During the northeast monsoon season, the Maritime Continent (MC) experiences synoptic cold surges that play a major role in extreme precipitation events and possibly aid the MJO in propagating across the MC. We evaluate the ability of CMIP6 GCMs to simulate the cold surge using a diagnostic based on sea level pressure and 850 hPa winds. Comparing output from CMIP6 and AMIP6 GCMs with multiple reanalysis and observations, we find that while the models are able to capture broad features of the surge winds, such as the counterclockwise turning at the equator (around the Karimata strait), they exhibit the following biases in cold surge precipitation: an equatorial wet component linked to SST bias (from the ocean model) and a dry bias aligned with the surge winds (from the atmospheric model; unrelated to SST bias). Although signatures of the aforementioned biases are present during both surge and non-surge days, they are amplified during surge days. These biases are hypothesized to be related to temperature bias over the Tibetan Plateau.

The Maritime Continent-Southeast Asia (MCSEA) region is home to about 675 million people (~8.6% of the world’s population) and is directly influenced by the Asian-Australian monsoon system. This makes understanding the range of projected changes to wet and dry season rainfall crucial for adaptation planning in areas such as agriculture and water resources management. A recent assessment noted that CMIP6 models show improvement over the CMIP5 ensemble in capturing historical global monsoon domain patterns and intensity. They also project a higher end-of-century increase in annual-mean monsoonal rains over global land (relative to 1995-2014) that is around 50% higher than in CMIP5. However, systematic regional biases and large inter-model spread in the projections do remain. To better understand the uncertainties around the ensemble-mean changes, we examine the simulated historical behaviour and future rainfall changes for the MCSEA region under the SSP585 scenario from the perspective of “hot” and “cool” CMIP6 models (15 in each set). An effective climate sensitivity (ECS) value higher or lower than 3.8C is used to define a “hot” or “cool” model, respectively. While both sets of models exhibit similar patterns of mean rainfall change in each season for the mid-century period (2041-2060), the contrasts in corresponding wet and dry signals between the two sets become more pronounced over land and ocean in some seasons by the end of the century (2081-2100). We also compare the projected changes in variability and seasonal extremes in accumulation and assess whether the relative differences seen between the two model sets are related to either the thermodynamic or circulation changes within them.

Mobile monitoring of CO2 is increasingly conducted to estimate high spatial resolution distribution. The longest mobile monitoring travels of CO2 on highways which are over 230 km crossing South Korea from urban to rural areas were performed in October 16, 17, and 26, 2020. Only data when vehicle speed of mobile platform is over 10 km/h were used. Because when mobile platform is driving in tunnels, monitored CO2 levels are enhanced more than twice the average of entire travel, the data were not used. The results of changes in monitored CO2 levels according to longitude which is divided into 20 from urban to rural areas showed that all routes had statistically significant declines. The averaged reduction rate of routes was -0.033 ppm/km, meaning that CO2 levels decreased 0.33 ppm when we move 10 km to rural areas. And the changes in monitored CO2 levels according to time were also analyzed. Because the difference of monitoring times of each route reflects difference of PBL height, having an impact on CO2 levels, the effect of PBL was corrected by subtracting the mobile monitored CO2 and the CO2 measured from high building in Seoul. Moving from urban to rural areas, the averaged ratio of change in CO2 levels over time was -0.42 ppm/min. Monitored CO2 was compared with ODIAC and NDVI in 2020 which means anthropogenic emissions and vegetation index, respectively. Our monitored CO2 level data showed positive relationships with ODIAC (2.72 tC/cell/ppm) and negative with NDVI (-0.002 units/ppm). The results suggest information on the spatial extent of the impact of urban CO2 dome on Korea. This study was supported by the National Research Foundation of Korea (NRF) grant funded by the Government of Korea (NRF-2019R1A2C3002868) and the Creative-Pioneering Researchers Program through Seoul National University (SNU). 

Based on the concentrations of CO₂, PM_2.5 and PM_1.0, and conventional meteorological observation data from 2016 to 2018 at Taiyuan station, which belongs to the Shanxi greenhouse gas observation network, the CO₂ concentration monthly and daily distribution characteristics, the weekend effect, and the variation characteristics on haze days and non-haze days, are analyzed. By using the Hybrid Single-Particle Lagrangian Integrated Trajectory model (backward trajectory model) and surface wind data, the transmission characteristics of atmospheric CO₂ in Taiyuan are studied in various seasons. The results show that, in Taiyuan, the CO₂ mole fraction in autumn and winter is higher than that in spring and summer, and on haze days is higher than that on non-haze days. The diurnal variation characteristic of CO₂ mole fraction in each season is ‘single peak and single valley’ with the peak value around 0700 (hereafter refers to Beijing Time) and the valley value around 1600. The CO₂ mole fraction on workdays is slightly higher than that on non-workdays and obviously different around 0800 of the early peak. Horizontal diffusion can reduce the CO₂ mole fraction, while breezy weather is not beneficial to CO₂ diffusion. The wind direction and speed in the upper levels are different from those near the surface, and the close air masses in the southwest–west–northwest sector raise the CO₂ concentration in Taiyuan obviously. This indicates that the CO₂ in Taiyuan is mainly contributed by local sources. 

Excepted of carbon emission from fossil fuel with in a city, the carbon released from human and animals through digestion and decomposition is also a part of the carbon cycle of urban system, which was often neglected due to its small magnitude compared with fossil fuel. At present, the human and animal respiration is mostly included in some analysis of local urban system, but it was rarely been seen in studies on a global scale. This study estimated the carbon released from human and livestock respiration in global and within large cities, and then, we compare it with carbon emission from fossil fuel within 14 reported world cities. The results showed that the annual mean carbon from human respiration was about 0.586 Gt C during 1995-2015, and that from livestock was about 0.806 Gt C in 2010, among which cattle release 60.19% of the total animal carbon, followed by sheep and buffalos. In terms of cities, Shanghai and Beijing have the highest carbon emissions from human, followed by Delhi. In addition, the value of carbon released from animal respiration have huge differences between. What’s more, Sao Paulo has the highest ratio of respiration of live creatures, while Delhi and Beijing have the highest ratio of animal respiration. Within all the studied cities, the total proportion of human and livestock respiration is less than 20%. The results suggested that, considering the food and feedstuff demands in some regions, the heterotrophic respiration have huge impacts on the carbon cycle in city areas.

Carbonyl Sulfide (OCS) is an analogue of Carbon Dioxide (CO2). In the Process of plant photosynthesis, taken up by hydrolysis, OCS is catalyzed by enzyme Carbonic Anhydrase (CA). However, being different from CO2, OCS will not be released by plant respiration. In recent studies, OCS is proportionally to CO2 when fixed by plant photosynthesis. OCS flux have been employed as an indicator of terrestrial ecosystem GPP. On the leaf level, BEPS model integrates the processed-based Farquhar model, and simulated the instantaneous photosynthesis. In this process, carbon fixation is determined by intercellular CO2 concentration, the maximum carboxylation rate (Vcmax), and the maximum electron transfer rate (Jmax). Furthermore, Vcmax is influenced by stomata conductance of CO2. By integrating the physiologically driven stomatal model, as well as the layered canopy conductance, this study simulates the conductance of both leaf and canopy level, also the flux of H2O. And then, conductance of OCS was simulated based on conductance and resistance, which was regulated by environment factors, and enzymes. Further, the OCS concentration of both leaf boundary layers and intercellular will be employed to simulate the OCS flux of plant photosynthesis. In this study, stomatal behaviors parameters in Ball-Berry model will give an access for OCS simulation. The further developmental BEPS model is employed to simulate both CO2 and OCS fluxes. Finally, based on the canopy gap and the sunlit and the shaded leaves, this study upscales from the leaf to the canopy level. Based on the InTec model, the timestep is transfered from instantaneous to 30 min, and also 24 h. The simulated OCS and CO2 fluxes was aiming to improve the simulation accuracy of GPP. 

Methane (CH₄) is a potent greenhouse gas which is largely derived by anthropogenic activities. China’s agricultural and energy production activities significantly contribute to the global anthropogenic CH₄ emissions through rice cultivation, livestock feeding and coal production. Understanding the spatial-temporal changes of China’s CH₄ emissions is a prerequisite for interpreting source contributions and for policy making of emissions reductions. However, the unavailability and scarcity of data from some emissions sources or years, and lack of fine gridded data make the estimate of CH₄ emissions a great challenge. This study provides a comprehensive comparison and evaluation of China’s anthropogenic CH₄ emissions by synthesizing the most publicly available datasets. The results show that there are large differences in both total emissions and spatial distributions, with values ranging from 44.4-57.5 Tg CH₄ yr^-1 in 2010. Temporally, emissions were stabilized in the 1990s, but increased significantly to 2010, with annual average growth rates (AAGRs) of 2.6-4.0% during 2000-2010, and slowed down again during 2011-2015. We further estimated the total emissions during 2015-2019 using IPCC tier one method, and the AAGRs were 0.3-0.8% because of the stable emissions from energy sector. Spatially, the emissions hotspots distributions among inventories are reasonably consistent, with more works need to be advanced in dynamic coal, rice and livestock spatial distribution. To sum it up, the availability of detailed activity data for sectors or subsectors and the use of region-specific emission factors play important roles in understanding source contributions, and reducing the uncertainty of bottom-up inventories.

The outbreak of coronavirus disease 2019 (COVID-19) significantly reduced economic, social and industrial activities, with indicators of gross domestic product (GDP), power consumption, and transport flows being influenced obviously, and thus decreased the fossil-related CO₂ emissions in China. Due to a 2-3 years’ time delays in statistics, traditional inventories generally lag real time several years and thus not suitable for such assessment. However, a timely assessment of impact from COVID-19 on provincial CO₂ emission reductions is crucial for both understanding the reduction and its implications for mitigation. Here, we used national and provincial GDP, traffic statistic data and an inventory to estimate the reductions in the first quarter (Q1) of 2020. A reduction of -11.0% was found in 2020 Q1, with secondary industry contributed 72.5%. At the province level, Hubei reduced by 44% and contributed the most to national total reductions. Moreover, GDP combined with traffic statistical data is found to have potential merit for estimating emissions changes when detailed energy activity data are unavailable. One implication for mitigation is changing people’s working style and recommending working from home, and holding virtual conferences to reduce emissions.

Accurate prediction of extreme weather and climate is crucial due to their devastating impacts on our society and environment. Here, predictability of frequency of summer (June-August) extreme hot days (SEHDs) in China has been assessed in the SINTEX-F2 seasonal forecast system during the period of 1983-2015. The hindcast had 12 ensemble members and was initialized on the first day of March, April and May, respectively. Results show that overall, the SINTEX-F2 predicts more regions with good prediction skills at shorter lead time due to its better capture of the linear trends. Whereas, in Southwest China and eastern Tibetan Plateau, the prediction skills are consistently increased at shorter lead time even without the impacts of linear trends; the correlation coefficients between the region-mean anomalies in the observed and predicted SEHD frequencies are 0.74, 0.68 and 0.61 at 1-3 month lead, respectively, and they remain as high as 0.56, 0.49 and 0.40 when the linear trends are removed. This is because the SINTEX-F2 can reproduce the observed influences of Indian Ocean Basin Mode (IOBM) on the SEHD frequency. The warm IOBM can cause anomalous ascending and strong divergence in the upper troposphere over the tropical Indian Ocean and Indian subcontinent. The strong divergence causes direct convergence and descending over Southwestern China and eastern Tibetan Plateau. This Predictability of SEHDs in Southwest China and eastern Tibetan Plateau doesn't depend on the choice of model. If they can reproduce the observed SEHD-IOBM relationship, they should be able to provide skillful prediction as well.

The typhoon's motion near Taiwan is affected by topographically phase-locked convection. This effect can cause the duration time longer and increase the accumulated rainfall in Taiwan. The National Science and Technology Center for Disaster Reduction (NCDR) uses HiRAM model to analyze the difference for TCs between present and future. The tracks found by dynamic downscaling HiRAM using 5km WRF simulations are sorted into different track types (north, central, south) depending on where they made landfall. Compare the vary in three types under climate change, besides changing for intensity, the moving of typhoons is sped up caused the duration time is decreased. Because of the change in the translation speed of Typhoons in a future climate scenario, it is imperative to explore the impacts of climate change on such TCs.To solve the mechanism for TCs moving, we use the PV tendency to realize the contribution in horizontal advection (HA), vertical advection (VA), and diabatic heating (DH). The DH term is an important role in the effect of terrain. Furthermore, we design the sensitivity test to change Taiwan's terrain. The precipitation distribution is changed then the diabatic heating change by the precipitation. Finally, use pseudo-global warming to simulation the same TCS in the future. Compare these two simulation, not only the structure is increase but also the mean contribution for PV tendency is changed.

The Weather Research and Forecasting (WRF)-Double-Moment 6-class (WDM6) microphysics parameterization treats the warm-phase hydrometeors as double-moment approach. Therefore, the number concentrations of cloud droplets and raindrops are predicted together with their mixing ratios. The ice microphysics process in WDM6 follows the study of Hong et al (2004), in which the number concentration of ice crystal is diagnosed based on its mixing ratio. Previous study showed that the ice crystal property in WDM6 can cause the substantial generation of ice crystal mass, which leads to an overestimation of surface precipitation. This study examines the effect of prognostic number concentration for ice crystal on the simulated microphysical processes and precipitation. The new version of WDM6, including the double-moment approach for ice crystal, has been tested in the idealized squall line and winter-time snowfall cases. Simulation results show that a significant decrease in ice crystal amount leads to an increase of snow. In addition, ice crystal and snow are formed higher altitude in the new WDM6, compared to the ones in the original WDM6. Revised ice microphysics processes such as ice nucleation and depositional growth are responsible for the changes in the vertical profile of hydrometeors mixing ratio. The new WDM6 alleviates the overestimation of surface precipitation due to the reduced ice mixing ratio. More detailed results will be presented in the conference.* This work was supported by the Office of Science User Facility and the National Research Foundation of Korea (NRF) Grant 2019R1C1C1008482 funded by the South Korean government (MSIT).

The study focuses on exploring the microphysical processes involved the evolution of Bright Band (BB) and its rain microphysics in three different study areas situated in a coastal (20 m above MSL namely Trivandrum), mid altitude (400m above MSL namely Braemore) and high-altitude (1820 m above MSL namely HACPO) cloud physics observatories in Western Ghats. The analysis is carried out primarily using the radar reflectivity profiles from Micro rain radar for the period 1^st June to 30^th September 2019. All three locations registered a total 13, 58 and 48 BB cases wherein total of 79 cm, 172 cm, and 405.98 cm rainfall is associated with BB formation. It has been noted that, higher percentage of rainfall is received prior to the BB (non-BB precipitation) of 2.7%, 14 % and 10 % from their respective observatories towards the total rainfall. Rain Drop Size Distributions (DSD) and gamma parameters in different rain classes (0.1 ≤ R < 1 mmhr ⁻¹; 1 ≤ R < 5 mmhr ⁻¹; 5 ≤ R < 10 mmhr ⁻¹; R ≥ 10 mmhr ⁻¹) has been analyses further wherein mid drop size distribution plays an important role in the collision-coalescence process in the Western Ghats region. Further, the coefficient and exponent values in radar reflectivity-rain rate (Z-R) relations signifies, microphysical variation before and during the rainfall specially in the high-altitude location of Western Ghats.

The relationship between the Arctic Oscillation (AO) and surface air temperature (SAT) over Korea is re-examined using the long-term observation and reanalysis datasets for the period of December 1958 to February 2020. Over the entire period, Korean SAT is positively correlated with the AO index with a statistically significant correlation coefficient, greater than 0.4, only in the boreal winter. It is found that this correlation is not static but changes on the decadal time scale. While the 15-year moving correlations are as high as 0.6 in 1980s and 1990s, they are smaller than 0.3 in the other decades. It is revealed that this decadal variation is partly due to the AO structure change over the North Pacific. In the period of 1980s-1990s, the AO-related sea level pressure fluctuation is strong and well defined over the western North Pacific and the related temperature advection effectively changes the winter SAT over Korea. In the other periods, the AO-related circulation anomaly is either weak or mostly confined within the central North Pacific. This result suggests that Korean SAT-AO index relationship, which becomes insignificant in recent decades is highly dependent on mean flow change in the North Pacific.

Drought frequently impacts both the agricultural productivity and the wellbeing of farming communities in drought-prone areas of Australia, including the largest agricultural region in the country - the Murray Darling Basin (MDB). Improving drought preparedness of farming communities in the MDB could be achieved by building capacity for a user-centred Integrated Early Warning System (I-EWS) for drought. In this study, a prospective I-EWS was investigated. Farming individuals were interviewed to inductively guide the selection of drought-related indices most appropriate for the study area. Based on interview results and desktop research, five drought-related indices directly relevant to the MDB were selected as inputs to the trigger levels for an I-EWS: the Standardised Precipitation Index, the Vegetation Health Index, Soil Moisture (percent of normal), the likelihood of exceeding median rainfall in a coming month and the chance of El Niño. Based on these inputs, decision rules were formulated for a staged “WATCH”, “ALERT” and “DECLARATION” drought early warnings. These decision rules were tested for the intense dry period from 2017 to 2019 for five key agricultural Local Government Areas in the Northern MDB. It was found that all three stages of the drought I-EWS were adequately triggered indicating that a warning lead time of 3-8 months could have been possible in the dry period. Data for the selected inputs are readily obtained from space-based products as well as national meteorological services and would be applicable to regions with limited observing networks or forecast capability. Thus, whilst the methodologies developed in this study and the resultant outcomes are tailored to the Northern MDB, this research ultimately serves as both a successful proof of concept for the drought EWS as well as a foundational base for the design of an operational user-centred I-EWS in susceptible to drought regions of Australia and other countries.

This study evaluates the U.S. National Oceanographic and Atmospheric Administration's (NOAA) Climate Prediction Center morphing technique (CMORPH) and the Japan Aerospace Exploration Agency's (JAXA) Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates over Australia across an 18 year period from 2001 to 2018. The evaluation was performed on a monthly time scale and used both point and gridded rain gauge data as the reference dataset. Overall statistics demonstrated that satellite precipitation estimates did exhibit skill over Australia and that gauge-blending yielded a notable increase in performance. Dependencies of performance on geography, season, and rainfall intensity were also investigated. The skill of satellite precipitation detection was reduced in areas of elevated topography and where cold frontal rainfall was the main precipitation source. Areas where rain gauge coverage was sparse also exhibited reduced skill. In terms of seasons, the performance was relatively similar across the year, with austral summer (DJF) exhibiting slightly better performance. The skill of the satellite precipitation estimates was highly dependent on rainfall intensity. The highest skill was obtained for moderate rainfall amounts (2–4 mm/day). There was an overestimation of low-end rainfall amounts and an underestimation in both the frequency and amount for high-end rainfall. Overall, CMORPH and GSMaP datasets were evaluated as useful sources of satellite precipitation estimates over Australia.

The Northern Murray-Darling Basin (MDB) is a key Australian agricultural region which requires efficient Agricultural Drought Management (ADM). Although a need for resilient ADM in local farming communities has long been recognised, previous studies on ADM assessments in the Northern MDB did not consider two key elements of resilient management: proactivity and suitability. This study assessed the current ADM Strategy (ADMS) implemented within each of five selected Northern MDB Local Government Areas (LGAs) (Paroo Shire, Balonne Shire, Murweh Shire, Maranoa Region, and Goondiwindi Region), specifically investigating the extent of proactivity, effectiveness, and suitability of ADMSs. To evaluate drought risk extent in each LGA, a region-specific drought risk index was developed, and drought risk mapping was conducted. All LGAs displayed very high levels of drought risk due to hazardous climatic conditions, vulnerable socio-economic attributes, and drought-exposed geographical features. A Criteria Based Ranking (CBR) survey was completed to produce a quantitative effectiveness and proactivity rank for each of the major ADMSs used in the Northern MDB. Results showed that Government Assistance was the most proactive and effective ADMS. Effectiveness ranks of the major ADMSs used and drought risk extent found in each LGA were correlated to determine ADM suitability. Overall, Balonne Shire and the Goondiwindi Region were identified as high priority areas requiring improved ADM. A user-centred Integrated Early Warning System for drought has the potential to increase the proactivity and suitability of ADM in such areas, strengthening drought preparedness and resilience of farming communities in the Northern MDB.

Climate is rapidly changing on a global scale, and climate-related impacts which are increasing in frequency and severity are already negatively affecting ecosystems and society. More often than not there is lots of news and information around the impacts tropical cyclones (TCs) have on population, infrastructure, and economy. This study expands perspective to how TCs affect the natural environment, specifically coastal marine areas. In the tropics, climate change increases the risk of extensive degradation of coral reef ecosystems, and it is also predicted that climate change will result in an increase in occurrences of more intense TCs. Using case studies for two countries in Oceania - Australia and Vanuatu – this study investigates how fisheries, tourism, and other sectors can be better prepared for the severe damage TCs inflict upon coral reefs. Using satellite remote sensing and GIS for risk mapping as well as exploring existing management of coastal marine areas, this study conducts an analysis of and provides recommendations for managing the ever-growing impacts TCs have on the natural environment which in turn affects coastal communities. 

Globally, the trend in recognition for drought risk management rather than drought crisis management has grown in recent years. Successful responses to drought have been found to involve proactive action in non-drought periods to build response capability. A tool used in proactive management is the Early Warning System (EWS). EWSs have the potential to prevent loss of life and reduce impacts of natural hazards such as drought on populations, economy and natural environment. However, some EWSs have certain limitations which reduce their efficiency including unintegrated data inputs, regional scale outputs, low community participation and ineffective communication of risk and associated preparation strategies. End users of EWSs would benefit from the advancement of localised risk knowledge and the improvement of communication and dissemination of risks and proactive action.In this study, demographic (age and gender) and sector (agriculture and local government) specific communication requirements were identified for the case study region in Australia. This study identified the demographic structure, analysed current communication strategies and limitations, and conducted focus groups/interviews of farmers for the case study region. This study has also developed drought Adaptive Capacity (AC) maps at a Local Government Area (LGA) scale for the case study region using a public consultation process to develop an AC index. AC maps can enhance localised risk knowledge in EWSs and support drought management resource allocation. 

Levoglucosan has been widely used to quantitatively assess biomass burning’s contribution to ambient aerosols, but previous such assessments have not accounted for levoglucosan’s degradation in the atmosphere. We develop the first global simulation of atmospheric levoglucosan, explicitly accounting for its chemical degradation, to evaluate the impacts on levoglucosan’s use in quantitative aerosol source apportionment. Levoglucosan is emitted into the atmosphere from the burning of plant matter in open fires (1.7 Tg yr^-1) and as biofuels (2.1 Tg yr^-1). Sinks of atmospheric levoglucosan include aqueous-phase oxidation (2.9 Tg yr^-1), heterogeneous oxidation (0.16 Tg yr^-1), gas-phase oxidation (1.4×10^-4 Tg yr^-1), and dry and wet deposition (0.27 and 0.43 Tg yr ^-1). The global atmospheric burden of levoglucosan is 19 Gg with a lifetime of 1.8 days. Observations show a sharp decline in levoglucosan’s concentrations and its relative abundance to organic carbon aerosol (OC) and particulate K⁺ from near-source to remote sites. We show that such features can only be reproduced when levoglucosan’s chemical degradation is included in the model. Using model results, we develop statistical parameterizations to account for the atmospheric degradation in levoglucosan measurements, improving their use for quantitative aerosol source apportionment.

Air pollution continues to be a major environmental and human health concern. While the burden of air pollution is greatest is Africa, Asia and the Middle East (WHO, 2016), regions with poor air quality are often understudied. Volatile organic compounds (VOCs) are a component of air pollution that leads to secondary pollutants including secondary organic aerosol (SOA) and ozone (O₃). Identifying the major reactive VOCs in a city is a key step towards guiding effective pollution control strategies. Here we present VOC characteristics, sources and reactivity in diverse cities in Asia, including less studied countries such as Saudi Arabia (Mecca, Medina, Jeddah), Pakistan (Lahore) and Nepal (Kathmandu), and more studied regions such as South Korea (Seoul) and PRC (Hong Kong). The VOC signatures were characterized based on VOC concentrations and VOC ratios (e.g., toluene/benzene, ethene/ethyne, i-pentane/n-pentane) during ground-based measurement campaigns from 2012-2018. Each city was found to have its own distinct VOC signature that reflects its major emission sources (e.g., traffic, industry, solvents, waste burning, biogenic). As examples, the combustion tracers ethyne and ethene were abundant in Lahore and Kathmandu, the gasoline evaporation tracer i-pentane in Mecca, the LPG tracers n-butane and i-butane in Hong Kong, and the solvent tracer toluene in Seoul. Ozone formation potential was strongly impacted by reactive alkenes in Mecca and Lahore, versus aromatics in Seoul. Because VOC abundances can evolve over time in response to emissions control strategies or other reasons, their evolution requires careful monitoring.

Light absorption and radiative forcing of black carbon (BC) is influenced by both BC itself and its interactions with other aerosol chemical compositions. Although the changes in BC concentrations in response to emission reduction measures have been well documented, the influence of emission reductions on the light absorption properties of BC and its influence on BC-boundary-layer interactions has been less explored. We examined how emission control measures during APEC affect the mixing state/light absorption of BC, and the associated implications for BC-PBL interactions. We found that both the mass concentration of BC and the BC coating materials declined during the APEC week, which reduced the light absorption and light absorption enhancement of BC. The reduced absorption aerosol optical depth (AAOD) during APEC were caused by both the declines in mass concentration of BC itself (52.0%), and the lensing effect of BC (48.0%). The reductions in coating materials (39.4%) dominated the influence of lensing effect, and the reduced light absorption capability contributed 3.2% to the total reductions in AAOD. Reduced light absorption of BC due to emission control during APEC enhanced planetary boundary layer height (PBLH) by 8.2 m. Different responses of PM_2.5 and O₃ were found to the changes in light absorption of BC. Reduced light absorption of BC due to emission reductions decreased near surface PM_2.5 concentrations but enhanced near surface O₃ concentrations in the North China Plain. These results suggest that current measures to control SO₂, NO_x, etc. would be efficient to reduce the absorption enhancement of BC, and to inhibit the feedback of BC on boundary layer. Yet enhanced ground O₃ might be a side effect of current emission control strategies. How to control emissions to offset this side effect of current emission control measures on O₃ should be an area of further focus. 

Organic aerosol (OA) is a major component of tropospheric submicron aerosol that can contribute to air pollution and cause adverse effects on human health. Chemical transport models however hardly reproduce the variability of OA concentrations in polluted areas, hindering the accurate quantitation of the impact of OA and the development of efficient control strategies. Herein, we improved both process-based (“complex SOA”) and observation-constrained SOA schemes (“simple SOA scheme”) in GEOS-Chem, include updating the emission, volatility distribution, and chemical processes of semivolatile and intermediate volatility organic compounds (S/IVOCs) as well as adding additional sources of nitrous acid. The simulations were evaluated against the online measurements in China as well as their positive matrix factorization results. The result shows that the simple SOA scheme can generally capture the magnitude as well as seasonal and spatial variations of OA and its components, which may serve as a reference for the development of the complex SOA scheme. The model performance of the complex scheme for OA is also improved after implementing the modifications. The underestimation of POA is significant reduced due to the lower volatility of SVOCs in the new volatility distribution estimate. The improved complex SOA scheme also shows reduced model-observation discrepancy of SOA and reaches a similar seasonal and spatial distribution to the simple SOA scheme, which can identify the OA sources for future control. S/IVOCs is the largest contributor to OA in China with the contribution over 60%. SVOCs contribute more as POA in winter and as SOA in summer. IVOCs contribute more in winter and in northern China due to the large emission from the heating processes in rural, which is highly uncertain and needs more constraints in future model developments.

Tropospheric ozone (O₃) is a critical phytotoxic pollutant. It adversely affects plants' vegetative & reproductive stages, which can lead to yield reduction. Field studies have reported that the ozone could cause 5-10% yield loss and deterioration in crop quality, which could be more in the future (Mittal et al., 2007). India is the second-most populous country globally, and agriculture contributes a large fraction to its economy.   The impact of elevated ozone on horticulture crops is a sparsely studied topic in India. Therefore, in the present study, we have tried to study the effects of elevated ozone on the growth and fruiting of tomatoes. For understanding the impact of ozone on tomato crops, two cultivars of tomato (Lycopersicon esculentum) Navodaya, and PKM-1 were selected for the experiment. The study was conducted (November 2019 to March 2020) at the Free Air Ozone Enrichment Facility (FAOEF) established at CSIR-National Botanical Research Institute (NBRI), Lucknow, India. The 15 pots of each cultivar were placed in all the 3 rings (1 for ambient (control) and 2 for elevated ozone (+20ppb) of FAOEF). During the experiment, various parameters of cultivars, like morphological, biochemical & Physiological changes, were studied. Both cultivars showed a decrease in the photosynthesis process under the elevated ozone concentrations. The high stomatal conductance of Navodaya was observed under the elevated ozone concentrations, while PKM-1 showed a fall in conductance under elevated ozone concentrations compared to ambient concentrations. A significant alteration was recorded in both cultivars' physiological, biochemical, and morphological parameters during the growth process under elevated ozone conditions. Both cultivars under elevated ozone conditions showed early maturation that might be leading to earlier senescence processes.

Urbanization has been occurring at an extreme rate in Asia, since the beginning of the 21^st Century that have impacted air pollution – threat to human health in mega urban areas and beyond.  From satellite scatterometer data processed by the Dense Sampling Method (DSM) with the Rosette Transform, urbanization is monitored in four dimensions (4D) including lateral expansion (2D), vertical build-up (1D), and decadal change (1D).  DSM results have been validated with light detection and ranging (lidar) data over many cities. DSM is found to be robust and is useful in determining building volume density of cities with different sizes, shapes, built-up patterns, and different socio-economic conditions in multiple countries under diverse geophysical and climatic conditions.  DSM provides a breakthrough overcoming shortfalls in analyses restricted to 2D.  DSM results reveal the extreme urbanization in Asian cities - Hanoi and Ho Chi Minh City in Vietnam, Seoul in South Korea, New Delhi in India, and Beijing in China.  DSM urban observations and air pollutants such as nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and formaldehyde (HCHO) derived from satellite data acquired by GOME, GOME-2, SCIAMACHY, and Sentinel-5P satellites are analyzed together with land surface topography from the NASA Shuttle Radar Topography Mission.  An atmospheric dynamics analysis is used to track air mass to capture the spatial pattern, transport, and distribution of the air pollutants.  With remote sensing inputs, the urban-climate-nested Gas-Aerosol-Transport-Radiation-General-Circulation-Mesoscale-and-Ocean Model provides a rigorous framework to understand physical processes and quantitatively estimate various system states and parameters to examine air pollution problem due to the acceleration in Asian urbanization. Together with air pollution observations and physical analyses, 4D DSM results enable a new quantification of urban development in space and in time that is crucial for physical modeling, assessing environmental change, and correctly portraying socio-economic trends in an intensively urbanized world.

High concentrations of PM_2.5 occurred frequently in China. However, it is still challenging to accurately predict PM_2.5 and its chemical components in models. This study we compared the inorganic components of PM_2.5 (sulfate, nitrate, ammonium (SNA)) simulated in WRF-Chem model with in-situ data in a heavy haze-fog event during November 2018 in Nanjing, China. Comparisons show that the model underestimates sulfate by 81% and fails to reproduce the significant increase of sulfate from early morning to noon, corresponding to the timing of fog dissipation that suggests the model underestimates the aqueous-phase formation of sulfate in clouds. Additionally, the model overestimates both nitrate and ammonium by 184% and 57%, respectively. These overestimates contribute to the simulated SNA being 77.2% higher than observed. However, cloud water content is also underestimated by the model which is a pathway for important aqueous-phase reactions. Therefore, we constrained the simulated cloud water content in a sensitivity simulation based on MODIS LWP observations. Results show that the simulation with MODIS-corrected cloud water content increases the sulfate by a factor of 3, decreases NMB by 53.5 %, and reproduces its diurnal cycle with the peak concentration occurring at noon. The improved sulfate simulation also improves the simulation of nitrate, which decreases the simulated nitrate bias from 184% to 50%, and shows a better diurnal pattern. Although the simulated ammonium is still higher than the observations, corrected cloud water content leads to a decrease of the modelled bias in SNA from 77.2% to 14.1%. The strong sensitivity of simulated SNA concentration to the cloud water content provides an explanation for the simulated SNA bias. Hence, the uncertainties in cloud water content can contribute to model biases in simulating SNA which are less frequently explored from a process-level perspective and can be reduced by constraining the model with satellite observations.

Water vapor vertical profiles are important in numerical weather prediction, moisture transport, and vertical flux calculation. This study presents the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) retrieval algorithm for water vapor vertical profiles and the retrieved results are validated with corresponding independent datasets under clear sky. The retrieved Vertical Column Densities (VCDs) and surface concentrations are validated with the Aerosol Robotic.
Network (AERONET) and National Climatic Data Centre (NCDC) datasets, achieving good correlation coecients (R) of 0.922 and 0.876, respectively. The retrieved vertical profiles agree well with weekly balloon-borne radiosonde measurements. Furthermore, the retrieved water vapor concentrations at different altitudes (100–2000 m) are validated with the corresponding European Centre for Medium-range Weather Forecasts (ECMWF) ERA-interim datasets, achieving a correlation coeffcient (R) varying from 0.695 to 0.857. The total error budgets for the surface concentrations and VCDs are 31% and 38%, respectively. Finally, the retrieval performance of the MAX-DOAS algorithm under different aerosol loads is evaluated. High aerosol loads obstruct the retrieval of surface concentrations and VCDs, with surface concentrations more liable to severe interference from such aerosol loads. To summarize, the feasibility of detecting water vapor profiles using MAX-DOAS under clear sky is confirmed in this work.

Organized mesoscale convection is important for many atmospheric phenomena and hazards, however the understanding of its governing mechanisms is incomplete. Theories explaining mesoscale organization rely on the interaction between convection outflows and lower-tropospheric wind shear. Here a new mechanism is presented, where lower-stratospheric wind shear is shown to influence mesoscale organization. The mechanism is linked to coupling between convection and gravity waves, with the stratosphere playing a role in shaping the tropospheric wave spectrum. The key result is that lower- stratospheric shear creates a preference for organized systems propagating in the same direction as the shear vector by weakening the systems propagating in the opposite direction to the shear. This result has important implications for stratosphere-troposphere interactions, numerical modeling, and understanding of convective organization in general.

Spatiotemporal characteristics of convective heating rate from Global Precipitation Measurement (GPM) are investigated, and convective gravity-wave (CGW) momentum flux of the off-line CGW parameterization by Kang et al. is calculated using the GPM data for 6 years (June 2014 to May 2020). The results are also compared with those using the MERRA-2 reanalysis data. The column maximum heating rate is maximal in the eastern Pacific, with dominant frequency peaks at semiannual and annual cycles. Cloud bottom height from GPM is relatively low while cloud top is nearly the same as MERRA-2, resulting in relatively deep convective heating. The cloud-top momentum flux (CTMF) of CGWs calculated using GPM is the largest in the Northern Hemisphere winter storm tracks with a similar pattern to that calculated using MERRA-2. However, large differences in the CTMF between that using GPM and MERRA-2 are found in the Southern Hemispheric (SH) storm tracks in July, where strong convective activities occurred more frequently from GPM than in MERRA-2, resulting in 1.8 times larger CTMF. Consequently, the magnitude of CGW drag (CGWD) using GPM is larger than that using MERRA-2. This implies that CGWs derived from realistic convective heating rates contribute more to decelerating the polar night jet in the SH. In the equatorial region, differences in CGWD are small in the stratosphere, whereas CGWD calculated using GPM is larger than that using MERRA-2 in the mesosphere. This is because high-phase-speed CGWs that can be less filtered during the vertical propagation are abundant in GPM-generated CGWs.

In this study, we investigate the characteristics of the convection under different wind shear condition using the vector vorticity cloud-resolving model (VVM). The Quasi Biannual Oscillation (QBO) describes that the west and east wind conditions oscillate in the tropical stratosphere. The wind conditions downward propagate into the troposphere, and it can lead to the changes in the characteristics of the convection. The gravity waves induced by the convection would feedback to QBO through the wave-mean flow interaction, which resulting in the phase change of QBO (Lindzen and Holton 1968; Holton and Lindzen 1972). Following Held et al. (1993) and Yoden et al. (2014), we perform idealized simulations to investigate the interactions between QBO and the convection. In the simulated result, the west and east wind vertically propagate from stratosphere to troposphere with averaged 113-days oscillation period. The period of the west wind shear condition is longer than that of the other condition before 500 days, but the periods of the two conditions become similar in the later. The results show that the horizontal distribution of the convection is aggregated under the strong wind shear condition, while it is sporadic when the wind shear condition is weaker. The difference in the period would be highly related to the characteristics of the convection. The higher domain-averaged column water vapor is accompanied by stronger low-level vertical wind shear, and both provides the convection with an appropriate environment to develop. The convection is more organized under stronger wind shear condition and moister environment, which could prolong the phase period of the QBO-like oscillation.

Seasonally dependent downward influences of the equatorial quasi-biennial oscillation (QBO) on the tropospheric circulations are investigated for the neutral periods without El Nino or La Nina events during 1979–2018 (in total 232months), by using observed and reanalyzed monthly-mean datasets of horizontal and vertical winds, temperature, geopotential height (GPH), mean sea-level pressure (MSLP), precipitation, and outgoing longwave radiation (OLR). A systematic analysis method is developed to survey any QBO influence on the tropospheric circulations from pole to pole for four seasons by introducing a QBO-phase defined with the leading two principal components ( = arctan [PC2(t)/PC1(t)] ) of the zonal-mean zonal wind in the equatorial lower stratosphere. Composite difference of a quantity between two groups of 120-degree each with opposite QBO-phases is investigated by sweeping the QBO-phase for every one degree and computing the statistical significance of the difference by a two-sided Student’s t-test. Through the systematic survey, QBO modulations of the circulation in summer high-latitudes are found in both hemispheres for a wide QBO-phases. The composite differences of MSLP and GPH in the upper troposphere-lower stratosphere (UTLS) show annular patterns or Rossby-wave teleconnection patterns in a wide QBO-phases. Temperature in the UTLS shows peculiarly zonally symmetric features in both hemisphere midlatitudes. Vertical winds in the middle troposphere and precipitation show positive pattern correlations, whereas OLR shows negative correlations, with zonally elongated features in the tropics and relatively small-scale features in the extratropics.In addition, some previous findings of the QBO modulations of tropical deep convection and winter polar vortex are also confirmed. Those include the modulation of tropical precipitation and OLR (Collimore et al., 2003) in some specific seasons (DJF and MAM), as well as that of winter (DJF) high-latitude zonal mean zonal wind (Holton and Tan, 1980) and zonal mean temperature (Naito and yoden, 2005).

We used newly available ERA5 hourly global data to examine the variations of atmospheric circulation on global scales and high frequencies. The space–time spectrum of surface pressure displays a typical red background spectrum but also a striking number of isolated peaks. Some peaks represent astronomically forced tides, but we show that most peaks are manifestations of the ringing of randomly excited global-scale resonant modes, reminiscent of the tones in a spectrum of a vibrating musical instrument. A few such modes have been tentatively identified in earlier observational investigations, but we demonstrate the existence of a large array of normal mode oscillations with periods as short as 2 h. This is a powerful and uniquely detailed confirmation of the predictions of the theory of global oscillations that has its roots in the work of Laplace two centuries ago. The delineation of the properties of the modes provides valuable diagnostic information about the atmospheric circulation. Our recent study has shown that some of the modes (esp. slow Rossby and Rossby-gravity modes) accompany circumglobally tranvelling rainfall variation in the tropics. This will be briefly discussed in the presentation.

Previous studies showed significant stratospheric warming at the Southern-Hemisphere (SH) high latitudes in September and October over 1979-2006. The warming trend center was located over the Southern Ocean poleward of the Western Pacific in September, with a maximum trend of about 2.8 K/decade. The warming trends in October showed a dipole pattern, with the warming center over the Ross and Amundsen Sea, and the maximum warming trend is about 2.6 K/decade. In the present study, we revisit the problem of the SH stratospheric warming in the recent decade. It is found that the SH high-latitude stratosphere continued warming in September and October over 2007-2017, but with very different spatial patterns. Multiple linear regression demonstrates that ozone increases play an important role in the SH high-latitude stratospheric warming in September and November, while the changes in the Brewer-Dobson circulation contributes little to the warming. This is different from the situation over 1979-2006 when the SH high-latitude stratospheric warming was mainly caused by the strengthening of the Brewer-Dobson circulation and the eastward shift of the warming center. Simulations forced with observed ozone changes over 2007-2017 shows warming trends, suggesting that the observed warming trends over 2007-2017 are at least partly due to ozone recovery. The warming trends due to ozone recovery have important implications for stratospheric, tropospheric and surface climates on SH.

Eastward airmass transport from the Asian summer monsoon (ASM) anticyclone in the upper troposphere and lower stratosphere (UTLS) often involves eastward shedding vortices, which can cover most of the Japanese archipelago. We investigated the aerosol characteristics of these vortices by analysing data from two lidar systems in Japan, at Tsukuba (36.1°N, 140.1°E) and Fukuoka (33.55°N, 130.36°E), during the summer of 2018. We observed several events with enhanced particle signals at Tsukuba at 15.5–18 km altitude (at or above the local tropopause) during August–September 2018, with a backscattering ratio of ~1.10 and particle depolarization of ~5% (i.e., not spherical, but more spherical than ice crystals). These particle characteristics may be consistent with those of solid aerosol particles, such as ammonium nitrate. Each event had a timescale of a few days. During the same study period, we also observed similar enhanced particle signals in the lower stratosphere at Fukuoka. Backward trajectory calculations for these sites for days with enhanced particle signals in the lower stratosphere and days without indicate that the former airmasses originated within the ASM anticyclone, and the latter more from edge regions. Reanalysis carbon-monoxide and satellite water-vapour data indicate that eastward shedding vortices were involved in the observed aerosol enhancements. Satellite aerosol data confirm that the period and latitudinal region were free from the direct influence of documented volcanic eruptions and high latitude forest fires. Our results indicate that the Asian Tropopause Aerosol Layer (ATAL) over the ASM region extends east towards Japan in association with the eastward shedding vortices, and that lidar systems in Japan can detect at least the lower stratospheric portion of the ATAL during periods when the lower stratosphere is undisturbed by volcanic eruptions and forest fires.

Ice clouds in the lower stratosphere (LSICs) regulate the water vapor budget in the stratosphere, impact the stratosphere and tropopshere exchange, and affect the surface energy balance. But the knowledge of its occurrence and formation mechanism is limited, especially in middle and high latitudes. In this study, we aim to assess the occurrence frequencies of LSIC over North America based on The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) instrument during the years 2006 to 2018. Possible driving forces such as deep convection are assessed based on Atmospheric Infrared Sounder (AIRS) observations during the same time. Results show that at nighttime, LSICs are most frequently observed during the thunderstorm season over the Great Plains from May to August (MJJA). Occurrence frequencies of deep convection and strong storm systems from AIRS show similar hotspots like the SCCs, with highest occurrence frequencies being observed over the Great Plains in MJJA. Both, seasonal patterns and daily time series of LSICs and deep convection show a high degree of spatial and temporal correlation. As further analysis indicates that the maximum fraction of LSICs generated by deep convection is observed over the Great Plains in MJJA, we conclude that, locally and regionally, deep convection is a leading factor for the formation of LSICs over North America. This study also analyzed the impact of gravity waves as a secondary formation mechanism for LSICs, as the Great Plains is a well-known hotspot for stratospheric gravity waves. Results provide better understanding of the physical processes and climate variability related to LSICs and will be of interest for modelers as LSIC sources such as deep convection and gravity waves are small-scale processes that are difficult to represent in global general circulation models.

With the enhanced satellite and in-situ observations such as Himawari-8 and Argo profiling float, the number and frequency of observations are dramatically increased since the 2000s. We have demonstrated that the combination of the incremental analysis updates (Bloom et al. 1996) and relaxation-to-prior methods (Zhang et al. 2004; Whitaker and Hamill 2012) significantly improved geostrophic balances and reproducibility in an Ensemble Kalman filter (EnKF)-based ocean data assimilation system assimilating observations as frequently as every day (Ohishi et al. in prep.). However, we have found that the low salinity structure in the ocean interior was degraded in the mid-latitude regions, and that the signals propagated toward subtropical regions. Therefore, the salinity field was not well reproduced in the subtropical and mid-latitude regions. To investigate the cause of the salinity degradation, we perform salinity budget analyses using outputs from the ocean data assimilation system. Cooler and warmer temperature increments near the surface and in the sub-surface, respectively, weakens density stratification in the mid-latitude region. These result in stronger vertical diffusions of salinity and eventually lead to the degradation. Innovation statistics of Desroziers et al. (2005) estimated from observations and ensemble forecasts imply that representation errors exist in the mid-latitude region with an abundance of fronts and eddies. Minamide and Zhang (2017) developed the adaptive observation error inflation (AOEI) method for all-sky satellite radiance data assimilation in the atmosphere to account for large differences of satellite radiances between cloudy and clear-sky conditions. Here we apply the AOEI method to the ocean data assimilation system for the first time to consider the representation errors in the mid-latitude region. The results show that the AOEI successfully decreases the temperature increments and reduces strong vertical diffusion, so that the salinity structure is greatly improved.

The Background Error Covariance (BEC) is a critical element in any data assimilation (DA) system as it spreads the observations information between model variables. Ensemble Kalman Filter (EnKF) DA systems provide an efficient framework to update the BEC based on the current model dynamics and observations, so-called flow-dependent BEC. The robustness of the EnKF BEC strongly depends on the ensemble size, i.e. number of samples used to describe the ocean state distribution (mean and covariance in an EnKF) . In real-time applications only limited ensembles (~1-100 members) can be however afforded. Large Ensemble experiments (LEEs) can provide robust BEC that may help devising better BECs by for example revealing missing information from the small ensembles or improving/tuning covariance localizations and inflations techniques, used to compensate for large ensemble. EnKFs further assume Gaussian state distributions, which may not be valid in highly nonlinear ocean regimes. LEEs provide enough samples for better assessment of the ocean state distributions and their types. We conducted a series of 1-year-long LEEs, starting from 50 to 5000 members, using the Red Sea ensemble data assimilation system that uses a 4km MITgcm forced with ensembles of ECMWF atmospheric fields for forecasting and assimilates real observations of sea surface temperature, sea surface height and temperature and salinity profiles. Results are analyzed under three different scenarios: (i) ensembles are integrated freely without assimilation, and ensembles evolve while assimilating observations (ii) with and (iii) without the covariance localization technique. Theresulting ensemble statistics will be presented and discussed.

A new Hybrid ensemble data assimilation system is implemented with a Massachusetts Institute of Technology general circulation model (MITgcm) of the Red Sea. The system is based on the Data Assimilation Research Testbed (DART) and combines a time-varying ensemble generated by the Ensemble Adjustment Kalman filter (EAKF) with a pre-selected quasi-static (monthly varying) ensemble as used in an Ensemble Optimal Interpolation (EnOI) scheme. The goal is to develop an efficient system that enhances the state estimate and model forecasting skill in the Red Sea with reduced computational load compared to the EAKF. Observations of satellite sea surface temperature (SST), altimeter sea surface height (SSH), and in situ temperature and salinity profiles are assimilated to evaluate the new system. The performance of the Hybrid scheme (here after Hybrid-EAKF) is assessed with respect to the EnOI and the EAKF results. The comparisons are based on the daily averaged forecasts against satellite SST and SSH measurements and independent in situ temperature and salinity profiles. Hybrid-EAKF yields significant improvements in terms of ocean state estimates compared to both EnOI and EAKF, in particular mitigating for dynamical imbalances that affects EnOI. Hybrid-EAKF improves the estimation of SST and SSH root-mean-square-differences by up to 20% compared to EAKF. High-resolution mesoscale eddy features, which dominate the Red Sea circulation, are further better represented in Hybrid-EAKF. Important reduction, by about 75%, in computational cost is also achieved with the Hybrid-EAKF system compared to the EAKF. These significant improvements were obtained with the Hybrid-EAKF after accounting for uncertainties in the atmospheric forcing and internal model physics in the time-varying ensemble.

Typhoon precipitation is thought to depend on the large-scale environment, as well as on small-scale process. Numerical prediction of typhoon intensity and precipitation continues to be challenge, the initial fields in a convection-allowing model must be accurate across a range of spatial scales. Radar is one of few observation platforms capable of observing TC inner-core structure at high spatial and temporal resolution. In this study, we use the ensemble Kalman filter (EnKF) to directly assimilate radar reflectivity, to evaluate the impact of single- and double-moment microphysics schemes on radar data assimilation (DA) and forecast, and to seek the optimal efficient strategy for radar data assimilation. EnKF-based radar reflectivity assimilation and forecasting experiments are conducted for the case of typhoon Lekima (2019). The effects of radar assimilation on the analyses and forecasts are investigated. The performance of the DA experiments is assessed in terms of both quantitative verification scores of subsequent forecasts and subject evaluations of forecast features. Our study shows that radar reflectivity assimilation has positive impact on both intensity and precipitation forecast. The DA experiments with double-moment microphysics (Thompson) scheme show the significant improvement in both analyzed structure and evolution of the forecasted typhoon, compared to the single-moment (Lin) scheme. Detailed diagnostics reveal that the ensemble error covariance is the key for better analysis and forecast of typhoon, due to its ability to better update the vortex thermodynamic structure.

The ground-based GNSS receiver measures the delay in the path in receiving a signal from a GNSS satellite and zenith total delay (ZTD) expresses this delay as the excess path length along the zenith direction. The ZTD data are available at a high temporal frequency and can provide the fast moisture information. This study investigates the impact of assimilating the GNSS-ZTD data on short-term heavy rainfall prediction associated with an afternoon thunderstorm over norther Taiwan on 22 July 2019. The ZTD data from 166 surface stations are assimilated with the WRF-LETKF system with an analysis cycle of 30 minutes. Results suggest that assimilating the ZTD data provides effective moisture adjustment from offshore to coastal area of Taiwan and enhances the onshore flow into the Taipei basin. With the great amount of moisture convergence flux over norther Taiwan, the model generates strong convections and the heavy precipitation takes place in a short time. In particular, results suggest that the moisture correction over northwestern Taiwan is critical to generate rainfall over Taoyuan and New Taipei, which further stimulates the development of strong convection over the Taipei basin.  Without enough moisture correction, such mechanism for heavy rainfall cannot be triggered in the forecast.

An assumption of uncorrelated observation errors is commonly adopted in conventional data assimilation. For this reason, high-resolution data is re-sampled with strategies like superobbing or data thinning. This also sacrifices the advantage of high temporal and spatial resolution observations that can provide essential detailed structures. However, assimilating the high-resolution data, such as radar radial wind, without considering the observation error correlation can lead to overfitting and thus degrade the performance of data assimilation and forecast. This study uses the radar ensemble data assimilation system, which couples the Weather Research and Forecasting model and Local Ensemble Transform Kalman Filter (WRF-LETKF), to assimilate the radar radial velocity and reflectivity data. We present a strategy to include the error correlation of the Doppler radar radial velocity in the WRF-LETKF radar assimilation system and examine their impact on short-term precipitation prediction based on the heavy rainfall case on 2nd June 2017 in Taiwan. We first estimate the horizontal error correlation scale for radial velocity based on the innovation statistics. The error correlation scale is approximately 25 km. The introduction of correlated observation error for Doppler radar radial winds exhibits more small-scale features in the wind analysis corrections compared to the experiment using the independent observation assumption. Consequently, the modification on wind corrections leads to stronger convergence accompanied by higher water vapor content, and induces subsequent local convections, resulting in more accurate simulated reflectivity. For probability quantitative precipitation forecast (PQPF), our results show that the experiment using the correlated observation error has higher probability to generate heavy rainfall and agrees better with the observation. 

In precipitation science, satellite data have been providing precious, fundamental information, while numerical models have been playing an equally important role. Data assimilation integrates the numerical models and real-world data and brings synergy. We have been working on assimilating the GPM data into the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) using the Local Ensemble Transform Kalman Filter (LETKF). This presentation summarizes the recent progress of the project started in 2013 to explore data assimilation methods for GPM and other satellite observations. The achievements are highlighted by successful data assimilation of GPM DPR reflectivity and a new development of an efficient data-driven (DD) method for a satellite simulator a.k.a. an observation operator in data assimilation. By assimilating DPR reflectivity, we estimated a model microphysics parameter corresponding to snowfall terminal velocity and successfully reduced the gap between the model-produced and observed CFAD (Contoured Frequency by Altitude Diagram). The results showed improvements in radiation budgets (OSR and OLR biases) and overall numerical weather prediction skill. As for the DD method for a satellite simulator, we developed a new approach using neural networks to simulate satellite microwave radiances without a need for a bias correction treatment. We applied machine leaning with model forecast data and corresponding actual satellite observations and built a bias aware simulator for satellite radiances. The results showed that the satellite simulator worked properly although slightly worse than the case with a radiative transfer model and bias correction. The early results are encouraging since we do not need a bias correction method to build a generally complex system to assimilate satellite radiance data.

The direct assimilation of satellite infrared brightness temperature (IRBT) observations has appreciably improved operational numerical weather prediction. Most applications and research into IRBT ensemble data assimilation (DA) thus far employ methods that explicitly assume Gaussian forecast statistics. However, the forecast ensemble statistics of both IRBT and model variables change depending on the presence or absence of clouds. These differences suggest that cloudy members are drawn from distributions different from clear members. Thus, replacing the Gaussian assumption with a Gaussian mixture model distribution assumption might potentially improve the impacts of assimilating IRBT observations. In a recent publication, we proposed using an efficient bi-Gaussian variant of the popular ensemble Kalman filter (EnKF) to handle having a mix of clear and cloudy members. Our BGEnKF method was tested against the EnKF using the 40-variable Lorenz 1996 model, a nonlinear observation operator that mimics IRBT statistics, and 20 ensemble members. Our results suggest that the BGEnKF can potentially outperform the EnKF in assimilating IRBT observations. In this talk, we will compare the performance of the BGEnKF and EnKF using the Weather Research and Forecasting (WRF) model. Their performances will be assessed using observing system simulation experiments (OSSE) over the equatorial Indian Ocean, during the start of the October 2011 Madden Julian Oscillation event. IRBT observations from the Meteosat Visible and Infrared Imager (MVIRI) infrared window channel will be assimilated.

This study explores the effect of the initial wind and moisture structure on the predictability of typhoon Lekima’s intensity through a 20-member ensemble simulation using a nested 27/9/3 km WRF model. Some members capture the peak intensity, while the others do not. The ensemble members are then separated into Strong and Weak groups according to the maximum 10-m wind speed at 48 h. Differences between the two groups and the ensemble sensitivity analyses indicate that intensity forecasts are sensitive to the initial primary circulation outside RMW (rather than initial maximum 10-m wind speed at RMW) and the initial secondary circulation. With greater absolute angular momentum (AAM) beyond the RMW directly related to stronger primary circulation, and stronger radial inflow, Strong group is found to have larger AAM import in low-level, helping to spin up the TC. Initial moisture in inner-core is also important to the intensity predictability through the development of inner-core convection. More sufficient initial inner-core moisture corresponds to stronger TC intensity at 48 h through the development of the inner-core convection. Besides, three additional sensitivity experiments are conducted to study the effect of model uncertainty in terms of model resolution on intensity forecast errors. The horizontal grid resolution greatly impacts the predictability of Lekima’s intensity, and the finer resolution is crucial to simulate the intensification and to capture the observed peak intensity. Our study demonstrates the role of the initial condition in inner-core and model resolution, suggesting the importance of inner-core data assimilation and a high-resolution model. 

Extremely persistent heavy rainfall (EPHR), the main source of flooding rainstorm in Southern China, has a longer duration compared with short-duration heavy rainfall (SHR), which is more likely to cause natural disasters like floods. However, the current research on EPHR is still of lack. Based on the hourly precipitation grid data in Southern China from April to September of 2008-2019, this paper defines EPHR, conducts research on its development, spatiotemporal distribution, and typical synoptic patterns. It was found that the EPHR process was asymmetric, with growing time about 1 h shorter than fading time on average. On average, EPHR accounts for less than 40% of duration but nearly 3/4 of precipitation in events. There was a tendency for EPHR to occur near land-sea boundaries and mountainous areas. It was more frequent in the early morning, differing from the “afternoon peak” observed for SHR. There were five EPHR high-incidence areas (HIAs). EPHR with longest duration occurred in coastal areas, and the largest precipitation was recorded near the Yunkai Mountains. Influenced by local characteristics, the peaking time of monthly and diurnal variations differed in HIAs. The low in Beibu Gulf was closely related to the occurrence of EPHR, but the relative positions of the low and HIAs varied in different synoptic pattern types. The trough at 500 hPa and the low vortex or shear line at 850 hPa interacted with the surface convergence mechanism to produce strong upward motion. There was also a warm and wet tongue at 850 hPa above the HIAs, combined with the vertically integrated moisture flux convergence. Additionally, the marine boundary-layer jet and isentropic ascent in lower troposphere possibly supported the coastal EPHR.

The Yangtze River delta (YRD) of the East China has undergone rapid urbanization since the mid-1990’s, forming an extensive urban agglomeration. A 44-yr (i.e., 1975-2018) climatology of the summertime (JJA) extreme hourly precipitation (EXHP; greater than the 90th percentile) over the YRD is analyzed using hourly rain gauge observations, and then the relationship between rapid urbanization and the EXHP is explored. Results show that increased occurrence frequency and amount of the EXHP are detected during the rapid-urbanization era as compared to the pre-urbanization era. Moreover, statistically significant larger increasing trends in both non-TC and TC-induced EXHP are observed at the urban stations than those at the nearby rural stations, collocating with the trends of the surface temperature. An analysis of 113 locally developed non-TC extreme rainfall events during 2011-18 summers shows contrasting rainfall distributions between strong- and weak-UHI events, suggesting the contribution of the urban heat island (UHI) effects to the more occurrences of the EXHP, especially over the band-shaped urban region where several major cities are distributed. WRF simulation of one of the extreme events occurring on 26 Jul 2018 indicates that the UHI effects could accelerate the initiation of convective cells, and the downward rainfall is enhanced under the influence of upstream city cluster through the cold outflows during the convective dissipation stage. This study reveals a clear correlation between the increased EXHP and rapid urbanization over the YRD region.

In this study, high-resolution surface and radar observations are used to analyze 24 localized extreme hourly rainfall (EXHR, > 60mm/h) events with strong urban heat island (UHI) effects over the Great Bay Area (GBA) in South China during 2011-2016 warm seasons. Quasi-idealized, convection-permitting ensemble simulations driven by diurnally varying lateral boundary conditions, which are extracted from the composite global analysis of 3-5 June 2013, are then conducted with a multi-layer urban canopy model to unravel the influences of the UHI and various surface properties nearby on the EXHR generation in a complex geographical environment with sea-land contrast, orography, and vegetation variation. Results show that EXHR is mostly distributed over the urban agglomeration and within about 40km on its downwind side, and produced during the afternoon-to-evening hours by short-lived meso-γtoβ-scale storms. On the EXHR days, the GBA is featured by a weak-gradient environment with abundant moisture, and a weak southwesterly flow prevailing in the boundary layer (BL). The UHI effects lead to the development of a deep mixed layer with “warm bubbles” over the urban agglomeration, and the enhancement of sea breezes under the influence of the herringbone coastline. The lower-BL convergence and BL-top divergence is developed in collocation with the “warm bubbles”, assisting in convective initiation. Such urban BL processes are enhanced by orographically increased horizontal winds, thereby intensifying convective-scale circulation and increasing the inhomogeneity of rainfall production over the urban region. Vegetation variations are not found to be an important factor in determining the EXHR production.

Mesoscale convective systems (MCSs) are important precipitation systems in the warm-season in eastern China and frequently produce damaging weather events. They modulate precipitation diurnal variations and exert momentum and heat perturbations to the large-scale environment. Accurate representation of MCSs is lacking in coarse resolution models since they cannot resolve individual convective plumes or organized convections. In this study, we present a convection-permitting climate simulations for the warm seasons of 2008-2017 with a 3km grid spacing that can explicitly resolve MCSs. An object-based tracking algorithm is used to identify the MCSs from the model results and gauge-satellite merged precipitation observation. It is found that the simulation can successfully capture the distribution and seasonal variation of MCS occurrences and key properties (such as size, lifetime, moving speed and rain rate). Specifically, it faithfully reproduces the nocturnal peak of MCS precipitation, and the diurnal variation and eastward propogation of MCS precipitation east of the Tibetan Plateau, demonstrating its superior ability in simulating organized convections. The results confirm the important role played by MCSs in warm-season precipitation, especially extreme precipitation. The MCS contribution to extreme precipitation is more than 40% in May and June, with more than 70% in the Yangtze River Basin in June. The finding that non-MCS precipitation is the main cause of the bias in the precipitation diurnal cycle provides useful guidance for future model improvement. 

Because of its dense population, extreme precipitation, in particular hourly extreme precipitation (HEP), is receiving increasing attention from both academic and public bodies in eastern China. Based on a continuous 50‐year record of hourly precipitation and reanalysis data, we show here for the first time that changes in the HEP occurrence are dominated by changes in the duration of the Meiyu front system. Further analyses reveal that greater occurrence of HEP in northeastern China, the lower reach of Yangtze River, and southern China during the warm season is largely due to a longer duration of the post‐Meiyu I stage when Meiyu front stays in northern China and meridional circulation dominates the eastern coastal area of China. These results improve our understanding of the changing behavior of extreme rainfall in China and shed light on the prevention of flash floods.

Aerosol are known to influence cloud properties and processes via several of mechanisms. At the same time cloud processes feedback onto populations of aerosols, firstly because initial aerosols are depleted when they nucleate cloud and, secondly, because aerosol material is released back into the air when hydrometeors evaporate. The impact of such interactions on heavy rainfall processes is still poorly understood. In this talk we use global and regional simulations with Met Office Unified Model, with a cloud-aerosol interacting microphysics and prognostic aerosols, to study the effects of aerosol-cloud interactions on rainfall extremes globally and on meso-scale and synoptic scale processes in the monsoon regions. The sensitivity of extreme rainfall to aerosol perturbations is compared to other sources of model uncertainty, including land-cover characteristics and model-physics parametrizations 

It has been shown in some previous studies that urbanization can lead to increases in heavy rainfall due to the urban heat island effect and the contribution from increased aerosol concentrations. However, not much investigation has been carried out on whether rainfall, especially heavy rainfall, will increase when a tropical cyclone (TC) makes landfall near a highly-urbanized area. This paper will present results from radar observations as well as numerical simulations of TCs making landfall near the Pearl River Delta (PRD), which show that as a TC is close to landfall, heavy rainfall generally occurs either over the megacity area of PRD or on the downstream side of the cyclone circulation of the TC. Numerical simulation results suggest that the physical attributes of the urban area such as increased friction and increased enthalpy flux all contribute towards such an increase. Results from increased aerosol concentration will also be presented.

On 21 October 2019, and in the days that followed, an animation of atmospheric gravity waves propagating away from the northwest coast of Western Australia went viral on social media and garnered the attention of local and international news outlets. These waves were associated with two atmospheric bores generated by convection over inland Western Australia that then propagated a significant distance from their source into the Timor Sea and Indian Ocean. While gravity waves in the atmosphere are more ubiquitous than the reported “rarely-seen phenomenon” implied, many things about these bores were indeed impressive. They had a large number of visible oscillations, that could be tracked a significant distance over a two day period on Himawari satellite images. Dust associated with the convective outflow responsible for their generation could also be seen some distance away from the coast. More unique though, is that the second of these bores passed over the RV Investigator during the Leg 1 voyage of the Australian component of the YMC field campaign, and was captured by the onboard meteorological station. Pressure sensors indicated as many as 7 oscillations on the order of 0.5 hPa after an initial pressure rise, as well as a number of smaller magnitude oscillations. These were co-located with wind oscillations of 3 m s^-1 over more than 25 degrees. Additionally, during this voyage, twice daily radiosonde launches were performed at 00 and 12 UTC. The 12 UTC launch from the previous evening provides information about the environment the bore propagated through, while the 00 UTC launch was shortly after the passage of the leading edge of the bore. These observations will be analysed and compared with theory in order to better understand the environment in which these events occur, and to see how well the theory predicts the observed bore.

This study provides a comprehensive overview of diurnal rainfall signal performance within the current collection of models in Phase 6 of the Coupled Model Inter-comparison Project (CMIP6). The results serve as a reference for understanding model physics performance to represent precipitating processes and atmosphere–land–ocean interactions in response to the diurnal solar radiation cycle. Performance metrics are based on the phase, amplitude, and two empirical orthogonal function (EOF) modes of the climatological diurnal rainfall cycle derived from a Tropical Rainfall Measurement Mission observational dataset. We found that the ensemble model biases of diurnal phase and amplitude over lands improved from CMIP5 to CMIP6; however, those over oceans are still highly uncertain among CMIP6 models. Evaluation with observed EOF modes shows that the CMIP6 models are bifurcated based on the second EOF (EOF2), which represents diurnal rainfall contrast of coastal regimes where large biases of phase and amplitude reside. While the model ensemble suggests models are benefited from higher resolution in simulating phase and amplitude biases, the most distinct difference between the bifurcations is that one group successfully captures prevailing nighttime rainfall over tropical islands and coasts, especially over the Maritime Continent. Convective rainfall diagnosed by cumulus parameterization is found to be responsible for such biases. Our results suggest that CMIP6 models have generally been improved in their representation of diurnal rainfall cycles; however, for coastal diurnal regimes, more study is needed to improve the model parameterization of precipitation processes interacting with islands and coastal regions as current model resolution is still too coarse to resolve them.

The impact of diurnal precipitation over Sumatra Island, the Indonesian Maritime Continent (MC), on synoptic disturbances over the eastern Indian Ocean is examined using high-resolution rainfall data and the Japanese 55-year Reanalysis data during the rainy season from September to April for the period 2000–2014. When the diurnal cycle is strong, the high precipitation area observed over Sumatra in the afternoon migrates offshore during nighttime and reaches 500 km off the coast on average. The strong diurnal events are followed by the development of synoptic disturbances over the eastern Indian Ocean for several days, and apparent twin synoptic disturbances straddling the equator develop only when the convective center of the Madden–Julian Oscillation (MJO) lies over the Indian Ocean (MJO-IO). Without the MJO, the synoptic disturbances develop mainly south of the equator. The differences in the locations and behaviors of active synoptic disturbances are related to the strength of mean horizontal winds in the lower troposphere. During the MJO-IO, the intensification of mean northeasterly winds in the northern hemisphere blowing into the organized MJO convection in addition to mean southeasterly winds in the southern hemisphere facilitate the formation of the twin disturbances. These results suggest that seed disturbances arising from the diurnal offshore migration of precipitation from Sumatra develop differently depending on the mean states over the eastern Indian Ocean. Furthermore, it is shown that the MJO events with the strong diurnal cycle tend to have longer duration and continuing eastward propagation of active convection across the MC, whereas the convective activities of the other MJO events weaken considerably over the MC and develop again over the western Pacific. These results suggest that the strong diurnal cycle over Sumatra facilitates the smooth eastward propagation of the intraseasonal convection across the MC.

This study examined the diurnal cycle of convection over Sumatra Island in an active phase of the Madden-Julian Oscillation (MJO) during the pre-YMC 2015 observation campaign based on observations and convection-permitting numerical model. Satellite observation indicates that, prior to an active phase of the MJO in early December, convection frequently occurred over the island in the afternoon. In contrast, during the active phase of the MJO in mid-December, afternoon convection over the island was suppressed, and a gap in convection aligned with the island was evident during the early morning hours. On the contrary, convective activities were observed over the offshore region within the whole day, especially during the daytime that was much weaker before. Our numerical experiments successfully replicated the main features of the observed modulation of the diurnal convection. Results suggest that, although the moisture was accumulated within the boundary layer over the mountainous areas during the active phase of the MJO, the afternoon convection was suppressed and delayed. Moisture budget analysis demonstrates that vertical moisture advection over the island, the dominating factor of diurnal convection, was weakened and delayed during the MJO active phase. It further became negative within the lower-to-mid troposphere from midnight to the next morning, indicating that the convection over the island was suppressed by the subsidence. Our results showed that such subsidence was highly related to the enhanced convective activities over the offshore region. On the other hand, our results also suggest that the strong westerlies played a secondary role in weakening and delaying the diurnal convection over the island, which induced the negative horizontal moisture advection within the mid-troposphere. Overall, this study suggests that the modulated diurnal cycle over Sumatra Island was likely induced by the enhanced convective activities over the offshore region during the active phase of the MJO. 

The land-sea contrast in the Maritime Continent (MC) has been found to influence the Madden-Julian Oscillation (MJO). However, the specific contribution from individual islands to the precipitation over the surrounding islands during MJO propagation is not well known. We found that when an island is removed in the presence of lower-tropospheric westerlies, precipitation increases over islands that are located to its east due to the strengthening of the westerlies. Frictional convergence of the stronger westerlies, aided by Coriolis, leads to an increase in vertical advection of moisture and precipitation over an island located to the east. On the other hand, the reduced heating over the removed island reduces the westerlies and precipitation to the west of the removed island and is consistent with the response of large-scale circulation to tropical heating. During background easterlies prior to MJO arrival, a systematic decrease in precipitation was found in the surrounding islands to the west side of the removed island. But, on the eastern side of the removed island, no systematic change in precipitation was found. The results imply that changes in the large-scale circulation in response to convection (or a lack thereof) over a removed island may significantly influence precipitation in the neighboring islands. Therefore, biases in model precipitation over an island in the MC may arise from bias in precipitation over a neighboring island. Moreover, the presence of different island chains in the MC has led to a more conducive environment for more overall precipitation over the islands in the MC.

The equatorial lower-stratospheric quasi-biennial oscillation (QBO) is explained to be maintained by interactions with waves, but such equi-amplitude eastward and westward waves as to robustize the periodicity and meridional-vertical extension have not been clarified. Observations have revealed that the coastal diurnal cycle (CDC) is the most robust mode of cloud-rainfall generation in the equatorial troposphere, in particular along the world’s longest coastlines surrounding major islands of the Indonesian maritime continent (IMC). The CDC (sea-land breeze) circulation for |latitude| < 30° (|Coriolis factor| < 2π/1 day) is a superposition of internal gravity waves. At the surface both the upward and downward components exist to satisfy zero vertical velocity condition, and large eddy viscosity/diffusivity balancing the buoyancy makes the velocity field almost similar to a steady circulation cell, of which the intensity and horizontal scale are governed by the land-sea temperature contrast. Above this cell, the acceleration balances the buoyancy, and the upper boundary (radiation) condition selects upward propagating waves. The land-sea contrast is reversed between day/night, and the horizontal phase velocity directs landward/seaward. The CDC bidirectional (eastward/westward at cross-equatorial coastlines) gravity-wave generation mechanism resembles the bottom condition in the Plumb’s QBO analog. The meridional range of QBO is narrower than the internal (vertical propagation) region, and is determined by the region where the solar diurnal cycle is larger than the annual cycle, within 10°-latitudes similar to the meridional range of the IMC. The zonal-wind amplitude of QBO is dependent on migration velocities of individual clouds, somewhat faster than the CDC. The period of QBO decreases with an increase of the CDC amplitudes, and becomes biperiodic for too strong CDC. The QBO-period becomes longer for a weaker CDC (such as in El Niño), and decayed if too weak.

The potential predictability of the Madden–Julian Oscillation (MJO) under the easterly and westerly phases of the Quasi-Biennial Oscillation (easterly: EQBO and westerly: WQBO) in boreal winter (November–February) is investigated using observational data. Nonlinear local Lyapunov exponents are computed for various MJO indices to quantify the MJO predictability. The results show that all MJO indices exhibit higher predictability during EQBO winters than during WQBO winters. The highest potential predictability of 43 days during EQBO winters and 37 days during WQBO winters is found for the MJO index obtained from bandpass-filtered (30–80 days) outgoing longwave radiation, 850-hPa zonal wind, and 200-hPa zonal wind data. Whereas, the potential predictability of the MJO from the real-time multivariate MJO index is 21 days during EQBO winters and 13 days during WQBO winters. Moreover, excluding strong ENSO years from EQBO and WQBO winters has a limited impact on the MJO predictability. The longer persistence and less disorganization of the MJO during the EQBO winters lead to the higher predictability for EQBO winters, as compared with that for WQBO winters.

Convectively coupled equatorial Kelvin waves (CCKWs) are major tropical weather systems bringing high impact weather and flooding, particularly in the Maritime Continent. CCKWs share many key features with theoretical, dry, linear equatorial Kelvin waves, such as a predominantly zonal component of their horizontal wind anomalies, and eastward propagation. Here, a vorticity budget for CCKWs is constructed using reanalysis data, to identify the basic mechanisms of eastward propagation and the observed growth. The budget is closed, with a small residual. Vortex stretching, from the divergence of the Kelvin wave acting on planetary vorticity (the $-fD$ term), is the sole mechanism by which the vorticity structure of a theoretical Kelvin wave propagates eastward. This term is also the key mechanism for the eastward propagation of CCKWs, but its different phasing also leads to growth of the CCKW. However, unlike in the theoretical wave, other vorticity source terms also play a role in the propagation and growth of CCKWs. In particular, vortex stretching from the divergence of the CCKW acting on its own relative vorticity (the $-\zeta D$ term) is actually the largest source term, and this contributes mainly to the growth of the CCKW, as well as to eastward propagation. Horizontal vorticity advection (and to a lesser extent, vertical advection), counters the vortex stretching, and acts to retard the growth of the CCKW. The tilting of horizontal vorticity into the vertical also plays a role. However, the meridional advection of planetary vorticity (the $-\beta v$ term, the main mechanism for westward propagation of Rossby waves), is negligible. The sum of the source terms in this complex vorticity budget leads to eastward propagation and growth of the CCKWs. The implications for numerical weather prediction, forecasting and climate simulations are discussed.