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Obesity is a public health crisis, presenting a huge burden on health care and the economic system in both developed and developing countries. According to the WHO’s latest report on obesity, 39% of adults of age 18 and above are obese, with an increase of 18% compared to the last few decades. Metabolic energy imbalance due to contemporary lifestyle, changes in gut microbiota, hormonal imbalance, inherent genetics, and epigenetics is a major contributory factor to this crisis. Multiple studies have shown that probiotics and their metabolites (postbiotics) supplementation have an effect on obesity-related effects in vitro, in vivo, and in human clinical investigations. Postbiotics such as the SCFAs suppress obesity by regulating metabolic hormones such as GLP-1, and PPY thus reducing feed intake and suppressing appetite. Furthermore, muramyl di-peptides, bacteriocins, and LPS have been tested against obesity and yielded promising results in both human and mice studies. These insights provide an overview of targetable pharmacological sites and explore new opportunities for the safer use of postbiotics against obesity in the future. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the importance of multiple county-level features in the trajectory of COVID-19. We examined feature importance across 2787 counties in the United States using data-driven machine learning models. Existing mathematical models of disease spread usually focused on the case prediction with different infection rates without incorporating multiple heterogeneous features that could impact the spatial and temporal trajectory of COVID-19. Recognizing this, we trained a data-driven model using 23 features representing six key influencing factors affecting the pandemic spread: social demographics of counties, population activities, mobility within the counties, movement across counties, disease attributes, and social network structure. Also, we categorized counties into multiple groups according to their population densities, and we divided the trajectory of COVID-19 into three stages: the outbreak stage, the social distancing stage, and the reopening stage. The study aimed to answer two research questions: (1) The extent to which the importance of heterogeneous features evolved at different stages; (2) The extent to which the importance of heterogeneous features varied across counties with different characteristics. We fitted a set of random forest models to determine weekly feature importance. The results showed that: (1) Social demographic features, such as gross domestic product, population density, and minority status maintained high-importance features throughout stages of COVID-19 across 2787 studied counties; (2) Within-county mobility features had the highest importance in counties with higher population densities; (3) The feature reflecting the social network structure (Facebook, social connectedness index), had higher importance for counties with higher population densities. The results showed that the data-driven machine learning models could provide important insights to inform policymakers regarding feature importance for counties with various population densities and at different stages of a pandemic life cycle. [ABSTRACT FROM AUTHOR]

This study investigates the impact of different specification of the underlying sea surface temperature (SST) on the prediction of intraseasonal rainfall variation associated with strong Monsoon Intraseasonal Oscillation (MISO) events in the northern Indian Ocean. A series of forecast experiments forced with observed hourly, daily, or seasonal SSTs are performed for three selected strong MISO events using the National Centers for Environmental Predictions (NCEP) atmospheric Global Forecast System (GFS). The comparison between these GFS forecasts shows that the intraseasonal SST variability is more important than its diurnal variability in the MISO prediction. The GFS experiments forced with daily SST which includes intraseasonal variability has higher prediction skill and faster speed in the northward propagation of the MISO intraseasonal rainfall anomalies than those forced with seasonal SST that do not include intraseasonal variability. No significant difference is found in the MISO prediction when GFS was forced by SST with or without SST diurnal cycle. The GFS runs forced with warmer and colder seasonal SSTs which mimic possible biases in SST prediction have comparable skill in the MISO prediction. A modified version of the NCEP Climate Forecast System coupled model (CFSm5) with 1- and 10-m vertical resolutions in the upper ocean is then used to examine their performance in the MISO prediction when all aspects of SST are actively included. The CFSm5 with 1-m vertical resolution in the upper ocean (CFSm501) shows larger amplitude of intraseasonal SST anomaly, with higher prediction skill in both intraseasonal SST and rainfall than the CFSm5 with the typical 10-m vertical resolution in the upper ocean (CFSm510) does. Compared with the uncoupled GFS, both CFSm501 and CFSm510, despite errors in predicted SSTs, have better prediction skill and more reasonable rainfall variability, which is attributed to the inclusion of active air–sea interaction. These results suggest the importance of intraseasonal variability of SST and air–sea interaction in improving the intraseasonal rainfall prediction associated with the MISO. [ABSTRACT FROM AUTHOR]

Oxygen vacancies play an essential role in the sensing mechanism of semiconductor gas sensors, determining their performance characteristics. To investigate the behavior of oxygen vacancies during a cooling process, a diffusion equation was established based on a model of gradient-distributed oxygen vacancies. Numerical solutions of the diffusion equation were obtained by using a numerical analysis method. The results showed that the oxygen vacancy distribution profile as well as the sensor resistance and response to reducing gas depend on the temperature interval defined by the initial and final temperature of the cooling process. The properties of the sensor can thus be controlled by adjusting the cooling temperature during the fabrication process. [ABSTRACT FROM AUTHOR]

The boreal-summer MJOs exhibit three distinctive downstream evolutions on their passages over the Maritime Continent (MC): (i) smoothly transitioning from Indian Ocean (IO) to western Pacific (type-I); (ii) rapidly decayed over the MC (type-II); and (iii) significantly intensified over the MC (type-III). During the life cycles of type-I and -III MJOs, basin-wide overturning circulations connect the convective dipoles between western and eastern north Pacific. For type-II MJO, regional overturning circulations connect the convective dipoles between the IO and Western North Pacific (WNP). For type-I and -III MJOs, the associated convection over eastern IO is able to generate strong Kevin-wave responses and significant positive SST anomalies over the MC and western Pacific. On the other hand, for type-II MJO in the IO, a persistent convection lingers around the WNP. It intensifies westerly monsoon flows, thus preventing the generation of coherent positive SST anomalies ahead of the IO MJO convection. It also blocks the formation of a robust Kelvin-wave response over the MC and western Pacific by delaying the northeastward migrations of the dry phase. The convection of type-I and -III MJOs at eastern IO is able to generate positive boundary-layer moisture anomalies along with positive column-integrated moisture tendencies over the MC and western Pacific, thus allowing them to propagate eastward smoothly. On the other hand, the type-II MJO convection in eastern IO is associated with a very dry troposphere and negative column-integrated moisture tendency over western Pacific, which result in the rapid decay of convection over the MC. These results emphasize the important roles of robust Kelvin-wave responses and positive SST anomalies on the eastward propagation of boreal-summer MJOs over the MC through moistening the troposphere ahead of the convection. [ABSTRACT FROM AUTHOR]

Coupled prediction systems for seasonal and inter-annual variability in the tropical Pacific are initialized from ocean analyses. In ocean initial states, small scale perturbations are inevitably smoothed or distorted by the observational limits and data assimilation procedures, which tends to induce potential ocean initial errors for the El Nino-Southern Oscillation (ENSO) prediction. Here, the evolution and effects of ocean initial errors from the small scale perturbation on the developing phase of ENSO are investigated by an ensemble of coupled model predictions. Results show that the ocean initial errors at the thermocline in the western tropical Pacific grow rapidly to project on the first mode of equatorial Kelvin wave and propagate to the east along the thermocline. In boreal spring when the surface buoyancy flux weakens in the eastern tropical Pacific, the subsurface errors influence sea surface temperature variability and would account for the seasonal dependence of prediction skill in the NINO3 region. It is concluded that the ENSO prediction in the eastern tropical Pacific after boreal spring can be improved by increasing the observational accuracy of subsurface ocean initial states in the western tropical Pacific. [ABSTRACT FROM AUTHOR]

Conflicting results have been presented regarding the link between Arctic sea-ice loss and midlatitude cooling, particularly over Eurasia. This study analyzes uncoupled (atmosphere-only) and coupled (ocean-atmosphere) simulations by the Climate Forecast System, version 2 (CFSv2), to examine this linkage during the Northern Hemisphere winter, focusing on the simulation of the observed surface cooling trend over Eurasia during the last three decades. The uncoupled simulations are Atmospheric Model Intercomparison Project (AMIP) runs forced with mean seasonal cycles of sea surface temperature (SST) and sea ice, using combinations of SST and sea ice from different time periods to assess the role that each plays individually, and to assess the role of atmospheric internal variability. Coupled runs are used to further investigate the role of internal variability via the analysis of initialized predictions and the evolution of the forecast with lead time. The AMIP simulations show a mean warming response over Eurasia due to SST changes, but little response to changes in sea ice. Individual runs simulate cooler periods over Eurasia, and this is shown to be concurrent with a stronger Siberian high and warming over Greenland. No substantial differences in the variability of Eurasian surface temperatures are found between the different model configurations. In the coupled runs, the region of significant warming over Eurasia is small at short leads, but increases at longer leads. It is concluded that, although the models have some capability in highlighting the temperature variability over Eurasia, the observed cooling may still be a consequence of internal variability. [ABSTRACT FROM AUTHOR]

The capability of a numerical model to simulate the statistical characteristics of the summer sea ice date of retreat (DOR) and the winter date of advance (DOA) is investigated using sea ice concentration output from the Climate Forecast System Version 2 model (CFSv2). Two model configurations are tested, the operational setting (CFSv2CFSR) which uses initial data from the Climate Forecast System Reanalysis, and a modified version (CFSv2PIOMp) which ingests sea ice thickness initialization data from the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) and includes physics modifications for a more realistic representation of heat fluxes at the sea ice top and bottom. First, a method to define DOR and DOA is presented. Then, DOR and DOA are determined from the model simulations and observational sea ice concentration from the National Aeronautics and Space Administration (NASA). Means, trends, and detrended standard deviations of DOR and DOA are compared, along with DOR/DOA rates in the Arctic Ocean. It is found that the statistics are generally similar between the model and observations, although some regional biases exist. In addition, regions of new ice retreat in recent years are represented well in CFSv2PIOMp over the Arctic Ocean, in terms of both spatial extent and timing. Overall, CFSv2PIOMp shows a reduction in error throughout the Arctic. Based on results, it is concluded that the model produces a reasonable representation of the climatology and variability statistics of DOR and DOA in most regions. This assessment serves as a prerequisite for future predictability experiments. [ABSTRACT FROM AUTHOR]

In this work, we examine the connection of vertical temperature gradient of the tropospheric atmosphere along the equator with El Niño-Southern Oscillation (ENSO) and the possible impact of the long-term change of the gradient. It is suggested that when the temperature anomalies in the lower troposphere are relatively warmer (cooler) than in the upper troposphere, the atmosphere is less (more) stable and favors an El Niño (a La Niña) event to develop. ENSO evolutions in 1997-1998 and 2014-2015 events are good examples of this relationship. They started from similar ocean anomaly states in the springs of 1997 and 2014, but developed into an extreme El Niño in 1997-1998 and a borderline El Niño in 2014-2015. That may be partially due to differences in the evolutions of the vertical temperature anomaly gradient in troposphere. Thus, in addition to the significant atmospheric response to ENSO, the preconditioning of vertical gradient of the tropospheric temperature due to internal atmospheric processes to some extent may play an active role in affecting ENSO evolution. The long-term trend with more pronounced warming in the upper troposphere than in the lower troposphere causes a reduction in the vertical temperature gradient in the troposphere. Moreover, unlike almost homogenous warm anomalies in the upper troposphere, the lower troposphere shows remarkable regional features along the equator during 1979-2014, with cold anomaly trends over the central and eastern Pacific Ocean associated with the so-called hiatus and some warm anomalies on its two sides in the east and west. This vertical and zonal distribution of the air temperature trends in the troposphere over the Pacific Ocean is consistent with the convection suppression over the central Pacific since 2000, implying a weakening of atmosphere and ocean coupling. [ABSTRACT FROM AUTHOR]

The present study examined the major features of the interdecadal variation of the summer rainfall over eastern China (IVRC) and the possible association with sea surface temperature (SST). We noted that the first leading mode of IVRC (accounting for nearly half of the total variance and with maximum loading for the summer rainfall anomalies over South China) may be not forced by SST. On the other hand, the second and third leading modes [accounting for 17.1 and 13.6 % of the total variance and mainly associated with the summer rainfall anomalies over the Yangtze River valley (YRV) and North China, respectively] in some extent are forced by SST anomalies. These observational results are confirmed by atmospheric general circulation model (AGCM) simulations forced by observed SST. By eliminating the internal dynamical process driven rainfall though ensemble mean, the simulations further suggest an overall enhancement of the intensity of IVRC in the corresponding ensemble mean, especially in the YRV and North China regions, but not in South China. That implies the different role of SST in driving IVRC over different regions. [ABSTRACT FROM AUTHOR]

In this paper we analyze the multi-week prediction bias and skill from the National Centers for Environment Prediction (NCEP) Climate Forecast System version 2 (CFSv2) based on its hindcasts for 1999-2012. The analyses focus on the prediction of the rainfall variability over South-East Asia during boreal warm seasons and the dependence of the prediction on the activity of intrasesaonal leading modes. It is shown that the prediction skill measured by anomaly correlation is comparable between the total anomalies and intraseasonal anomalies during the first 2 weeks. After week 2, the prediction skill drops substantially and the skill for total anomalies is largely from the prediction for the interannual variability. Moreover, the forecast skill tends to be higher when the amplitude of the Madden-Julian Oscillation and the Boreal Summer Intraseasonal Oscillation (BSISO) is larger, especially for the BSISO. It is noted that the prediction skill over South-East Asia depends on the phase of the BSISO. One deficiency in the CFSv2 is that the northward propagation of the forecast BSISO is generally slower than the observed. [ABSTRACT FROM AUTHOR]

In this study, reasons for spatial variability in prediction skill of seasonal mean sea surface temperature (SST) are investigated. The analysis explores the connection between the spatial variation of SST prediction skill in tropical latitudes and local simultaneous correlation between observed SST and precipitation (SST-P). The results show that high (low) SST prediction skills and slow (fast) decay in skill with lead time are generally collocated with large positive (weakly positive or negative) SST-P correlations. The reasons for spatial variation in SST-P correlation can be explained by whether the primary forcing is from the ocean to the atmosphere or vice versa. Over regions where the ocean is generally known to force the atmosphere, it is found that SST-P correlation has large positive values. Over regions where the atmosphere forces ocean, SST-P correlation is weak due to intrinsically unpredictable nature of atmospheric variability. The physical explanation for spatial variation in SST-P correlation, and apparent link between the spatial variations in SST-P correlation and spatial variations in skill of SST predictions also establishes a physical basis for the latter. As a corollary, low SST prediction skill in coupled forecast models over certain geographical regions may be due to the inherent limits on predictability in addition to the contribution from model biases or initialization errors. [ABSTRACT FROM AUTHOR]

The Madden-Julian Oscillation (MJO) is the primary mode of tropical intraseasonal climate variability and has significant modulation of global climate variations and attendant societal impacts. Advancing prediction of the MJO using state of the art observational data and modeling systems is thus a necessary goal for improving global intraseasonal climate prediction. MJO prediction is assessed in the NOAA Climate Forecast System version 2 (CFSv2) based on its hindcasts initialized daily for 1999-2010. The analysis focuses on MJO indices taken as the principal components of the two leading EOFs of combined 15°S-15°N average of 200-hPa zonal wind, 850-hPa zonal wind and outgoing longwave radiation at the top of the atmosphere. The CFSv2 has useful MJO prediction skill out to 20 days at which the bivariate anomaly correlation coefficient (ACC) drops to 0.5 and root-mean-square error (RMSE) increases to the level of the prediction with climatology. The prediction skill also shows a seasonal variation with the lowest ACC during the boreal summer and highest ACC during boreal winter. The prediction skills are evaluated according to the target as well as initial phases. Within the lead time of 10 days the ACC is generally greater than 0.8 and RMSE is less than 1 for all initial and target phases. At longer lead time, the model shows lower skills for predicting enhanced convection over the Maritime Continent and from the eastern Pacific to western Indian Ocean. The prediction skills are relatively higher for target phases when enhanced convection is in the central Indian Ocean and the central Pacific. While the MJO prediction skills are improved in CFSv2 compared to its previous version, systematic errors still exist in the CFSv2 in the maintenance and propagation of the MJO including (1) the MJO amplitude in the CFSv2 drops dramatically at the beginning of the prediction and remains weaker than the observed during the target period and (2) the propagation in the CFSv2 is too slow. Reducing these errors will be necessary for further improvement of the MJO prediction. [ABSTRACT FROM AUTHOR]

The present study assesses the forecast skill of the Madden-Julian Oscillation (MJO) observed during the period of DYNAMO (Dynamics of the MJO)/CINDY (Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011) field campaign in the GFS (NCEP Global Forecast System), CFSv2 (NCEP Climate Forecast System version 2) and UH (University of Hawaii) models, and revealed their strength and weakness in forecasting initiation and propagation of the MJO. Overall, the models forecast better the successive MJO which follows the preceding event than that with no preceding event (primary MJO). The common modeling problems include too slow eastward propagation, the Maritime Continent barrier and weak intensity. The forecasting skills of MJO major modes reach 13, 25 and 28 days, respectively, in the GFS atmosphere-only model, the CFSv2 and UH coupled models. An equal-weighted multi-model ensemble with the CFSv2 and UH models reaches 36 days. Air-sea coupling plays an important role for initiation and propagation of the MJO and largely accounts for the skill difference between the GFS and CFSv2. A series of forecasting experiments by forcing UH model with persistent, forecasted and observed daily SST further demonstrate that: (1) air-sea coupling extends MJO skill by about 1 week; (2) atmosphere-only forecasts driven by forecasted daily SST have a similar skill as the coupled forecasts, which suggests that if the high- resolution GFS is forced with CFSv2 forecasted daily SST, its forecast skill can be much higher than its current level as forced with persistent SST; (3) atmosphere-only forecasts driven by observed daily SST reaches beyond 40 days. It is also found that the MJO-TC (Tropical Cyclone) interactions have been much better represented in the UH and CFSv2 models than that in the GFS model. Both the CFSv2 and UH coupled models reasonably well capture the development of westerly wind bursts associated with November 2011 MJO and the cyclogenesis of TC05A in the Indian Ocean with a lead time of 2 weeks. However, the high-resolution GFS atmosphere-only model fails to reproduce the November MJO and the genesis of TC05A at 2 weeks' lead. This result highlights the necessity to get MJO right in order to ensure skillful extended-range TC forecasting. [ABSTRACT FROM AUTHOR]

The influence of El Niño-Southern Oscillation (ENSO) on the convectively coupled Kelvin waves over the tropical Pacific is investigated by comparing the Kelvin wave activity in the eastern Pacific (EP) El Niño, central Pacific (CP) El Niño, and La Niña years, respectively, to 30-yr (1982-2011) mean statistics. The convectively coupled Kelvin waves in this study are represented by the two leading modes of empirical orthogonal function (EOF) of 2-25-day band-pass filtered daily outgoing longwave radiation (OLR), with the estimated zonal wavenumber of 3 or 4, period of 8 days, and eastward propagating speed of 17 m s. The most significant impact of ENSO on the Kelvin wave activity is the intensification of the Kelvin waves during the EP El Niños. The impact of La Niña on the reduction of the Kelvin wave intensity is relatively weaker, reflecting the nonlinearity of tropical deep convection and the associated Kelvin waves in response to ENSO sea surface temperature (SST) anomalies. The impact of the CP El Niño on the Kelvin waves is less significant due to relatively weaker SST anomalies and smaller spatial coverage. ENSO may also alter the frequency, wavelength, and phase speed of the Kelvin waves. This study demonstrates that low-frequency ENSO SST anomalies modulate high-frequency tropical disturbances, an example of weather-climate linkage. [ABSTRACT FROM AUTHOR]

This work evaluates the skill of retrospective predictions of the second version of the NCEP Climate Forecast System (CFSv2) for the North Atlantic sea surface temperature (SST) and investigates the influence of El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) on the prediction skill over this region. It is shown that the CFSv2 prediction skill with 0-8 month lead displays a 'tripole'-like pattern with areas of higher skills in the high latitude and tropical North Atlantic, surrounding the area of lower skills in the mid-latitude western North Atlantic. This 'tripole'-like prediction skill pattern is mainly due to the persistency of SST anomalies (SSTAs), which is related to the influence of ENSO and NAO over the North Atlantic. The influences of ENSO and NAO, and their seasonality, result in the prediction skill in the tropical North Atlantic the highest in spring and the lowest in summer. In CFSv2, the ENSO influence over the North Atlantic is overestimated but the impact of NAO over the North Atlantic is not well simulated. However, compared with CFSv1, the overall skills of CFSv2 are slightly higher over the whole North Atlantic, particularly in the high latitudes and the northwest North Atlantic. The model prediction skill beyond the persistency initially presents in the mid-latitudes of the North Atlantic and extends to the low latitudes with time. That might suggest that the model captures the associated air-sea interaction in the North Atlantic. The CFSv2 prediction is less skillful than that of SSTA persistency in the high latitudes, implying that over this region the persistency is even better than CFSv2 predictions. Also, both persistent and CFSv2 predictions have relatively low skills along the Gulf Stream. [ABSTRACT FROM AUTHOR]

The predictable patterns and predictive skills of monsoon precipitation in the Northern Hemisphere summer (June-July-August) are examined using reforecasts (1983-2010) from the National Center for Environmental Prediction Climate Forecast System version 2 (CFSv2). The possible connections of these predictable patterns with global sea surface temperature (SST) are investigated. The empirical orthogonal function analysis with maximized signal-to-noise ratio is used to isolate the predictable patterns of the precipitation for three regional monsoons: the Asian and Indo-Pacific monsoon (AIPM), the Africa monsoon (AFM), and the North America monsoon (NAM). Overall, the CFSv2 well predicts the monsoon precipitation patterns associated with El Niño-South Oscillation (ENSO) due to its good prediction skill for ENSO. For AIPM, two identified predictable patterns are an equatorial dipole pattern characterized by opposite variations between the equatorial western Pacific and eastern Indian Ocean, and a tropical western Pacific pattern characterized by opposite variations over the tropical northwestern Pacific and the Philippines and over the regions to its west, north, and southeast. For NAM, the predictable patterns are a tropical eastern Pacific pattern with opposite variations in the tropical eastern Pacific and in Mexico, the Guyana Plateau and the equatorial Atlantic, and a Central American pattern with opposite variations in the eastern Pacific and the North Atlantic and in the Amazon Plains. The CFSv2 can predict these patterns at least 5 months in advance. However, compared with the good skill in predicting AIPM and NAM precipitation patterns, the CFSv2 exhibits little predictive skill for AFM precipitation, probably because the variability of the tropical Atlantic SST plays a more important than ENSO in the AFM precipitation variation and the prediction skill is lower for the tropical Atlantic SST than the tropical Pacific SST. [ABSTRACT FROM AUTHOR]

During boreal summer, convective activity over the eastern Pacific (EPAC) inter-tropical convergence zone (ITCZ) exhibits vigorous intraseasonal variability (ISV). Previous observational studies identified two dominant ISV modes over the EPAC, i.e., a 40-day mode and a quasi-biweekly mode (QBM). The 40-day ISV mode is generally considered a local expression of the Madden-Julian Oscillation. However, in addition to the eastward propagation, northward propagation of the 40-day mode is also evident. The QBM mode bears a smaller spatial scale than the 40-day mode, and is largely characterized by northward propagation. While the ISV over the EPAC exerts significant influences on regional climate/weather systems, investigation of contemporary model capabilities in representing these ISV modes over the EPAC is limited. In this study, the model fidelity in representing these two dominant ISV modes over the EPAC is assessed by analyzing six atmospheric and three coupled general circulation models (GCMs), including one super-parameterized GCM (SPCAM) and one recently developed high-resolution GCM (GFDL HIRAM) with horizontal resolution of about 50 km. While it remains challenging for GCMs to faithfully represent these two ISV modes including their amplitude, evolution patterns, and periodicities, encouraging simulations are also noted. In general, SPCAM and HIRAM exhibit relatively superior skill in representing the two ISV modes over the EPAC. While the advantage of SPCAM is achieved through explicit representation of the cumulus process by the embedded 2-D cloud resolving models, the improved representation in HIRAM could be ascribed to the employment of a strongly entraining plume cumulus scheme, which inhibits the deep convection, and thus effectively enhances the stratiform rainfall. The sensitivity tests based on HIRAM also suggest that fine horizontal resolution could also be conducive to realistically capture the ISV over the EPAC, particularly for the QBM mode. Further analysis illustrates that the observed 40-day ISV mode over the EPAC is closely linked to the eastward propagating ISV signals from the Indian Ocean/Western Pacific, which is in agreement with the general impression that the 40-day ISV mode over the EPAC could be a local expression of the global Madden-Julian Oscillation (MJO). In contrast, the convective signals associated with the 40-day mode over the EPAC in most of the GCM simulations tend to originate between 150°E and 150°W, suggesting the 40-day ISV mode over the EPAC might be sustained without the forcing by the eastward propagating MJO. Further investigation is warranted towards improved understanding of the origin of the ISV over the EPAC. [ABSTRACT FROM AUTHOR]

The mechanisms controlling the El Niño have been studied by analyzing mixed layer heat budget of daily outputs from a free coupled simulation with the Climate Forecast System (CFS). The CFS is operational at National Centers for Environmental Prediction, and is used by Climate Prediction Center for seasonal-to-interannual prediction, particularly for the prediction of the El Niño and Southern Oscillation (ENSO) in the tropical Pacific. Our analysis shows that the development and decay of El Niño can be attributed to ocean advection in which all three components contribute. Temperature advection associated with anomalous zonal current and mean vertical upwelling contributes to the El Niño during its entire evolutionary cycle in accordance with many observational, theoretical, and modeling studies. The impact of anomalous vertical current is found to be comparable to that of mean upwelling. Temperature advection associated with mean (anomalous) meridional current in the CFS also contributes to the El Niño cycle due to strong meridional gradient of anomalous (mean) temperature. The surface heat flux, non-linearity of temperature advection, and eddies associated with tropical instabilities waves (TIW) have the tendency to damp the El Niño. Possible degradation in the analysis and closure of the heat budget based on the monthly mean (instead of daily) data is also quantified. [ABSTRACT FROM AUTHOR]

While large-scale circulation fields from atmospheric reanalyses have been widely used to study the tropical intraseasonal variability, rainfall variations from the reanalyses are less focused. Because of the sparseness of in situ observations available in the tropics and strong coupling between convection and large-scale circulation, the accuracy of tropical rainfall from the reanalyses not only measures the quality of reanalysis rainfall but is also to some extent indicative of the accuracy of the circulations fields. This study analyzes tropical intraseasonal rainfall variability in the recently completed NCEP Climate Forecast System Reanalysis (CFSR) and its comparison with the widely used NCEP/NCAR reanalysis (R1) and NCEP/DOE reanalysis (R2). The R1 produces too weak rainfall variability while the R2 generates too strong westward propagation. Compared with the R1 and R2, the CFSR produces greatly improved tropical intraseasonal rainfall variability with the dominance of eastward propagation and more realistic amplitude. An analysis of the relationship between rainfall and large-scale fields using composites based on Madden-Julian Oscillation (MJO) events shows that, in all three NCEP reanalyses, the moisture convergence leading the rainfall maximum is near the surface in the western Pacific but is above 925 hPa in the eastern Indian Ocean. However, the CFSR produces the strongest large-scale convergence and the rainfall from CFSR lags the column integrated precipitable water by 1 or 2 days while R1 and R2 rainfall tends to lead the respective precipitable water. Diabatic heating related to the MJO variability in the CFSR is analyzed and compared with that derived from large-scale fields. It is found that the amplitude of CFSR-produced total heating anomalies is smaller than that of the derived. Rainfall variability from the other two recently produced reanalyses, the ECMWF Re-Analysis Interim (ERAI), and the Modern Era Retrospective-analysis for Research and Applications (MERRA), is also analyzed. It is shown that both the ERAI and MERRA generate stronger rainfall spectra than the R1 and more realistic dominance of eastward propagating variance than R2. The intraseasonal variability in the MERRA is stronger than that in the ERAI but weaker than that in the CFSR and CMORPH. [ABSTRACT FROM AUTHOR]

The differences in tropical air-sea interactions and global climate connection as well as the hindcast skills for the warm pool (WP) and cold tongue (CT) El Niños are investigated based on observed, (re)analyzed, and model hindcast data. The robustness of observed global climate connection is established from the model simulations. Lastly, variations of atmosphere and ocean conditions in the recent decades, and their possible connection with the frequency increase of the WP El Niño are discussed. Consistent with previous results, our individual case study and composite results suggest that stronger (weaker) and more eastward extended (westward confined) westerly wind along the equatorial Pacific in early months of a year is associated with active (suppressed) air-sea interaction over the cold tongue/the Intertropical Convergence Zone complex, as well as more (less) intensive oceanic thermocline feedback, favoring the CT (WP) El Niño development. The preceding westerly wind signal and air-sea interaction differences may be responsible for the predication skill difference with higher (lower) overall hindcast skill for the CT (WP) El Niño in the Climate Forecast System of National Centers for Environmental Prediction. Our model experiments show that, in addition to the tropics, the eastern Pacific, North America and North Atlantic are the major regions having robust climate differences between the CT and WP El Niños. Nevertheless, the climate contrasts seem not robust over the Eurasian continent. Also, the frequency increase of the WP El Niño in the recent decades may not be directly connected with the linear trend of the tropical climate. [ABSTRACT FROM AUTHOR]

This paper analyzes surface climate variability in the climate forecast system reanalysis (CFSR) recently completed at the National Centers for Environmental Prediction (NCEP). The CFSR represents a new generation of reanalysis effort with first guess from a coupled atmosphere-ocean-sea ice-land forecast system. This study focuses on the analysis of climate variability for a set of surface variables including precipitation, surface air 2-m temperature (T2m), and surface heat fluxes. None of these quantities are assimilated directly and thus an assessment of their variability provides an independent measure of the accuracy. The CFSR is compared with observational estimates and three previous reanalyses (the NCEP/NCAR reanalysis or R1, the NCEP/DOE reanalysis or R2, and the ERA40 produced by the European Centre for Medium-Range Weather Forecasts). The CFSR has improved time-mean precipitation distribution over various regions compared to the three previous reanalyses, leading to a better representation of freshwater flux (evaporation minus precipitation). For interannual variability, the CFSR shows improved precipitation correlation with observations over the Indian Ocean, Maritime Continent, and western Pacific. The T2m of the CFSR is superior to R1 and R2 with more realistic interannual variability and long-term trend. On the other hand, the CFSR overestimates downward solar radiation flux over the tropical Western Hemisphere warm pool, consistent with a negative cloudiness bias and a positive sea surface temperature bias. Meanwhile, the evaporative latent heat flux in CFSR appears to be larger than other observational estimates over most of the globe. A few deficiencies in the long-term variations are identified in the CFSR. Firstly, dramatic changes are found around 1998-2001 in the global average of a number of variables, possibly related to the changes in the assimilated satellite observations. Secondly, the use of multiple streams for the CFSR induces spurious jumps in soil moisture between adjacent streams. Thirdly, there is an inconsistency in long-term sea ice extent variations over the Arctic regions between the CFSR and other observations with the CFSR showing smaller sea ice extent before 1997 and larger extent starting in 1997. These deficiencies may have impacts on the application of the CFSR for climate diagnoses and predictions. Relationships between surface heat fluxes and SST tendency and between SST and precipitation are analyzed and compared with observational estimates and other reanalyses. Global mean fields of surface heat and water fluxes together with radiation fluxes at the top of the atmosphere are documented and presented over the entire globe, and for the ocean and land separately. [ABSTRACT FROM AUTHOR]

In this paper, lead-time and spatial dependence in skill for prediction of monthly mean climate variability is analyzed. The analysis is based on a set of extensive hindcasts from the Climate Forecast System at the National Centers for Environmental Prediction. The skill characteristics of initialized predictions is also compared with the AMIP simulations forced with the observed sea surface temperature (SST) to quantify the role of initial versus boundary conditions in the prediction of monthly means. The analysis is for prediction of monthly mean SST, precipitation, and 200-hPa height. The results show a rapid decay in skill with lead time for the atmospheric variables in the extratropical latitudes. Further, after a lead-time of approximately 30-40 days, the skill of monthly mean prediction is essentially a boundary forced problem, with SST anomalies in the tropical central/eastern Pacific playing a dominant role. Because of the larger contribution from the atmospheric internal variability to monthly time-averages (compared to seasonal averages), skill for monthly mean prediction associated with boundary forcing is also lower. The analysis indicates that the prospects of skillful prediction of monthly means may remain a challenging problem, and may be limited by inherent limits in predictability. [ABSTRACT FROM AUTHOR]

In this work, we examine the sensitivity of tropical mean climate and seasonal cycle to low clouds and cloud liquid water path (CLWP) by prescribing them in the NCEP climate forecast system (CFS). It is found that the change of low cloud cover alone has a minor influence on the amount of net shortwave radiation reaching the surface and on the warm biases in the southeastern Atlantic. In experiments where CLWP is prescribed using observations, the mean climate in the tropics is improved significantly, implying that shortwave radiation absorption by CLWP is mainly responsible for reducing the excessive surface net shortwave radiation over the southern oceans in the CFS. Corresponding to large CLWP values in the southeastern oceans, the model generates large low cloud amounts. That results in a reduction of net shortwave radiation at the ocean surface and the warm biases in the sea surface temperature in the southeastern oceans. Meanwhile, the cold tongue and associated surface wind stress in the eastern oceans become stronger and more realistic. As a consequence of the overall improvement of the tropical mean climate, the seasonal cycle in the tropical Atlantic is also improved. Based on the results from these sensitivity experiments, we propose a model bias correction approach, in which CLWP is prescribed only in the southeastern Atlantic by using observed annual mean climatology of CLWP. It is shown that the warm biases in the southeastern Atlantic are largely eliminated, and the seasonal cycle in the tropical Atlantic Ocean is significantly improved. Prescribing CLWP in the CFS is then an effective interim technique to reduce model biases and to improve the simulation of seasonal cycle in the tropics. [ABSTRACT FROM AUTHOR]

The status of the numerical reproduction of the Madden–Julian Oscillation (MJO) by current global models was assessed through diagnoses of four pairs of coupled and uncoupled simulations. Slow eastward propagation of the MJO, especially in low-level zonal wind, is realistic in all these simulations. However, the simulated MJO suffers from several common problems. The MJO signal in precipitation is generally too weak and often eroded by an unrealistic split of an equatorial maximum of precipitation into a double ITCZ structure over the western Pacific. The MJO signal in low-level zonal wind, on the other hand, is sometimes too strong over the eastern Pacific but too weak over the Indian Ocean. The observed phase relationship between precipitation and low-level zonal wind associated with the MJO in the western Pacific and their coherence in general are not reproduced by the models. The seasonal migration in latitude of MJO activity is missing in most simulations. Air–sea coupling generally strengthens the simulated eastward propagating signal, but its effects on the phase relationship and coherence between precipitation and low-level zonal wind, and on their geographic distributions, seasonal cycles, and interannual variability are inconsistent among the simulations. Such inconsistency cautions generalization of results from MJO simulations using a single model. In comparison to observations, biases in the simulated MJO appear to be related to biases in the background state of mean precipitation, low-level zonal wind, and boundary-layer moisture convergence. This study concludes that, while the realistic simulations of the eastward propagation of the MJO are encouraging, reproducing other fundamental features of the MJO by current global models remains an unmet challenge. [ABSTRACT FROM AUTHOR]