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The ability of the 5th phase of the Coupled Model Intercomparison Project (CMIP5) to reproduce twentieth-century climate trends over the seven CONUS regions of the National Climate Assessment is evaluated. This evaluation is carried out for summer and winter for three time periods, 1895–1939, 1940–1979, and 1980–2005. The evaluation includes all 206 CMIP5 historical simulations from 48 unique models and their multi-model ensemble (MME), as well as a gridded in situ dataset of surface air temperature and precipitation. Analysis is performed on both individual members and the MME, and considers reproducing the correct sign of the trends by the members as well as reproducing the trend values. While the MME exhibits some trend bias in most cases, it reproduces historical temperature trends with reasonable fidelity for summer for all time periods and all regions, including at the CONUS scale, except the Northern Great Plains from 1895 to 1939 and Southeast during 1980–2005. Likewise, for DJF, the MME reproduces historical temperature trends across all time periods over all regions, including at the CONUS scale, except the Southeast from 1895 to 1939 and the Midwest during 1940–1979. Model skill was highest across all of the seven regions during JJA and DJF for the 1980–2005 period. The quantitatively best result is seen during DJF in the Southwest region with at least 74% of the ensemble members correctly reproducing the observed trend across all of the time periods. No clear trends in MME precipitation were identified at these scales due to high model precipitation variability.

Cool season precipitation event characteristics are evaluated across a suite of downscaled climate models over the northeastern US. Downscaled hindcast simulations are produced by dynamically downscaling the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2) using the National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (WRF) regional climate model (RCM) and the Goddard Earth Observing System Model, Version 5 (GEOS-5) global climate model. NU-WRF RCM simulations are produced at 24, 12, and 4-km horizontal resolutions using a range of spectral nudging schemes while the MERRA2 global downscaled run is provided at 12.5-km. All model runs are evaluated using four metrics designed to capture key features of precipitation events: event frequency, event intensity, even total, and event duration. Overall, the downscaling approaches result in a reasonable representation of many of the key features of precipitation events over the region, however considerable biases exist in the magnitude of each metric. Based on this evaluation there is no clear indication that higher resolution simulations result in more realistic results in general, however many small-scale features such as orographic enhancement of precipitation are only captured at higher resolutions suggesting some added value over coarser resolution. While the differences between simulations produced using nudging and no nudging are small, there is some improvement in model fidelity when nudging is introduced, especially at a cutoff wavelength of 600 km compared to 2000 km. Based on the results of this evaluation, dynamical regional downscaling using NU-WRF results in a more realistic representation of precipitation event climatology than the global downscaling of MERRA2 using GEOS-5.

The cloud water content (CW) and radiative heating rate (QR) structures related to northward propagating boreal summer intraseasonal oscillations (BSISOs) are analyzed using data from A-train satellites in conjunction with the ERA-Interim reanalysis. It is found that the northward movement of CW- and QR anomalies are closely synchronized with the northward movement of BSISO precipitation maxima. Commensurate with the northward propagating BSISO precipitation maxima, the CW anomalies exhibit positive ice (liquid) CW maxima in the upper (middle/low) troposphere with a prominent tilting structure in which the low-tropospheric (upper-tropospheric) liquid (ice) CW maximum leads (lags) the BSISO precipitation maximum. The BSISO-related shortwave heating (QSW) heats (cools) the upper (low) troposphere; the longwave heating (QLW) cools (heats) the upper (middle/low) troposphere. The resulting net radiative heating (QRN), being dominated by QLW, cools (heats) the atmosphere most prominently above the 200 hPa level (below the 600 hPa level). Enhanced clouds in the upper and middle troposphere appears to play a critical role in increasing low-level QLW and QRN. The vertically-integrated QSW, QLW and QRN are positive in the region of enhanced CW with the maximum QRN near the latitude of the BSISO precipitation maximum. The bottom-heavy radiative heating anomaly resulting from the cloud-radiation interaction may act to strengthen convection.

Winter precipitation (PR) characteristics in western United States (WUS) related to atmospheric river (AR) landfalls are examined using the observation-based PRISM data. The observed AR-related precipitation characteristics are in turn used to evaluate model precipitation data from the NASA MERRA2 reanalysis and from seven dynamical downscaling simulations driven by the MERRA2. Multiple metrics including mean bias, Taylor diagram, and two skill scores are used to measure model performance for three climatological sub-regions in WUS, Pacific Northwest (PNW), Pacific Southwest (PSW) and Great Basin (GB). All model data well represent the winter-mean PR with spatial pattern correlations of 0.8 or higher with PRISM for the three sub-regions. Higher spatial resolutions and/or the use of spectral nudging generally yield higher skill scores in simulating the geographical distribution of PR for the entire winter. The PRISM data shows that the AR-related fraction of winter PR and associated daily PR PDFs in each region vary strongly for landfall locations; AR landfalls in the northern WUS coast (NC) affect mostly PNW while those in the southern WUS coast (SC) affect both PSW and GB. NC (SC) landfalls increase the frequency of heavy PR in PNW (PSW and GB) but reduce it in PSW (PNW). All model data reasonably represent these observed variations in the AR-related winter PR fractions and the daily PR PDFs according to AR landfall locations. However, unlike for the entire winter period, no systematic effects of resolution and/or spectral nudging are identified in these AR-related PR characteristics. Dynamical downscaling in this study generally yield positive added values to the MERRA2 PR in the AR-related PR fraction for most sub-regions and landfall locations, most noticeably for PSW by NU-WRF. The downscaling also generate positive added value in p95 for PNW, but negative values for PSW and GB due to overestimation of heavy precipitation events.

Representation of the boreal summer intraseasonal oscillations (BSISO) is evaluated in the 20-year climate simulations from 27 general circulation models (GCMs), produced as part of a global multi-model evaluation project coordinated to study the vertical structure and physical processes of the Madden–Julian oscillation (MJO). Model performance metrics are developed to assess the simulated BSISO characteristics, with a special focus on its northward propagation over the Asian monsoon domain. Several process-oriented diagnostics developed by the MJO community are also tested for the BSISO. Simulating the phase speed and meridional extent of BSISO northward propagation, the northwest–southeast tilted rain-band structure and the quasi-biweekly mode are identified as some of the persisting problems for many GCMs. Interestingly, many of the GCMs, which capture BSISO eastward propagation, also show good fidelity in simulating BSISO northward propagation. Meridional vertical profiles of anomalous wind, temperature and diabatic heating of BSISO are better simulated in the GCMs that simulate the northward propagation. Process-oriented diagnostics based on seasonal mean vertical shear of zonal and meridional wind, large-scale rain fraction and relative humidity are also examined, but it still remains challenge to find a process diagnostic which is strongly linked to BSISO northward propagation. The complex spatial structure and presence of multi-scale disturbances, demand the development of more focused GCM evaluation metrics and process diagnostics specifically for the BSISO.

Large-scale meteorological patterns (LSMPs) associated with temperature extremes are evaluated in a suite of regional climate model (RCM) simulations contributing to the North American Regional Climate Change Assessment Program. LSMPs are characterized through composites of surface air temperature, sea level pressure, and 500 hPa geopotential height anomalies concurrent with extreme temperature days. Six of the seventeen RCM simulations are driven by boundary conditions from reanalysis while the other eleven are driven by one of four global climate models (GCMs). Four illustrative case studies are analyzed in detail. Model fidelity in LSMP spatial representation is high for cold winter extremes near Chicago. Winter warm extremes are captured by most RCMs in northern California, with some notable exceptions. Model fidelity is lower for cool summer days near Houston and extreme summer heat events in the Ohio Valley. Physical interpretation of these patterns and identification of well-simulated cases, such as for Chicago, boosts confidence in the ability of these models to simulate days in the tails of the temperature distribution. Results appear consistent with the expectation that the ability of an RCM to reproduce a realistically shaped frequency distribution for temperature, especially at the tails, is related to its fidelity in simulating LMSPs. Each ensemble member is ranked for its ability to reproduce LSMPs associated with observed warm and cold extremes, identifying systematically high performing RCMs and the GCMs that provide superior boundary forcing. The methodology developed here provides a framework for identifying regions where further process-based evaluation would improve the understanding of simulation error and help guide future model improvement and downscaling efforts.

This study evaluates model fidelity in simulating relationships between seasonally averaged precipitation, cloud fraction and surface insolation from the North American Regional Climate Change Assessment Project (NARCCAP) hindcast using observational data from ground stations and satellites. Model fidelity is measured in terms of the temporal correlation coefficients between these three variables and the similarity between the observed and simulated joint probability distribution functions (JPDFs) in 14 subregions over the conterminous United States. Observations exhibit strong negative correlations between precipitation/cloud fraction and surface insolation for all seasons, whereas the relationship between precipitation and cloud fraction varies according to regions and seasons. The skill in capturing these observed relationships varies widely among the NARCCAP regional climate models, especially in the Midwest and Southeast coast regions where observations show weak (or even negative) correlations between precipitation and cloud fraction in winter due to frequent non-precipitating stratiform clouds. Quantitative comparison of univariate and JPDFs indicates that model performance varies markedly between regions as well as seasons. This study also shows that comparison of JPDFs is useful for summarizing the performance of and highlighting problems with some models in simulating cloud fraction and surface insolation. Our quantitative metric may be useful in improving climate models by highlighting shortcomings in the formulations related with the physical processes involved in precipitation, clouds and radiation or other multivariate processes in the climate system.

Tropical subseasonal variability of precipitation from five global reanalyses (RAs) is evaluated against Global Precipitation Climatology Project (GPCP) and Tropical Rainfall Measuring Mission (TRMM) observations. The RAs include the three generations of global RAs from the National Center for Environmental Prediction (NCEP), and two other RAs from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Aeronautics and Space Administration/Goddard Space Flight Center (NASA/GSFC). The analysis includes comparisons of the seasonal means and subseasonal variances of precipitation, and probability densities of rain intensity in selected areas. In addition, the space–time power spectrum was computed to examine the tropical Madden-Julian Oscillation (MJO) and convectively coupled equatorial waves (CCEWs). The modern RAs show significant improvement in their representation of the mean state and subseasonal variability of precipitation when compared to the two older NCEP RAs: patterns of the seasonal mean state and the amplitude of subseasonal variability are more realistic in the modern RAs. However, the probability density of rain intensity in the modern RAs show discrepancies from observations that are similar to what the old RAs have. The modern RAs show higher coherence of CCEWs with observed variability and more realistic eastward propagation of the MJO precipitation. The modern RAs, however, exhibit common systematic deficiencies including: (1) variability of the CCEWs that tends to be either too weak or too strong, (2) limited coherence with observations for waves other than the MJO, and (3) a systematic phase lead or lag for the higher-frequency waves.

The change in Madden–Julian oscillation (MJO) amplitude and variance in response to anthropogenic climate change is assessed in the 1° nominal resolution community climate system model, version 4 (CCSM4), which has a reasonable representation of the MJO characteristics both dynamically and statistically. The twentieth century CCSM4 run is compared with the warmest twenty-first century projection (representative concentration pathway 8.5, or RCP8.5). The last 20 years of each simulation are compared in their MJO characteristics, including spatial variance distributions of winds, precipitation and outgoing longwave radiation, histograms of event amplitude, phase and duration, and composite maps of phases. The RCP8.5 run exhibits increased variance in intraseasonal precipitation, larger-amplitude MJO events, stronger MJO rainfall in the central and eastern tropical Pacific, and a greater frequency of MJO occurrence for phases corresponding to enhanced rainfall in the Indian Ocean sector. These features are consistent with the concept of an increased magnitude for the hydrological cycle under greenhouse warming conditions. Conversely, the number of active MJO days decreases and fewer weak MJO events occur in the future climate state. These results motivate further study of these changes since tropical rainfall variability plays such an important role in the region’s socio-economic well being.

Monthly-mean precipitation, mean (TAVG), maximum (TMAX) and minimum (TMIN) surface air temperatures, and cloudiness from the CORDEX-Africa regional climate model (RCM) hindcast experiment are evaluated for model skill and systematic biases. All RCMs simulate basic climatological features of these variables reasonably, but systematic biases also occur across these models. All RCMs show higher fidelity in simulating precipitation for the west part of Africa than for the east part, and for the tropics than for northern Sahara. Interannual variation in the wet season rainfall is better simulated for the western Sahel than for the Ethiopian Highlands. RCM skill is higher for TAVG and TMAX than for TMIN, and regionally, for the subtropics than for the tropics. RCM skill in simulating cloudiness is generally lower than for precipitation or temperatures. For all variables, multi-model ensemble (ENS) generally outperforms individual models included in ENS. An overarching conclusion in this study is that some model biases vary systematically for regions, variables, and metrics, posing difficulties in defining a single representative index to measure model fidelity, especially for constructing ENS. This is an important concern in climate change impact assessment studies because most assessment models are run for specific regions/sectors with forcing data derived from model outputs. Thus, model evaluation and ENS construction must be performed separately for regions, variables, and metrics as required by specific analysis and/or assessments. Evaluations using multiple reference datasets reveal that cross-examination, quality control, and uncertainty estimates of reference data are crucial in model evaluations.

Effects of atmospheric river (AR) landfalls in the California coast on the cold-season precipitation in California are examined for the cold seasons of 10 water years (WYs) 2001–2010 using observed data and regional modeling in conjunction with AR-landfall inventory based on visual inspections of precipitable water vapor (PWV) from remote sensing and reanalysis. The PWV in the SSM/I and SSMIS retrievals and the ERA-Interim reanalysis shows 95 AR-landfall days in the California coast that are almost evenly split between the northern and southern coasts across 37.5N. The CPC/NCEP gridded daily precipitation analysis shows that 10–30% of the cold-season precipitation totals in California have occurred during these AR landfalls. The analysis also reveals that the percentage of precipitation and the precipitation intensity during AR landfalls in California are characterized by strong north-to-south gradient. This north–south contrast in the AR precipitation is reversed for the non-AR precipitation in the coastal range. The frequency of AR landfalls and the cold-season precipitation totals in the Sierra Nevada region are only marginally correlated. Instead, AR landfalls are closely related with the occurrence of heavy precipitation events. The freezing-level altitudes are systematically higher for AR wet days than non-AR wet days indicating warmer low-troposphere during AR storms. Cold season simulations for the 10 WYs 2001–2010 show that the Weather Research and Forecast (WRF) model can reasonably simulate important features in both the seasonal and AR precipitation totals. The daily pattern correlation coefficients between the simulated and ERA-Interim upper-air fields exceed 0.9 for most of the period. This suggests that the simulated temporal variations in the atmospheric circulation agree reasonably with the reanalysis over seasonal time scales, characteristics critical for reliable simulations of regional scale hydrologic cycle. The simulated seasonal and AR precipitation totals also agree reasonably with the CPC/NCEP precipitation analysis. The most notable model errors include the overestimation (underestimation) of the season-total and AR precipitation in the northern (southern) California region. The differences in the freezing-level altitudes during the AR- and non-AR wet days in the simulation agree with those from the ERA-Interim reanalysis. The freezing level altitudes are systematically overestimated in the simulations, suggesting warm biases in the low troposphere. Overall, WRF appears to perform reasonably in simulating the key features in the cold season precipitation related with AR landfalls, an important capability for assessing the impact of global climate variations and change on future hydrology in California.

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.

Simulations of late 20th and 21st century Arctic cloud amount from 20 global climate models (GCMs) in the Coupled Model Intercomparison Project phase 3 (CMIP3) dataset are synthesized and assessed. Under recent climatic conditions, GCMs realistically simulate the spatial distribution of Arctic clouds, the magnitude of cloudiness during the warmest seasons (summer–autumn), and the prevalence of low clouds as the predominant type. The greatest intermodel spread and most pronounced model error of excessive cloudiness coincides with the coldest seasons (winter–spring) and locations (perennial ice pack, Greenland, and the Canadian Archipelago). Under greenhouse forcing (SRES A1B emissions scenario) the Arctic is expected to become cloudier, especially during autumn and over sea ice, in tandem with cloud decreases in middle latitudes. Projected cloud changes for the late 21st century depend strongly on the simulated modern (late 20th century) annual cycle of Arctic cloud amount: GCMs that correctly simulate more clouds during summer than winter at present also tend to simulate more clouds in the future. The simulated Arctic cloud changes display a tripole structure aloft, with largest increases concentrated at low levels (below 700 hPa) and high levels (above 400 hPa) but little change in the middle troposphere. The changes in cloud radiative forcing suggest that the cloud changes are a positive feedback annually but negative during summer. Of potential explanations for the simulated Arctic cloud response, local evaporation is the leading candidate based on its high correlation with the cloud changes. The polar cloud changes are also significantly correlated with model resolution: GCMs with higher spatial resolution tend to produce larger future cloud increases.

The intraseasonal variability associated with the Asian summer monsoon as simulated by a number of atmospheric general circulation models (AGCMs) are analyzed and assessed against observations. The model data comes from the Monsoon GCM Intercomparison project initiated by the CLIVAR/Asian-Australian Monsoon Panel. Ten GCM groups, i.e., the Center for Ocean-Land-Atmosphere Studies (COLA), Institute of

The Madden and Julian Oscillation (MJO) is the most prominent mode of intraseasonal variations in the tropical region. It plays an important role in climate variability and has a significant influence on medium-to-extended ranges weather forecasting in the tropics. This study examines the forecast skill of the oscillation in a set of recent dynamical extended range forecasts (DERF) experiments performed by the National Centers for Environmental Prediction (NCEP). The present DERF experiments were done with the reanalysis version of the medium range forecast (MRF) model and include 50-day forecasts, initialized once-a-day (0Z) with reanalyses fields, for the period between 1 January, 1985, and 31 December, 1989. The MRF model shows large mean errors in representing intraseasonal variations of the large-scale circulation, especially over the equatorial eastern Pacific Ocean. A diagnostic analysis has considered the different phases of the MJO and the associated forecast skill of the MRF model. Anomaly correlations on the order of 0.3 to 0.4 indicate that skillful forecasts extend out to 5 to 7 days lead-time. Furthermore, the results show a slight increase in the forecast skill for periods when convective anomalies associated with the MJO are intense. By removing the mean errors, the analysis shows systematic errors in the representation of the MJO with weaker than observed upper level zonal circulations. The examination of the climate run of the MRF model shows the existence of an intraseasonal oscillation, although less intense (50–70%) and with faster (nearly twice as fast) eastward propagation than the observed MJO. The results indicate that the MRF model likely has difficulty maintaining the MJO, which impacts its forecast. A discussion of future work to improve the representation of the MJO in dynamical models and assess its prediction is presented.

We present a comparison of the zonal mean meridional circulations derived from monthly in situ data (i.e. radiosondes and ship reports) and from the NCEP/NCAR reanalysis product. To facilitate the interpretation of the results, a third estimate of the mean meridional circulation is produced by subsampling the reanalysis at the locations where radiosonde and surface ship data are available for the in situ calculation. This third estimate, known as the subsampled estimate, is compared to the complete reanalysis estimate to assess biases in conventional, in situ estimates of the Hadley circulation associated with the sparseness of the data sources (i.e., radiosonde network). The subsampled estimate is also compared to the in situ estimate to assess the biases introduced into the reanalysis product by the numerical model, initialization process and/or indirect data sources such as satellite retrievals. The comparisons suggest that a number of qualitative differences between the in situ and reanalysis estimates are mainly associated with the sparse sampling and simplified interpolation schemes associated with in situ estimates. These differences include: (1) a southern Hadley cell that consistently extends up to 200 hPa in the reanalysis, whereas the bulk of the circulation for the in situ and subsampled estimates tends to be confined to the lower half of the troposphere, (2) more well-defined and consistent poleward limits of the Hadley cells in the reanalysis compared to the in-situ and subsampled estimates, and (3) considerably less variability in magnitude and latitudinal extent of the Ferrel cells and southern polar cell exhibited in the reanalysis estimate compared to the in situ and subsampled estimates. Quantitative comparison shows that the subsampled estimate, relative to the reanalysis estimate, produces a stronger northern Hadley cell (∼20%), a weaker southern Hadley cell (∼20–60%), and weaker Ferrel cells in both hemispheres. These differences stem from poorly measured oceanic regions which necessitate significant interpolation over broad regions. Moreover, they help to pinpoint specific shortcomings in the present and previous in situ estimates of the Hadley circulation. Comparisons between the subsampled and in situ estimates suggest that the subsampled estimate produces a slightly stronger Hadley circulation in both hemispheres, with the relative differences in some seasons as large as 20–30%. 6These differences suggest that the mean meridional circulation associated with the NCEP/NCAR reanalysis is more energetic than observations suggest. Examination of ENSO-related changes to the Hadley circulation suggest that the in situ and subsampled estimates significantly overestimate the effects of ENSO on the Hadley circulation due to the reliance on sparsely distributed data. While all three estimates capture the large-scale region of low-level equatorial convergence near the dateline that occurs during El Nino, the in situ and subsampled estimates fail to effectively reproduce the large-scale areas of equatorial mass divergence to the west and east of this convergence area, leading to an overestimate of the effects of ENSO on the zonal mean circulation.

The boreal summer intraseasonal variability (BSISV), which is characterized by pronounced meridional propagation from the equatorial zone to the Indian Continent, exerts significant modulation of the active/break phases of the south Asian monsoon. This form of variability provides a primary source of subseasonal predictive skill of the Asian summer monsoon. Unfortunately, current general circulation models display large deficiencies in representing this variability. The new cloud observations made available by the CloudSat mission provide an unprecedented opportunity to advance our characterization of the BSISV. In this study, the vertical structures of cloud water content and cloud types associated with the BSISV over the Indian Ocean and subcontinent are analyzed based on CloudSat observations from 2006 to 2008. These cloud structures are also compared to their counterparts as derived from ERA-interim reanalysis. A marked vertical tilting structure in cloud water is illustrated during the northward propagation of the BSISV based on both datasets. Increased cloud liquid water content (LWC) tends to appear to the north of the rainfall maximum, while ice water content (IWC) in the upper troposphere slightly lags the convection. This northward shift of increased LWC, which is in accord with local enhanced moisture as previously documented, may play an important role in the northward propagation of the BSISV. The transition in cloud structures associated with BSISV convection is further demonstrated based on CloudSat, with shallow cumuli at the leading edge, followed by the deep convective clouds, and then upper anvil clouds. Some differences in cloud water structures between CloudSat and ERA-interim are also noted, particularly in the amplitudes of IWC and LWC fields.

The boreal summer intraseasonal oscillation (BSISO) of the Asian summer monsoon (ASM) is one of the most prominent sources of short-term climate variability in the global monsoon system. Compared with the related Madden-Julian Oscillation (MJO) it is more complex in nature, with prominent northward propagation and variability extending much further from the equator. In order to facilitate detection, monitoring and prediction of the BSISO we suggest two real-time indices: BSISO1 and BSISO2, based on multivariate empirical orthogonal function (MV-EOF) analysis of daily anomalies of outgoing longwave radiation (OLR) and zonal wind at 850 hPa (U850) in the region 10°S–40°N, 40°–160°E, for the extended boreal summer (May–October) season over the 30-year period 1981–2010. BSISO1 is defined by the first two principal components (PCs) of the MV-EOF analysis, which together represent the canonical northward propagating variability that often occurs in conjunction with the eastward MJO with quasi-oscillating periods of 30–60 days. BSISO2 is defined by the third and fourth PCs, which together mainly capture the northward/northwestward propagating variability with periods of 10–30 days during primarily the pre-monsoon and monsoon-onset season. The BSISO1 circulation cells are more Rossby wave like with a northwest to southeast slope, whereas the circulation associated with BSISO2 is more elongated and front-like with a southwest to northeast slope. BSISO2 is shown to modulate the timing of the onset of Indian and South China Sea monsoons. Together, the two BSISO indices are capable of describing a large fraction of the total intraseasonal variability in the ASM region, and better represent the northward and northwestward propagation than the real-time multivariate MJO (RMM) index of Wheeler and Hendon.

The MJO modulation of sea surface chlorophyll-a (Chl) examined initially by Waliser et al. in Geophys Res Lett, (2005) is revisited with a significantly longer time-series of observations and a more systematic approach to characterizing the possible mechanisms underlying the MJO-Chl relationships. The MJO composite analysis of Chl and lead-lag correlations between Chl and other physical variables reveal regional variability of Chl and corresponding indicative temporal relationships among variables. Along the path of the MJO convection, wind speed—a proxy for oceanic vertical turbulent mixing and corresponding entrainment—is most strongly correlated with Chl when wind leads Chl by a few days. Composite Chl also displays MJO influences away from the path of the MJO convection. The role of wind speed in those regions is generally the same for Chl variability as that along the path of the MJO convection, although Ekman pumping also plays a role in generating Chl variability in limited regions. However, the wind forcing away from the MJO convection path is less coherent, rendering the temporal link relatively weak. Lastly, the potential for bio-physical feedbacks at the MJO time-scale is examined. The correlation analysis provides tantalizing evidence for local bio-feedbacks to the physical MJO system. Plausible hypothesis for Chl to amplify the MJO phase transition is presented though it cannot be affirmed in this study and will be examined and reported in a future modeling study.

The Madden-Julian Oscillation (MJO) simulation diagnostics developed by MJO Working Group and the process-oriented MJO simulation diagnostics developed by MJO Task Force are applied to 37 Coupled Model Intercomparison Project phase 5 (CMIP5) models in order to assess model skill in representing amplitude, period, and coherent eastward propagation of the MJO, and to establish a link between MJO simulation skill and parameterized physical processes. Process-oriented diagnostics include the Relative Humidity Composite based on Precipitation (RHCP), Normalized Gross Moist Stability (NGMS), and the Greenhouse Enhancement Factor (GEF). Numerous scalar metrics are developed to quantify the results. Most CMIP5 models underestimate MJO amplitude, especially when outgoing longwave radiation (OLR) is used in the evaluation, and exhibit too fast phase speed while lacking coherence between eastward propagation of precipitation/convection and the wind field. The RHCP-metric, indicative of the sensitivity of simulated convection to low-level environmental moisture, and the NGMS-metric, indicative of the efficiency of a convective atmosphere for exporting moist static energy out of the column, show robust correlations with a large number of MJO skill metrics. The GEF-metric, indicative of the strength of the column-integrated longwave radiative heating due to cloud-radiation interaction, is also correlated with the MJO skill metrics, but shows relatively lower correlations compared to the RHCP- and NGMS-metrics. Our results suggest that modifications to processes associated with moisture-convection coupling and the gross moist stability might be the most fruitful for improving simulations of the MJO. Though the GEF-metric exhibits lower correlations with the MJO skill metrics, the longwave radiation feedback is highly relevant for simulating the weak precipitation anomaly regime that may be important for the establishment of shallow convection and the transition to deep convection.