Most global climate models (GCMs) suffer from biases of a reversed zonal gradient in sea surface temperature (SST) and weak surface easterlies (the westerly bias) in the equatorial Atlantic during boreal spring. These biases exist in atmospheric GCMs (AGCMs) and are amplified by air–sea interactions in atmospheric–oceanic GCMs. This problem has persisted despite considerable model improvements in other aspects. This study proposes a hypothesis that there are two possible root causes for the westerly bias. The first is insufficient lower-tropospheric diabatic heating over Amazonia. The second is erroneously weak zonal momentum flux (entrainment) across the top of the boundary layer. This hypothesis is based on a scale analysis of a simple model for a well-mixed equatorial boundary layer and diagnoses of simulations from eight AGCMs. Severe westerly biases in AGCMs tend to occur when the diabatic heating at low levels (850–700 hPa) over Amazonia is too weak. Deficient low-level diabatic heating weakens the zonal gradient in sea level pressure along the Atlantic equator, introducing westerly biases. In addition, westerly biases may also occur when easterly momentum flux due to entrainment is underestimated.

Observations from the Atmospheric Radiation Measurement Program (ARM) site at Manus Island in the western Pacific and (re)analysis products are used to investigate moistening by shallow cumulus clouds and by the circulation in large-scale convective events. Large-scale convective events are defined as rainfall anomalies larger than one standard deviation for a minimum of three consecutive days over a 10° × 10° domain centered at Manus. These events are categorized into two groups: Madden–Julian oscillation (MJO) events, with eastward propagation, and non-MJO events, without propagation. Shallow cumulus clouds are identified as continuous time–height echoes from 1-min cloud radar observations with their tops below the freezing level and their bases within the boundary layer. Daily moistening tendencies of shallow clouds, estimated from differences between their mean liquid water content and precipitation over their presumed life spans, and those of physical processes and advection from (re)analysis products are compared with local moistening tendencies from soundings. Increases in low-level moisture before rainfall peaks of MJO and non-MJO events are evident in both observations and reanalyses. Before and after the rainfall peaks of these events, precipitating and nonprecipitating shallow clouds exist all the time, but their occurrence fluctuates randomly. Their contributions to moisture tendencies through evaporation of condensed water are evident. These clouds provide perpetual background moistening to the lower troposphere but do not cause the observed increase in low-level moisture leading to rainfall peaks. Such moisture increase is mainly caused by anomalous nonlinear zonal advection.

No abstract

Following the idea that large-scale wind perturbations cause repeatable rainfall patterns over small tropical islands, the spatial pattern of the midsummer drought (MSD) is investigated as a repeatable rainfall pattern over the Intra-Americas Seas (IAS). For that, statistical techniques, including linear regressions, canonical correlation analysis, and variance budgets, were applied to the Tropical Rainfall Measuring Mission and ERA-Interim datasets to assess 1) the MSD pattern repeatability and 2) its explained variance in different time scales. As shown by the results, the MSD pattern is not a unique feature of the boreal summer intraseasonal variability in the IAS: it is more robust during summer but it exists in all rainy seasons on daily, intraseasonal, and interannual time scales. On diurnal time scales, the MSD pattern explains a negligible part of the total variance during summer (<2%), but on interannual scales it explains up to 20% and it captures the spatial features of “El Niño” rainfall anomalies. On all time scales, the MSD pattern is accompanied by repeatable wind and pressure patterns: anomalous lower-tropospheric (925 hPa) easterlies over a domain-wide meridional northward pressure gradient. These results provide evidence for the hypothesis that the MSD pattern manifests an underlying geographically determined, mechanistic pattern. Also, they suggest that the repeatable MSD-shaped rainfall and wind patterns could be extrapolated in time to better understand the climatic conditions behind droughts and pluvials, and to diagnose the causes behind rainfall trends.

In order to quantify how much variability is affected by the diurnal cycle (DC), two variance‐partitioning schemes were applied on two rainfall datasets that cover the Intra Americas Seas (IAS) domain at a 3‐hourly resolution during 21 years. The datasets are the Tropical Rainfall Measurement Mission (TRMM) 3B42, and a simulation from the Centre National de Recherches Météorologiques (CNRM) model. From TRMM, the results show that the variance attributable to the DC is large because it shapes the variability of transients. Such diurnal effects shape transients similarly during all seasons, but their effects produce different patterns over continental and oceanic regions. Over continental regions, the total variance attributable to the DC is much larger than that attributable to the mean seasonal cycle and to interannual variability. But over oceanic regions, the latter two types of partitions can be larger than that of the DC. Although the analysed model simulation captures the broad features of the observed variance partitions, it overestimates the importance of the typical diurnal march, it underestimates how the DC tends to suppresses transient variability during nighttime over land, and it underestimates how it tends to increase it otherwise. As shown by the results, the variance‐partitioning approach of this study might complement previous approaches because rather than focusing on diurnal amplitudes and phases, it focuses on diurnal effects. Therefore, this approach could be useful to diagnose DC‐related issues from a relatively new perspective, issues such as the evaluation of systematic errors, or the evaluation of how the DC affects transients under different large‐scale conditions or slow‐varying forcings.