The Extratropical 40-Day Oscillation in the UCLA General Circulation Model. Part II: Spatial
We examine intraseasonal oscillations in a three-year, perpetual-January simulation using a version.
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We examine intraseasonal oscillations in a three-year, perpetual-January simulation using a version.
The influence of ocean on the continental rainfall, in seasonal and intraseasonal scales were examined. A method has been developed and validated to estimate IWT from the product of SWV and IWV.
This study examines the nature of episodes of enhanced warm-season moisture flux into the Gulf of California. Both spatial structure and primary time scales of the fluxes are examined using the 40-yr ECMWF Re-Analysis data for the period 1980-2001. The analysis approach consists of a compositing technique that is keyed on the low-level moisture fluxes into the Gulf of California. The results show that the fluxes have a rich spectrum of temporal variability, with periods of enhanced transport over the gulf linked to African easterly waves on subweekly (3-8 day) time scales, the Madden-Julian oscillation (MJO) at intraseasonal time scales (20-90 day), and intermediate (10-15 day) time-scale disturbances that appear to originate primarily in the Caribbean Sea-western Atlantic Ocean. In the case of the MJO, enhanced low-level westerlies and large-scale rising motion provide an environment that favors large-scale cyclonic development near the west coast of Central America that, over the course of about 2 weeks, expands northward along the coast eventually reaching the mouth of the Gulf of California where it acts to enhance the southerly moisture flux in that region. On a larger scale, the development includes a northward shift in the eastern Pacific ITCZ, enhanced precipitation over much of Mexico and the southwestern United States, and enhanced southerly/southeasterly fluxes from the Gulf of Mexico into Mexico and the southwestern and central United States. In the case of the easterly waves, the systems that reach Mexico appear to redevelop/reorganize on the Pacific coast and then move rapidly to the northwest to contribute to the moisture flux into the Gulf of California. The most intense fluxes into the gulf on these time scales appear to be synchronized with a midlatitude short-wave trough over the U.S. West Coast and enhanced low-level southerly fluxes over the U.S. Great Plains. The intermediate (10-15 day) time-scale systems have zonal wavelengths roughly twice that of the easterly waves, and their initiation appears to be linked to an extratropical U.S. East Coast ridge and associated northeasterly winds that extend well into the Caribbean Sea during their development phase. The short (3-8 day) and, to a lesser extent, the intermediate (10-15 day) time-scale fluxes tend to be enhanced when the convectively active phase of the MJO is situated over the Americas.
The Madden-Julian Oscillation (MJO) is the dominant intraseasonal signal in the global tropical atmosphere. Almost all numerical climate models have difficulty to simulate realistic MJO. Four TRMM datasets of latent heating were diagnosed for signals in the MJO. In all four datasets, vertical structures of latent heating are dominated by two components, one deep with its peak above the melting level and one shallow with its peak below. Profiles of the two components are nearly ubiquitous in longitude, allowing a separation of the vertical and zonal/temporal variations when the latitudinal dependence is not considered. All four datasets exhibit robust MJO spectral signals in the deep component as eastward propagating spectral peaks centered at period of 50 days and zonal wavenumber 1, well distinguished from lower- and higher-frequency power and much stronger than the corresponding westward power. The shallow component shows similar but slightly less robust MJO spectral peaks. MJO signals were further extracted from a combination of band-pass (30 - 90 day) filtered deep and shallow components. Largest amplitudes of both deep and shallow components of the MJO are confined to the Indian and western Pacific Oceans. There is a local minimum in the deep components over the Maritime Continent. The shallow components of the MJO differ substantially among the four TRMM datasets in their detailed zonal distributions in the eastern hemisphere. In composites of the heating evolution through the life cycle of the MJO, the shallow components lead the deep ones in some datasets and at certain longitudes. In many respects, the four TRMM datasets agree well in their deep components, but not in their shallow components and the phase relations between the deep and shallow components. These results indicate that caution must be exercised in applications of these latent heating data.
ERA-40 data are analyzed to demonstrate that wave forcing at lower latitudes plays a crucial role in driving the tropical upwelling portion of the Brewer-Dobson circulation. It is shown that subtropical wave forcing is correlated with tropical upwelling on both intraseasonal and interannual time scales when transient waves are taken into account, and that tropical wave forcing exerts its influence on tropical upwelling via its body force on the zonal mean flow.
Although active sensors can provide cloud profiles at good vertical resolution, clouds are often coupled with dynamics to form fast and organized structures. Lack of understanding of these organized systems leads to great challenge for numerical models. The deficiency is partly reflected, for example, in poorly modeled intraseasonal variations (e.g., MJD). Remote sensing clouds in the middle and upper troposphere has been challenging from space. Vis/IR sensors are sensitive to the topmost cloud layers whereas low-frequency MW techniques are sensitivity to liquid and precipitation at the bottom of cloud layers. The middle-level clouds, mostly in the ice phase, require a sensor that has moderate penetration and sensitivity to cloud scattering, in order to measure cloud water content. Sensors at submm wavelengths provide promising sensitivity and coverage with the spatial resolution needed to measure cloud water content floating in the upper air. In addition, submm-wave sensors are able to provide better measurements of upper-tropospheric humidity than traditional microwave instruments.
In Asian monsoon countries such as China and India, human health and safety problems caused by air-pollution are worsening due to the increased loading of atmospheric pollutants stemming from rising energy demand associated with the rapid pace of industrialization and modernization. Meanwhile, uneven distribution of monsoon rain associated with flash flood or prolonged drought, has caused major loss of human lives, and damages in crop and properties with devastating societal impacts on Asian countries. Historically, air-pollution and monsoon research are treated as separate problems. However a growing number of recent studies have suggested that the two problems may be intrinsically intertwined and need to be studied jointly. Because of complexity of the dynamics of the monsoon systems, aerosol impacts on monsoons and vice versa must be studied and understood in the context of aerosol forcing in relationship to changes in fundamental driving forces of the monsoon climate system (e.g. sea surface temperature, land-sea contrast etc.) on time scales from intraseasonal variability (~weeks) to climate change (~ multi-decades). Indeed, because of the large contributions of aerosols to the global and regional energy balance of the atmosphere and earth surface, and possible effects of the microphysics of clouds and precipitation, a better understanding of the response to climate change in Asian monsoon regions requires that aerosols be considered as an integral component of a fully coupled aerosol-monsoon system on all time scales. In this paper, using observations and results from climate modeling, we will discuss the coherent variability of the coupled aerosol-monsoon climate system in South Asia and East Asia, including aerosol distribution and types, with respect to rainfall, moisture, winds, land-sea thermal contrast, heat sources and sink distributions in the atmosphere in seasonal, interannual to climate change time scales. We will show examples of how elevated absorbing aerosols (dust and black carbon) may interact with monsoon dynamics to produce feedback effects on the atmospheric water cycle, leading to in accelerated melting of snowpacks over the Himalayas and Tibetan Plateau, and subsequent changes in evolution of the pre-monsoon and peak monsoon rainfall, moisture and wind distributions in South Asia and East Asia.
Radiative and turbulent surface exchanges of heat and moisture across the atmosphere-ocean interface are fundamental components of the Earth s energy and water balance. Characterizing the spatiotemporal variability of these exchanges of heat and moisture is critical to understanding the global water and energy cycle variations, quantifying atmosphere-ocean feedbacks, and improving model predictability. These fluxes are integral components to tropical ocean-atmosphere variability; they can drive ocean mixed layer variations and modify the atmospheric boundary layer properties including moist static stability, thereby influencing larger-scale tropical dynamics. Non-parametric cluster-based classification of atmospheric and ocean surface properties has shown an ability to identify coherent weather regimes, each typically associated with similar properties and processes. Using satellite-based observational radiative and turbulent energy flux products, this study investigates the relationship between these weather states and surface energy processes within the context of tropical climate variability. Investigations of surface energy variations accompanying intraseasonal and interannual tropical variability often use composite-based analyses of the mean quantities of interest. Here, a similar compositing technique is employed, but the focus is on the distribution of the heat and moisture fluxes within their weather regimes. Are the observed changes in surface energy components dominated by changes in the frequency of the weather regimes or through changes in the associated fluxes within those regimes? It is this question that the presented work intends to address. The distribution of the surface heat and moisture fluxes is evaluated for both normal and non-normal states. By examining both phases of the climatic oscillations, the symmetry of energy and water cycle responses are considered.
In this paper, preliminary results are presented showing that the two record-setting extreme events during 2010 summer (i.e., the Russian heat wave-wildfires and Pakistan flood) were physically connected. It is found that the Russian heat wave was associated with the development of an extraordinarily strong and prolonged extratropical atmospheric blocking event in association with the excitation of a large-scale atmospheric Rossby wave train spanning western Russia, Kazakhstan, and the northwestern China-Tibetan Plateau region. The southward penetration of upper-level vorticity perturbations in the leading trough of the Rossby wave was instrumental in triggering anomalously heavy rain events over northern Pakistan and vicinity in mid- to late July. Also shown are evidences that the Russian heat wave was amplified by a positive feedback through changes in surface energy fluxes between the atmospheric blocking pattern and an underlying extensive land region with below-normal soil moisture. The Pakistan heavy rain events were amplified and sustained by strong anomalous southeasterly flow along the Himalayan foothills and abundant moisture transport from the Bay of Bengal in connection with the northward propagation of the monsoonal intraseasonal oscillation.
For decades, deep cumulus convection was viewed as consisting partly of undilute plumes that do not interact with their surrounding environment in order to explain their observed tendency to reach or penetrate the tropical tropopause. This behavior was built into all cumulus parameterizations used in terrestrial global climate and numerical weather prediction models, and it still persists in some models today. In the past decade, though, some embarrassing failures of global models have come to light, notably their tendency to rain over land near noon rather than in late afternoon or evening as observed, and the absence in the models of the Madden-Julian Oscillation (MJO), the major source of intraseasonal (30-90 day) precipitation variability in the Indian Ocean, West Pacific, and surrounding continental regions. In the past decade it has become clear that an important missing component of parameterizations is strong turbulent entrainment of drier environmental air into cumulus updrafts, which reduces the buoyancy of the updrafts and thus limits their vertical development. Tropospheric humidity thus serves as a throttle on convective penetration to high altitudes and delays the convective response to large-scale destabilizing influences in the environment.
Hidden Markov models can be used to investigate structure of subseasonal variability. East African short rain variability has connections to large-scale tropical variability. MJO - Intraseasonal variations connected with appearance of "wet" and "dry" states. ENSO/IOZM SST and circulation anomalies are apparent during years of anomalous residence time in the subseasonal "wet" state. Similar results found in previous studies, but we can interpret this with respect to variations of subseasonal wet and dry modes. Reveal underlying connections between MJO/IOZM/ENSO with respect to East African rainfall.
The recent US National Academies report "Assessment of Intraseasonal to Interannual Climate Prediction and Predictability" was unequivocal in recommending the need for the development of a North American Multi-Model Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multi-model ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation, and has proven to produce better prediction quality (on average) then any single model ensemble. This multi-model approach is the basis for several international collaborative prediction research efforts, an operational European system and there are numerous examples of how this multi-model ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test Bed (CTB) NMME workshops (February 18, and April 8, 2011) a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data is readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (http://origin.cpc.ncep.noaa.gov/products/people/wd51yf/NMME/index.html). Moreover, the NMME forecast are already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, presents an overview of the multi-model forecast quality, and the complementary skill associated with individual models.
The Madden-Julian Oscillation (MJO) represents a prominent mode of intraseasonal tropical variability. It is manifest by coherent large-scale changes in atmospheric circulation, convection, and thermodynamic processes. Preconditioning of the environment prior to the active phase of the MJO has been noted, but the balance of theorized mechanisms to accomplish this process remains unresolved. Further, there is a lack of consensus on the means by which primary initiation of an MJO event occurs. Observational and modeling efforts have recently been undertaken to advance our understanding of the physical underpinnings governing MJO development. However these intensive studies are often limited in space and/or time and are potentially subject to model deficiencies. Satellite observations, especially those providing vertical resolution of temperature and moisture, provide an opportunity to expand our knowledge of processes critical to MJO initiation and preconditioning. This work will provide an analysis of suppressed phase thermodynamics with an emphasis on the use of a complementary suite of satellite observations including AIRS/AMSU-A profiles, CERES radiative fluxes, and cloud properties observed by MODIS. Emphasis of this work will regard the distribution of cloud regimes, their radiative-convective effects, and their relationship to moist static energy during the recharge and suppressed stages of MJO initiation and eastward propagation. The analyses will make use of cloud regimes from MODIS observations to provide a compositing technique that enables the identification of systematic connections between different cloud regimes and the larger scale environment. Within these cloud regimes, the relationship between the associated cloud-radiative effects observed by CERES, vertically-resolved and vertically-integrated thermodynamics using AIRS/AMSU-A observations, and atmospheric boundary layer fluxes will be demonstrated.
South Asia encompasses a wide and highly varied geographic region, and includes climate zones ranging from the mountainous Himalayan territory to the tropical lowland and coastal zones along alluvial floodplains. The region's climate is dominated by a monsoonal circulation that heralds the arrival of seasonal rainfall, upon which much of the regional agriculture relies. The spatial and temporal distribution of this rainfall is, however, not uniform over the region. Northern South Asia, central India, and the west coast receive much of their rainfall during the southwest monsoon season, between June and September. These rains partly result from the moisture transport accompanying the monsoonal winds, which move in the southwesterly direction from the equatorial Indian Ocean. Regions further south, such as south/southeast India and Sri Lanka, may receive rains from both the southwest monsoon, and also during the northeast monsoon season between October and December (with northeasterly monsoon wind flow and moisture flux), which results in a bi- or multi-modal rainfall distribution. In addition, rainfall across South Asia displays a large amount of intraseasonal and interannual variability. Interannual variability is influenced by many drivers, both natural (e.g., El Ni-Southern Oscillation; ENSO) and man-made (e.g., rising temperatures due to increasing greenhouse gas concentrations), and it is challenging to obtaining accurate time-series of annual rainfall, even amongst various observed data products, which display inconsistencies amongst themselves. These climatic and rainfall variations can further complicate South Asia's agricultural and water management. Agriculture employs at least 65 of the workforce in most South Asian countries, and nearly 80 of South Asia's poor inhabit rural areas. Understanding the response of current agricultural production to climate variability and future climate change is of utmost importance in securing food and livelihoods for South Asia's growing population. In order to assess the future of food and livelihood security across South Asia, the Agricultural Model Intercomparison and Improvement Project (AgMIP) has undertaken integrated climate-crop-economic assessments of the impact of climate change on food security and poverty in South Asia, encompassing Bangladesh, India, Nepal, Pakistan, and Sri Lanka. AgMIP has funded, on a competitive basis, four South Asian regional research teams (RRTs) and one South Asian coordination team (CT) to undertake climate-crop-economic integrated assessments of food security for many districts in each of these countries, with the goal of characterizing the state of food security and poverty across the region, and projecting how these are subject to change under future climate change conditions.
This study examines the veracity of the tropical cyclone (TC) statistics estimated from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) global atmospheric reanalysis, focusing on the climatological-mean genesis regions, tracks and their lifetime maximum intensity, as well as the interannual and intraseasonal variations in TC activity. The results are validated against the International Best Track Archive for Climate Stewardship (IBTrACS) data for 1980-2016. MERRA-2 represents the spatial distribution of the TC genesis location and the tracks realistically well over all main development regions (MDRs), but with notable discrepancies including too weak TC activity over the Arabian Sea and the South China Sea and too strong activity over the North Indian Ocean. Over the western North Pacific and the North Atlantic, MERRA-2 underestimates recurving TCs steered by background westerlies in the mid-latitudes and thereby exaggerates northwestward moving TCs, resulting in an overestimate of the landfall probability in East Asia and North America. Excessive development of TCs over the Bay of Bengal also tends to exaggerate the landfall probability in India. In spite of the discrepancies in the annual TC number, the seasonal variation of TC genesis is realistic in MERRA-2. MERRA-2 also captures the TC intensity relationship between the minimum pressure and the maximum surface wind speed at the mature stage, although the maximum intensity is weaker than in the observations. While MERRA-2 tends to reproduce the interannual variations of the observed TC number and the power dissipation index (PDI), the level of accuracy depends on the region. MERRA-2 describes the changes in the TC genesis region and tracks realistically according to the different phases of El Nino and the Southern Oscillation (ENSO) and the Madden-Julian Oscillation (MJO), although it is less realistic over the North Indian Ocean.
Observational studies show a strong connection between the intraseasonal Madden-Julian oscillation (MJO) and the stratospheric quasi-biennial oscillation (QBO): the boreal winter MJO is stronger, more predictable, and has different teleconnections when the QBO in the lower stratosphere is easterly versus westerly. Despite the strength of the observed connection, global climate models do not produce an MJO-QBO link. Here the authors use a current-generation ocean-atmosphere coupled NASA Goddard Institute for Space Studies global climate model (Model E2.1) to examine the MJO-QBO link. To represent the QBO with minimal bias, the model zonal mean stratospheric zonal and meridional winds are relaxed to reanalysis fields from 1980-2017. The model troposphere, including the MJO, is allowed to freely evolve. The model with stratospheric nudging captures QBO signals well, including QBO temperature anomalies. However, an ensemble of nudged simulations still lacks an MJO-QBO connection.
The TROPOspheric Monitoring Instrument (TROPOMI) is used to derive top-down NOXemissions for twolarge power plants and three megacities in North America. Wefirst re-process the vertical column NO2with an improved airmass factor to correct for a known systematic low bias in the operational retrieval near urban centers. For the two power plants,top-down NOXemissions agree to within 10% of the emissions reported by the power plants. We then derive top-down NOXemissions rates for New York City, Chicago, and Toronto, and compare them to projected bottom-up emissions inventories. Inthis analysis of 2018 NOXemissions, wefind a +22% overestimate for New York City, a−21% underestimate in Toronto, andgood agreement in Chicago in the projected bottom-up inventories when compared to the top-down emissions. Top-downNOXemissions also capture intraseasonal variability, such as the weekday versus weekend effect (emissions are +45% larger onweekdays versus weekends in Chicago). Finally, we demonstrate the enhanced capabilities of TROPOMI, which allow us toderive a NOXemissions rate for Chicago using a single overpass on July 7, 2018. The large signal-to-noise ratio of TROPOMI iswell-suited for estimating NOXemissions from relatively small sources and for sub-seasonal timeframes.
The NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex) employed the NASA P-3, Stratton Park Engineering Company (SPEC) Learjet 35, and a host of satellites and surface sensors to characterize the coupling of aerosol processes, cloud physics, and atmospheric radiation within the Maritime Continent’s complex southwest monsoonal environment. Conducted in the late summer of 2019 from Luzon Philippines in conjunction with the Office of Naval Research Propagation of Intraseasonal Tropical OscillatioNs (PISTON) experiment with its R/VSally Ride stationed in the North Western Tropical Pacific, CAMP2Ex documented diverse biomass burning, industrial and natural aerosol populations and their interactions with small to congestus convection. The 2019 season exhibited El Nino and associated drought, high biomass burning emissions, and an early monsoon transition allowing for observation of pristine to massively polluted environments as they advected through intricate diurnal mesoscale and radiative environments into the monsoonal trough. CAMP2Ex’s preliminary results indicate 1) increasing aerosol loadings tend to invigorate congestus convection in height and increase liquid water paths; 2) lidar, polarimetry, and geostationary Advanced Himawari Imager remote sensing sensors have skill in quantifying diverse aerosol and cloud properties and their interaction; and 3) high resolution remote sensing technologies are able to greatly improve our ability to evaluate the radiation budget in complex cloud systems. Through the development of innovative informatics technologies, CAMP2Ex provides a benchmark dataset of an environment of extremes for the study of aerosol, cloud and radiation processes as well as a crucible for the design of future observing systems.