AS1.10

Extreme precipitation is expected to increase with climate change at the Clausius-Clapeyron rate of approximately 7% per °C of warming; however, tropical cyclone (TC) precipitation may increase at a greater rate due to feedbacks between the storm dynamics and the thermodynamic increase in moisture. Previous modeling studies simulate increasing TC intensities with warming sea surface temperatures (SSTs), which may push the precipitation increase above the Clausius-Clapeyron rate. Recent work by the authors used the Community Atmosphere Model (CAM) in a state of radiative-convective equilibrium (RCE) with globally-uniform SSTs varying between 295 and 305 K to break down the TC precipitation response to warming into thermodynamic and dynamic contributions. Results showed that for 99th percentile TC precipitation, increases in atmospheric moisture (thermodynamics) contributed just over 66% of the precipitation increase while increases in TC intensity (dynamics) contributed about 20%. This work explores if the relationship between TC precipitation, SST, and storm intensities found in the RCE simulations holds for observations and high-resolution climate model simulations. The observations consist of TC tracks and intensities from the IBTrACS database, SSTs from the NOAA OISST dataset, and precipitation from the IMERG satellite product. The high-resolution climate model simulations are from the High Resolution Model Intercomparison Project (HighResMIP), a CMIP6-endorsed MIP that has both historical and future climate runs. The methodology involves extracting TC precipitation using an automated algorithm, binning TCs by relevant characteristics (i.e., their local-environment SSTs, intensities, and outer sizes), extracting various precipitation metrics from their precipitation fields, and calculating relationships between the precipitation metrics, TC characteristics, and SSTs. The goal is to use these relationships to project future TC precipitation changes under different future climate change scenarios using just changes in SST.

How to cite: Stansfield, A. and Reed, K.: Projecting Future Tropical Cyclone Precipitation Increases using a Hierarchical Modeling Framework, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6117, https://doi.org/10.5194/egusphere-egu22-6117, 2022.

In recent years, western Europe has been threatened by anomalous tropical cyclones, developed from tropical transition (TT) processes in which a baroclinic cyclone becomes in a fully barotropic cyclone. Thirty-three tropical transition events were identified in the North Atlantic basin during the period 1979-2019 using ERA5 and HURDAT datasets. The TTs show a favored seasonality covering 70% of total between September and November.

A TT climatology is built and analyzed using large-scale storm-centered composites to study their common features and highlighting their differences respect the long-term climatology. The results reveal that TT synoptic environment is mainly characterized by a trough at 300 hPa and a strong anticyclone located north of the cyclone. In addition, a previous westerlies meridional trough with quasigeostrophic forcing acts as precursor. As the ERA5 does not accurately represent the diabatic processes due to its horizontal resolution, the deepening of mean sea level pressure is not shown in the composites. The average Potential Vorticity 300-200 hPa (PV) shows a decreasing in the upper troposphere around the cyclones as the moment of TT is approaching, while the PV is increasing in the lower troposphere. This PV conjunction promotes low-level wind speed intensification around the cyclone center that is linked with differential diabatic heat source in the low troposphere.

How to cite: Calvo-Sancho, C., González-Alemán, J. J., Bolgiani, P., Santos-Muñoz, D., Farrán, J. I., Sastre, M., and Martín, M. L.: A Climatology of Tropical Transitions in the North Atlantic Ocean, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2395, https://doi.org/10.5194/egusphere-egu22-2395, 2022.

The direct detection — or tracking — of tropical cyclones (TC) in gridded datasets outputs from reanalyses or model simulations is required to assess TC statistics. This issue has been tackled independently by many modeling centers or research groups; hence there is little homogeneity in the existing methods. The trackers – i.e., the algorithms used to perform that tracking -- generally fall into one of two categories: physics-based or dynamics-based. Physics-based trackers use sea-level pressure as their primary tracking variable, with additional warm-core and intensity criteria, whereas dynamics-based trackers use kinematic variables such as vorticity.

We compared four trackers taken from both categories and that we deem very different from one another in terms of their formulation: UZ (sometimes called TempestExtremes, Ullrich et al. 2021), OWZ (Tory et al. 2013), TRACK (Hodges et al. 2017) and CNRM (Chauvin et al. 2016). We assessed their performances by tracking TCs in ERA5 and comparing the outcome to the IBTrACS database – a collection of TC observations from several meteorological centers worldwide.

We find typical detection rates ranging from 70 to 80% and False Alarm (FA) rates ranging from 20 to 50% depending on the trackers. Based on the finding that a large proportion of these FAs are extra-tropical cyclones, we adapted an existing filtering method that relies on the relative positions of the detected tracks and the upper troposphere subtropical jet. When applied identically to the four trackers, it reduces FA rates to figures ranging from 9 to 30% while leaving detection rates unchanged.

Even though we were able to find most of the observed TCs in ERA5, we find, in agreement with several results in the recent literature, that their intensity is largely underestimated. However, and perhaps counterintuitively, there is no simple attenuation relationship between observed and reanalyzed TCs: for example, the strongest observed TCs are found in ERA5 with intensities covering almost the entire TC intensity scale.

We conclude by providing guidelines applicable when faced with the question of which tracker(s) to use depending on the research question. In particular, we show that using several trackers is not necessarily relevant for optimizing detection skills but combining them can be helpful to gain insight into different aspects of TCs in the same dataset.

Finally, we used the expertise gained above to track TCs in a set of HighResMIP simulations performed with the IPSL-CM7A model at different resolutions. In agreement with recent results, we find that the ability to simulate TCs improves significantly with resolution. Even though the intensity of simulated TCs remains too weak on average, the global statistics approach observations for simulations at a few tens of kilometers of horizontal resolution.

How to cite: Bourdin, S., Fromang, S., Dulac, W., Cattiaux, J., and Chauvin, F.: Tracking tropical cyclones in reanalysis and simulations: guidelines from an intercomparison of four algorithms, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1806, https://doi.org/10.5194/egusphere-egu22-1806, 2022.

In this study, we have examined meteorological drivers that led to the genesis of tropical cyclone Seroja. Developing over the Maritime Continent and in April 2021, it brought historic flooding and landslides to southern Indonesia, East Timor and Western Australia’s Mid West region. Seroja was the first tropical cyclone to have a significant impact on Indonesian land.

We have shown that the genesis of tropical cyclone Seroja in the region of Timor and Suvu Seas was associated with enhanced Equatorial Convection on March 27, 2021 which was preceded by warm sea surface anomalies (SSTs) in that region. The Equatorial Convection was related to Madden-Julian Oscillation (MJO) mode: it developed on the leading edge of MJO where SSTs were high. We have also investigated the role of tropical waves in the development of tropical cyclone Seroja. The interaction between convectively coupled equatorial Rossby wave and three convectively coupled Kelvin waves embedded within the larger-scale envelope of the MJO, provided a supportive environment for this extreme event. The Equatorial Convection that eventually became tropical cyclone Seroja moved southwest, boosted by environmental cyclonic vorticity associated with Rossby Wave. Each of the three Kelvin Waves that arrived over the Maritime Continent had a unique contribution in this event; structuring the convection, winds and precipitation patterns.

How to cite: Latos, B., Peyrillé, P., Lefort, T., Flatau, M., Flatau, P., Baranowski, D., Florida, N., Permana, D., and Szkółka, W.: The role of tropical waves in the genesis of tropical cyclone Seroja, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-566, https://doi.org/10.5194/egusphere-egu22-566, 2022.

The vertical structure of precipitation and its evolution during mid-level dry-air intrusion (DAI) for landfalling tropical cyclones (LTCs) over China in the past 10 years were examined using several observed TRMM PR products and a reanalysis dataset. We show that in the outer region where the environmental mid-level dry air intrudes more easily, the process of environmental DAI has an important effect on the vertical structure and precipitation of LTCs through promoting substantially stratiform precipitation while inhibiting infrequent intense convective precipitation. Although the total mean rain rate does not change much during the DAI period, both the mean rain rate and area of stratiform precipitation are almost doubled, while the convective precipitation halves compared to the situation prior to the DAI period. Also, the vertical structure of precipitation relative to the vertical wind shear (VWS) is modulated by the dry air, with a clear stratiform precipitation structure in the DAI region, though the dry-air distribution of LTCs does not depend on the direction of the VWS but rather on the synoptic environmental collocation. Further analysis shows that the mid-level DAI is favorable to the generation of stratiform precipitation through producing moderate mid-level convergence and less intense low-level subsidence, which contribute to the mid-level spin-up without spinning down the low-level circulation. At the same time, it helps maintaining the uniform stratiform precipitation above the melting layer and homogenizing the low-level circulation, and thus boosts the development of stratiform precipitation in intensity and area in the outer region.

How to cite: Shu, S.: How does dry air influence the precipitation of landfalling tropical cyclones, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1492, https://doi.org/10.5194/egusphere-egu22-1492, 2022.

Tropical cyclones (TCs) are some of the most dangerous natural hazards that human civilisation is exposed to. Effective adaptation for coastal regions requires reliable forecasts of risks for the season. Natural Hazard models such as the Synthetic Tropical cyclOne genRation Model (STORM) developed by Bloemendaal et al. (2020) are a common choice to assess risks without the expense of running a full forecast model. STORM has so far only been compared to observations on a basin-wide scale. However, for useful risk assessments in coastal regions, the model is also required to be skilful on much smaller spatial scales. We examine landfall statistics in some key areas such as the Gulf of Mexico.  Numerous indices for TC genesis have been developed over the past decades that aim to derive genesis locations from meteorological variables. None of the currently operational indices however is capable of realistically modelling interannual variability in genesis numbers and locations. Here, we compare the purely statistical Poisson interannual variability to that observed. Using Poisson regression between observations and driving environmental variables such as relative sea surface temperatures and wind shear, we then produce a new index for genesis location that has better predictive skill on interannual time scales.

How to cite: Ermis, S. and Toumi, R.: Modelling interannual variability in a tropical cyclone hazard model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1725, https://doi.org/10.5194/egusphere-egu22-1725, 2022.

The diurnal variation of tropical oceanic convection has been recognized for decades. Recent observational studies have also documented a diurnal cycle associated with the upper-level cirrus canopy of tropical cyclone (TC) measured using the infrared brightness temperature from satellites. However, TC canopy clouds are not always coupled tightly with deep convection. The overshooting top (OT) is an appropriate proxy for deep convection with an intense updraft that can penetrate the tropopause, which has an important influence on typhoon intensification. So far, there are no observational evidences for the relationship between diurnal intensity variation and OT occurrences in TC.

We analyze the diurnal variation of OTs within 45 western North Pacific typhoons, using 9003 Himawari-8 satellite images and a unique OT detection algorithm. We examine the distribution of OTs in different types of typhoons in terms of both intensity and intensity change and the relationship between the OTs and typhoon intensification on a diurnal scale. Our results show that a greater OT density occurs in strong typhoons and rapid intensification (RI) typhoons. Moreover, RI typhoons showed greater diurnal variation than non-RI typhoons. The diurnal cycle of OT density in RI typhoons was in phase with the intensification of the typhoon, with the maximum in the early morning. These observational results are consistent with recently published case study simulations of the diurnal radiation effects on TC in both realistic and idealized scenarios. Therefore, OT density can become a potentially effective indicator to estimate diurnal changes in typhoon intensity.

How to cite: Tang, X., Sun, L., Zhuge, X., Tan, Z.-M., and Fang, J.: Diurnal Variation of Clouds Overshooting Tops Detected by Himawari-8 Satellite and Typhoon Intensity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3862, https://doi.org/10.5194/egusphere-egu22-3862, 2022.

Tropical storms that develop over the North Indian Ocean basin pose a major threat to the extensive peninsular coastlines teeming with overpopulated cities and vast areas of low-lying farmlands. With each year, the economic and property losses due to storm-induced gales, landslides and flash floods over the coastlines are becoming more frequent. Reliable subseasonal prediction of tropical cyclogenesis over the landlocked North Indian Ocean basin has extreme demand and requires accurate rendition of the crucial parameters that influence the storm development. While several genesis potential indices are used for climatological monitoring and prediction of cyclogenesis globally, their skill in subseasonal prediction of individual storm development is limited, especially near coastlines. This study reviews an improved genesis potential parameter, namely IGPP, that can detect cyclogenesis, evolution and storm tracks from post-processed Multi-model ensemble outputs. The IGPP is a revised version of Kotal Genesis Potential Parameter (KGPP) introduced by the India Meteorological Department for short and medium‐range operational cyclogenesis prediction over the North Indian Ocean. We analyzed and compared the cyclogenesis prediction systems when multiple storm systems of different intensities develop simultaneously. Results show that false alarms and overestimation of values present in KGPP are remarkably reduced by using IGPP for all the cases. Moreover, IGPP outperforms KGPP in distinguishing between developing and non-developing storms by accurately representing the cyclogenesis and intensity variations. The mean IGPP shows better correlation with maximum wind speeds of selected storms, with an improvement of almost 34 % compared to KGPP, which we attribute to the changes in thermodynamic and shear terms. The thermodynamic term is modified as the mean equivalent potential temperature of the surface and middle troposphere to include the effect of warm sea surface and tropospheric latent heat release whereas the vertical wind shear between 850 and 200 hPa levels is averaged over an annular region between 100 and 200 km radii from the storm centres and rescaled. IGPP has replaced KGPP operationally and is successfully implemented as one of the indices for the extended range probabilistic prediction of cyclogenesis by the India Meteorological Department. Probabilistic predictions using IGPP has been instrumental in providing early guidance on storm formation and weekly forecasts are available at https://www.tropmet.res.in/erpas/.

How to cite: Sudheesh, S. G., Sahai, A. K., Sukumarapillai, A., Joseph, S., and Beucler, T.: An Improved Genesis and Evolution Parameter for Subseasonal Prediction of the North Indian Ocean Tropical Cyclones, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6009, https://doi.org/10.5194/egusphere-egu22-6009, 2022.

Approximately, 25.6 tropical cyclones (TCs) occur in the western North Pacific (WNP) each year, of which 3.4 TCs affect the Korean Peninsula (KP). In 2019, a record of seven TCs affected the KP. We investigated and elucidated the favorable conditions influencing the TCs approaching the KP using the Weather Research and Forecasting model version 4.0 (WRFv4). The cold Maritime Continent-warm WNP sea surface temperature (SST) was found to be a major factor. The effect of the SST gradient was examined for one representative case using WRFv4 with varying SST anomalies. This study suggests that the SST gradient-induced change in the circulation over the Maritime Continent is a main factor causing the TCs to approach the KP.

How to cite: Moon, M. and Ha, K.-J.: Abnormal Activities of Tropical Cyclones in 2019 Over theKorean Peninsula, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6808, https://doi.org/10.5194/egusphere-egu22-6808, 2022.

Quantifiable assessment of how different physical processes promote tropical cyclone (TC) development is paramount in improving basic understanding of TC genesis and TC intensification forecasts. This assessment can be made via Eulerian budgets or by linearizing the equations of motion. For instance, the Sawyer-Eliassen equation gives the secondary circulation driven by a steady thermodynamic forcing. However, existing diagnostic frameworks often make implicit assumptions such as axisymmetry and temporally-averaged forcing, precluding discussions on how spatially heterogeneous or transient forcing may affect TC intensity. 

In this work, we combine principal component analysis with multiple linear regression to build a linear framework that predicts the evolution of three-dimensional wind fields at different forecast windows, based on current heating and wind conditions. We apply this model to ensembles of WRF simulations on Hurricane Maria (2017) and Typhoon Haiyan (2013). Uniquely, the simulations include cloud radiative feedback denial experiments, which enables us to quantify the extent to which radiative processes drive TC intensification. Given their simplicity, our models are reasonably accurate, with coefficients of determination exceeding 0.8 for forecast windows longer than six hours. The linear nature of our model allows us to cleanly decompose the contributions of different physical processes to three-dimensional TC kinematic changes. Using radiative heating as an example, preliminary results suggest that this heating creates outward-propagating diurnal variability in wind perturbations during critical intensification periods of Hurricane Maria. These wind perturbations resemble a shallow lower-tropospheric secondary circulation; implications of this circulation to TC intensification are explored. 

More generally, our framework can map thermodynamic forcing to kinematic changes without relying on axisymmetric assumptions, which opens the door to data-driven discovery of the leading physical pathways to TC intensification.

How to cite: Tam, F. I.-H., Beucler, T., and Ruppert, J.: A Data-Driven Approach to Isolate the Role of Radiative Heating in Tropical Cyclone Intensification, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6921, https://doi.org/10.5194/egusphere-egu22-6921, 2022.

Process-oriented diagnostics of tropical cyclones (TCs) facilitate a comparison of models to observations with respect to the physical processes relevant to TCs, informing which processes to target for model improvement. Here we use diagnostics based on the column-integrated moist static energy (MSE) variance budget, which focuses on how convection, moisture, clouds, and related processes are coupled. We use five different reanalysis datasets to provide an 'observation'-based reference against which high-resolution global climate models can be evaluated: ERA-Interim, MERRA-2, CFSR, ERA-5, and JRA-55. We calculate the budget in 10 x 10 degree boxes following tracked TCs composites over storm snapshots of the same intensity. The composites are qualitatively similar to prior work, with radiative feedbacks contributing most to MSE variance growth in the early stages of TC development and in weaker storms, and with surface flux feedbacks increasing strongly with intensity. Reanalyses that have a stronger radiative feedback, normalized by the box-mean MSE variance, in a given intensity bin exhibit a higher percentage of storms that intensify to the next bin, which emphasizes the value of the MSE variance budget as a process-oriented diagnostic for understanding model simulation of TCs. However, there is a large spread in MSE variance and the radiative and surface flux feedback contributions to MSE variance growth across reanalyses, even when considering composites over storms of the same intensity. The spread across reanalyses is comparable to the spread across the high-resolution climate models considered by Wing et al. (2019). This suggests that the data assimilation present in reanalyses does little to constrain the TC-MSE variance budget and indicates that caution must be taken when evaluating climate models against reanalysis. Ongoing work continues to evaluate climate model simulations, including those from the HighResMIP ensemble, against the reanalysis-based reference, and examine the large-scale environments associated with TC formation in reanalyses.

How to cite: Wing, A., Dirkes, C., Camargo, S., Kim, D., and Moon, Y.: Process-oriented diagnosis of tropical cyclones based on the moist static energy variance budget in reanalyses and high-resolution climate models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9805, https://doi.org/10.5194/egusphere-egu22-9805, 2022.

Devastating Japan in October 2019, Supertyphoon (STY) Hagibis was an important typhoon in the history of the Pacific. A striking feature of Hagibis was its explosive RI (rapid intensification). In 24 h, Hagibis intensified by 100 kt, making it one of the fastest-intensifying typhoons ever observed. After RI, Hagibis’s intensification stalled. Using the current typhoon intensity record holder, i.e., STY Haiyan (2013), as a benchmark, this work explores the intensity evolution differences of these 2 high-impact STYs.

We found that the extremely high pre-storm sea surface temperature reaching 30.5∘C, deep/warm pre-storm ocean heat content reaching 160 kJ cm^-2, fast forward storm motion of ~8 m s^-1, small during-storm ocean cooling effect of ~ 0.5∘C, significant thunderstorm activity at its center, and rapid eyewall contraction were all important contributors to Hagibis’s impressive intensification. There was 36% more air-sea flux for Hagibis’s RI than for Haiyan’s.

After its spectacular RI, Hagibis’s intensification stopped, despite favorable environments. Haiyan, by contrast, continued to intensify, reaching its record-breaking intensity of 170 kt. A key finding here is the multiple pathways that storm size affected the intensity evolution for both typhoons. After RI, Hagibis experienced a major size expansion, becoming the largest typhoon on record in the Pacific. This size enlargement, combined with a reduction in storm translational speed, induced stronger ocean cooling that reduced ocean flux and hindered intensification. The large storm size also contributed to slower eyewall replacement cycles (ERCs), which prolonged the negative impact of the ERC on intensification.

How to cite: Lin, I., Rogers, R. F., Huang, H.-C., Liao, Y.-C., Herndon, D., Yu, J.-Y., Chang, Y.-T., Zhang, J. A., Patricola, C. M., Pun, I.-F., and Lien, C.-C.: Ocean Interaction and the Intensity Evolution of Two High-Impact Super Typhoons: Hagibis (2019) and Haiyan (2013), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1254, https://doi.org/10.5194/egusphere-egu22-1254, 2022.

The Madden-Julian Oscillation (MJO) can create favorable conditions for tropical cyclone (TC) genesis in the Indian Ocean, but past work has not thoroughly investigated how TCs after genesis may influence MJO convection development. This work utilizes long-term composite analysis to broadly establish the relationship between Indian Ocean TCs and each MJO phase over the Indian basin, and then seeks to isolate direct impacts of the TCs on MJO convection coverage and intensity.

We first examine Indian Ocean TC interactions with MJO convection using daily-mean ERA5 reanalysis and TRMM precipitation products from 1998-2018 for TC and non-TC days per MJO phase, excluding 3 days before and after Best-Track TC lifespans to reduce contamination of non-TC composites. Preliminary analysis suggests that TC periods are associated with stronger MJOs, with an anomalously stronger MJO large-scale circulation and associated subsidence over the equatorial Indian Ocean. We find higher CAPE and increased TRMM rainfall during convectively-active MJO phases over the eastern Indian Ocean when TCs are present, but increased dry-air advection, greater CIN, and decreased TRMM rainfall over the western Indian Ocean during the same phases. These findings allude to suppression of MJO convection development during TC periods in the western MJO convective envelope, with coincident enhancement of MJO convection in the eastern MJO convective envelope. While these broad conclusions are consistent during non-convectively-active MJO phases, changing MJO strength during TC periods for convectively-active MJO phases limit our ability to quantify TC impacts on MJO convection using only composite analysis.

To better quantify TC influences, we next isolate direct TC impacts on MJO convection using metrics for the TC range of influence, likelihood of interaction with MJO convection, and strength of TC-MJO convection interactions. Since a TC’s influence likely extends beyond the 34-kt wind radii provided by Best-Track, we determine an “outer wind radius” by integrating a radial wind model outward from each TC eye to ~5 m/s. We next quantify the overlapping area between each TC outer wind radius and the coincident MJO convection by using the MJO precipitation boundary determined from a large-scale precipitation tracking dataset, with the size of the overlapping area providing the likelihood of interaction. For time periods when TC outer wind radii and MJO convection overlap, we compare the convection coverage and intensity observed by TRMM between MJO convection sectors with and without TC wind overlap. The strength of a TC-MJO convection interaction is finally quantified by comparing the convection coverage and intensity between these sectors. With climatological statistics on the likelihood and strength of TC-MJO interactions, future MJO prediction and Maritime Continent rainfall forecasts could be adjusted according to the presence or absence of Indian Ocean TCs.

How to cite: Thayer, J. and Hence, D.: Tropical Cyclone Interactions with Madden-Julian Oscillation Convection in the Indian Ocean, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2080, https://doi.org/10.5194/egusphere-egu22-2080, 2022.

Forecasting the Madden-Julian Oscillation (MJO) is challenging for many numerical weather prediction (NWP) systems and climate models. Models tend to simulate slower MJO propagation than in observations, impacting other weather and climate patterns across the world through its teleconnections. Observations show that sea surface temperatures (SST), and subsequently sea surface fluxes influence MJO convection in the tropics. Coupled ocean-atmosphere models, which dynamically predict SST, tend to perform better in forecasting the MJO than atmosphere-only models that use persisted SST. Lower resolution coupled climate models are routinely used by forecasting centres, however, there are only a few operational weather forecasts utilising high resolution coupled NWP systems. The Met Office has developed a coupled NWP system running in near real-time since May 2016, alongside their operational, atmosphere-only NWP system. Comparison between the models using the Real-time Multivariate MJO index reveals that both are similarly skillful within 7 and 10 forecast days for operational and coupled models, respectively. The coupled model produces faster MJO propagation than the operational model. Consistent with this faster propagation, coupled model forecasts initiated during active MJO convection over the Indian Ocean (RMM phase 1), show enhanced convection by lead day 7 in the Sulawesi-Banda Sea region located ahead (to the east) of the convective envelope. Warm SST anomalies of order 0.1°C in that region are simulated in the coupled model composites from lead day 1, consistent with observations. When the coupled model is initialised with active MJO convection over the Maritime Continent (RMM phase 4), the model suppresses convection faster in the equatorial Indian Ocean region, which is behind (to the west) of the MJO convection. Cold SST anomalies are created in the coupled model from lead day 1 in that region, stronger than observations suggest and leading to excessive suppression of convection in the coupled model here. We explain the differences between the coupled and atmosphere-only simulations through a combination of upwelling diagnostics, analysis of moisture budget terms and targeted numerical experiments for SST-convection feedback.

How to cite: Karlowska, E., Matthews, A., Webber, B., Graham, T., and Xavier, P.: Sea surface temperature impact on Madden-Julian Oscillation convection in the Met Office coupled and atmosphere-only forecast models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-271, https://doi.org/10.5194/egusphere-egu22-271, 2022.

The Madden-Julian oscillation (MJO) events can be categorized into four types based on their propagation characteristics: standing, jumping, slow-propagating, and fast-propagating types. While the characteristics of each MJO type have been documented in the literature, it remains unknown whether such diversity is realistically represented in the state-of-art climate models. This study evaluates the MJO diversity in 28 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. We find that many CMIP6 models reasonably reproduce the MJO diversity although the relative frequency of propagating types tends to be underestimated. When individual models are grouped into the GOOD and POOR models by considering the performance in capturing propagation pattern of the four MJO types, the GOOD models show a much stronger relationship between MJO type and underlying sea surface temperature (SST) anomalies, especially for standing and fast-propagating types. We find a systematic difference in the model biases in the climatological mean SST and column water vapor between the GOOD and POOR models, with the POOR models exhibiting much stronger cold and dry biases over the equatorial western Pacific. Our results suggest that the MJO diversity can be improved by reducing model mean bias.

How to cite: Back, S.-Y., Kim, D., and Son, S.-W.: MJO diversity in CMIP6 models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11037, https://doi.org/10.5194/egusphere-egu22-11037, 2022.