble. The temporal variability of the supermodel is slightly on the low side but improved compared to the multi-model ensemble. The simulations are being extended to 2100 to assess the simulation of climate variability and climate change. Performing prediction experiments with the supermodel is the main perspective in the next step.  </p>

On the simulation of northeast monsoon rainfall over southern peninsular India in CMIP5 models

The skill of 34 CMIP5 models to simulate the mean state and interannual variability of Northeast Monsoon Rainfall (NEMR) is studied here. The mean (1979–2005) NEMR over southern Peninsular India (SPIRF), Indian Ocean and Maritime continents (10°S-30°N,40°E-120°E) is simulated reasonably well by CMIP5 models with pattern correlation ranges from 0.6 to 0.93. A few individual models have been found to be outperformed the multi model ensemble (PCC-0.88). Diverse behaviour in the simulation of Indian and Pacific Ocean SST is observed in the CMIP5 models. A set of models (high skill models: HSM), which shows an NIOD like mean (1979–2005) SST bias in Indian Ocean and strong La Nina like mean SST bias in the Pacific Ocean, are able to simulate the mean NEMR more realistically. Another set of models (low skill models: LSM) which shows a Positive IOD (PIOD) like mean SST bias in the Indian Ocean and a weak La Nina like mean SST bias in the Pacific Ocean are not able to simulate the observed equatorial Indian Ocean westerlies. This leads to an abnormal ascending motion and unrealistic wet bias over the western Indian Ocean and dry bias over the southern Peninsular India, southeast Asia and southeast Indian Ocean. Observational analysis reveals that the ascending anomalies over warm pool in the climatological mean Walker circulation during NEM season is modified as ascending anomalies to the east and west of warm pool region and descending anomalies over warm pool region during El Nino and PIOD. This modulation is manifested as an interesting pattern of warm and wet western Indian Ocean, southern Peninsular India and central and eastern Pacific Ocean and cool and dry warm pool region including Maritime continents.The observation analysis also reveals that the establishment of South China Sea anticyclone and Bay of Bengal anticyclone during El Nino and PIOD are strongly related with the ascending motion over south peninsular India and hence enhances the south Peninsular Indian rainfall during NEM season. Around 70% of the CMIP5 models were not able to capture the observed positive correlation that exist between SPIRF and Nino3.4 SST as well as SPIRF and DMI. An unrealistic westward extension of warm anomalies over the equatorial Pacific cold tongue is observed in low skill models (LSM-IAV). Unrealistic westward extension of South China Sea anticyclone and Bay of Bengal anticyclone (up to 70°E) is also observed in the LSM-IAV model ensemble. This is manifested as the abnormal descending anomalies and unrealistic dry bias over the southern Peninsular India and hence the unrealistic negative CC between SPIRF and Nino 3.4 SST (SPIRF-DMI). The descending anomalies over South China Sea and ascending anomalies over the western Indian Ocean and southern Peninsular India (50°E-80°E) is well captured but with lower intensity in HSM-IAV and hence it shows the observed positive CC between SPIRF and Nino3.4 SST as well as SPIRF and DMI.

Origins of the excessive westward extension of ENSO SST simulated in CMIP5/6 models

An excessive westward extension of the simulated ENSO-related sea surface temperature (ENSO SST) variability in the CMIP5 and CMIP6 models is the most apparent ENSO SST pattern bias and dominates the intermodel spread in ENSO SST variability among the models. The ENSO SST bias lowers the models’ skill in ENSO-related simulations and induces large intermodel uncertainty in ENSO-related projections. The present study investigates the origins of the excessive westward extension of ENSO SST in 25 CMIP5 and 25 CMIP6 models. Based on the intermodel spread of ENSO SST variability simulated in the 50 models, we reveal that this ENSO SST bias among the models largely depends on the simulated cold tongue strength in the equatorial western Pacific (EWP). Models simulating a stronger cold tongue tend to simulate a larger mean zonal SST gradient in the EWP and then a larger zonal advection feedback in the EWP, favoring a more westward extension of the ENSO SST pattern. In addition, with the overall improvement in the EWP cold tongue from CMIP5 to CMIP6, the excessive westward extension bias of ENSO SST in CMIP6 models is also reduced relative to those in CMIP5 models. The results suggest that the bias and intermodel disagreement in the mean-state SST have been improved, which benefits to improving ENSO simulation.

Role of Equatorial Cold Tongue in Central Pacific Double-ITCZ Bias in the NCAR CESM1.2

Warm SST bias underlying the spurious southern ITCZ has long been recognized as one of the main causes for double-ITCZ bias in coupled GCMs in the central Pacific. This study demonstrates that the NCAR CESM1.2 can still simulate significant double-ITCZ bias even with cold SST bias in the southern ITCZ region, indicating that warm SST bias is not a necessary condition for double-ITCZ bias in the central Pacific. Further analyses suggest that the equatorial cold tongue (ECT) biases play important roles in the formation of double-ITCZ bias in the central Pacific. The severe cold SST biases in the ECT region in the central Pacific may enhance the SST gradient between the ECT and southern ITCZ region, strengthening the lower-troposphere dynamical convergence and hence convection in the southern ITCZ region. The formation mechanism of excessive ECT bias is further investigated. It is shown that the cold SST biases in the ECT region can be largely attributed to the anomalous cooling tendency produced by the upper-ocean zonal advection due to overly strong zonal currents. In the ECT region, the westward ocean surface zonal current is driven by the equatorial easterly surface winds. It is shown that convection bias simulated by the atmospheric model in the equatorial Amazon region may lead to easterly wind bias in the downwind side (west) of convection region. The mean Walker circulation transports these easterly wind momentum anomalies downward and westward to the surface, resulting in the overly strong surface easterly wind in the central equatorial Pacific.