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Abstract Studying convection, which is one of the least understood physical mechanisms in the tropical atmosphere, is very important for weather and climate predictions of extreme events such as storms, hurricanes, monsoons, floods and hail. Collecting more observations to do so is critical. It is also a challenge. The OTREC (Organization of Tropical East Pacific Convection) field project took place in the summer of 2019. More than thirty scientists and twenty students from the US, Costa Rica, Colombia, México and UK were involved in collecting observations over the ocean (East Pacific and Caribbean) and land (Costa Rica, Colombia). We used the NSF NCAR Gulfstream V airplane to fly at 13 kilometers altitude sampling the tropical atmosphere under diverse weather conditions. The plane was flown in a ‘lawnmower’ pattern and every 10 minutes deployed dropsondes that measured temperature, wind, humidity and pressure from flight level to the ocean. Similarly, over the land we launched radiosondes, leveraged existing radars and surface meteorological networks across the region, some with co-located Global Positioning System (GPS) receivers and rain sensors, and installed a new surface GPS meteorological network across Costa Rica, culminating in an impressive systematic data set that when assimilated into weather models immediately gave better forecasts. We are now closer than ever in understanding the environmental conditions necessary for convection as well as how convection influences extreme events. The OTREC data set continues to be studied by researchers all over the globe. This article aims to describe the lengthy process that precedes science breakthroughs. 

Abstract The Earth’s climate system is a classical example of a multiscale, multiphysics dynamical system with an extremely large number of active degrees of freedom, exhibiting variability on scales ranging from micrometers and seconds in cloud microphysics, to thousands of kilometers and centuries in ocean dynamics. Yet, despite this dynamical complexity, climate dynamics is known to exhibit coherent modes of variability. A primary example is the El Niño Southern Oscillation (ENSO), the dominant mode of interannual (3–5 yr) variability in the climate system. The objective and robust characterization of this and other important phenomena presents a long-standing challenge in Earth system science, the resolution of which would lead to improved scientific understanding and prediction of climate dynamics, as well as assessment of their impacts on human and natural systems. Here, we show that the spectral theory of dynamical systems, combined with techniques from data science, provides an effective means for extracting coherent modes of climate variability from high-dimensional model and observational data, requiring no frequency prefiltering, but recovering multiple timescales and their interactions. Lifecycle composites of ENSO are shown to improve upon results from conventional indices in terms of dynamical consistency and physical interpretability. In addition, the role of combination modes between ENSO and the annual cycle in ENSO diversity is elucidated. 

Abstract The Madden‐Julian Oscillation (MJO), the dominant mode of tropical intraseasonal variability, is commonly identified using the realtime multivariate MJO (RMM) index based on joint empirical orthogonal function (EOF) analysis of near‐equatorial upper and lower level zonal winds and outgoing longwave radiation. Here, in place of conventional EOFs, we apply an operator‐theoretic formalism based on dynamic systems theory (the Koopman operator) to extract an analog to RMM that exhibits certain features that refine the characterization and predictability of the MJO. In particular, the spectrum of Koopman operator eigenfunctions, with eigenvalues corresponding to mode periods, contains a leading intraseasonal mode with period of ∼50 days. Moreover, the amplitude of this leading intraseasonal eigenfunction exhibits a seasonal modulation clearly peaked in boreal winter. Finally, the phase space formed by the complex Koopman MJO eigenfunction exhibits a smoother temporal evolution and higher degree of autocorrelation than RMM, which may contribute to enhanced predictive skill. 

Abstract. The continental tropics play a leading role in the terrestrial energy,water, and carbon cycles. Land–atmosphere interactions are integral in theregulation of these fluxes across multiple spatial and temporal scales overtropical continents. We review here some of the important characteristics oftropical continental climates and how land–atmosphere interactions regulatethem. Along with a wide range of climates, the tropics manifest a diversearray of land–atmosphere interactions. Broadly speaking, in tropicalrainforest climates, light and energy are typically more limiting thanprecipitation and water supply for photosynthesis and evapotranspiration (ET),whereas in savanna and semi-arid climates, water is the critical regulatorof surface fluxes and land–atmosphere interactions. We discuss the impact ofthe land surface, how it affects shallow and deep clouds, and how theseclouds in turn can feed back to the surface by modulating surface radiationand precipitation. Some results from recent research suggest that shallowclouds may be especially critical to land–atmosphere interactions. On theother hand, the impact of land-surface conditions on deep convection appearsto occur over larger, nonlocal scales and may be a more relevantland–atmosphere feedback mechanism in transitional dry-to-wet regions andclimate regimes. 

Understanding multiscale rainfall variability in the South Pacific convergence zone (SPCZ), a southeastward-oriented band of precipitating deep convection in the South Pacific, is critical for both the human and natural systems dependent on its rainfall, and for interpreting similar off-equatorial diagonal convection zones around the globe. A k-means clustering method is applied to daily austral summer (December–February) Tropical Rainfall Measuring Mission (TRMM) satellite rainfall to extract representative spatial patterns of rainfall over the SPCZ region for the period 1998–2013. For a k = 4 clustering, pairs of clusters differ predominantly via spatial translation of the SPCZ diagonal, reflecting either warm or cool phases of El Niño–Southern Oscillation (ENSO). Within each of these ENSO phase pairs, one cluster exhibits intense precipitation along the SPCZ while the other features weakened rainfall. Cluster temporal behavior is analyzed to investigate higher-frequency forcings (e.g., the Madden–Julian oscillation and synoptic-scale disturbances) that trigger deep convection where SSTs are sufficiently warm. Pressure-level winds and specific humidity from the Climate Forecast System Reanalysis are composited with respect to daily cluster assignment to investigate differences between active and quiescent SPCZ conditions to reveal the conditions supporting enhanced or suppressed SPCZ precipitation, such as low-level poleward moisture transport from the equator. Empirical orthogonal functions (EOFs) of TRMM precipitation are computed to relate the “modal view” of SPCZ variability associated with the EOFs to the “state view” associated with the clusters. Finally, the cluster number is increased to illustrate the change in TRMM rainfall patterns as additional degrees of freedom are permitted. 

Abstract Drought conditions significantly impact human and natural systems in the Tropics. Here, multiple observational and reanalysis products and ensembles of simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are analyzed with respect to drought areal extent over tropical land regions and its past and future relationships to the El Niño/Southern Oscillation (ENSO). CMIP5 models forced with prescribed sea surface temperatures compare well to observations in capturing the present day time evolution of the fraction of tropical land area experiencing drought conditions and the scaling of drought area and ENSO, that is, increasing tropical drought area with increasing ENSO warm phase (El Niño) strength. The ensemble of RCP8.5 simulations suggests lower end‐of‐the‐century El Niño strength‐tropical drought area sensitivity. At least some of this lower sensitivity is attributable to atmosphere‐ocean coupling, as historic coupled model simulations also exhibit lower sensitivity compared to the observations.