Variability of Monthly Diurnal Cycle Composites of TOA Radiative Fluxes in the Tropics

Taylor, Patrick C.

doi:10.1175/jas-d-13-0112.1

Cited by 12 publications

(17 citation statements)

References 29 publications

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“…In the tropical region, the pIWP reaches the maximum at noontime (1200 LST) and a secondary weaker peak at 0600 LST. The results are consistent with a similar bimodal distribution found in the longwave cloud forcing diurnal cycle by Taylor (2012), and are also consistent with the trimodal structure of tropical convective clouds and corresponding diurnal cycles: early morning shallow convection, afternoon scattered convection, and nocturnal organized convection (Johnson et al 1999).…”

Section: B Diurnal Cycle Over Oceanssupporting

confidence: 88%

“…All of the climate models are atmospheric global circulation models (GCMs) that participated in the CMIP5. The simulations conducted by these GCMs are forced with the observed sea surface temperatures (SST), sea ice fractions, CO 2 concentrations, and other external forcings as defined in the Atmospheric Model Intercomparison Project (AMIP) framework (Taylor et al 2012). The modeled pIWP are computed by integrating IWCs from the 200-hPa level upward.…”

Section: Climate Models and Merra Reanalysismentioning

confidence: 99%

“…Besides the diurnal cycle of precipitation, a number of previous studies have investigated the diurnal cycles of cloud fraction, relative humidity, or top-of-atmosphere (TOA) radiative fluxes (e.g., Yang and Slingo 2001;May et al 2012;Taylor 2012Taylor , 2014. However, there are relatively fewer studies on the diurnal cycle of UT ice clouds, which are closely related to deep convection (Soden 2000;Su et al 2006;Tian et al 2004).…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Diurnal Cycle of Upper-Tropospheric Ice Clouds in Climate Models Using SMILES Observations

Jiang

Zhai

et al. 2015

Journal of the Atmospheric Sciences

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International audienceUpper-tropospheric ice cloud measurements from the Superconducting Submillimeter Limb Emission Sounder (SMILES) on the International Space Station (ISS) are used to study the diurnal cycle of upper-tropospheric ice cloud in the tropics and midlatitudes (40°S–40°N) and to quantitatively evaluate ice cloud diurnal variability simulated by 10 climate models. Over land, the SMILES-observed diurnal cycle has a maximum around 1800 local solar time (LST), while the model-simulated diurnal cycles have phases differing from the observed cycle by −4 to 12 h. Over ocean, the observations show much smaller diurnal cycle amplitudes than over land with a peak at 1200 LST, while the modeled diurnal cycle phases are widely distributed throughout the 24-h period. Most models show smaller diurnal cycle amplitudes over ocean than over land, which is in agreement with the observations. However, there is a large spread of modeled diurnal cycle amplitudes ranging from 20% to more than 300% of the observed over both land and ocean. Empirical orthogonal function (EOF) analysis on the observed and model-simulated variations of ice clouds finds that the first EOF modes over land from both observation and model simulations explain more than 70% of the ice cloud diurnal variations and they have similar spatial and temporal patterns. Over ocean, the first EOF from observation explains 26.4% of the variance, while the first EOF from most models explains more than 70%. The modeled spatial and temporal patterns of the leading EOFs over ocean show large differences from observations, indicating that the physical mechanisms governing the diurnal cycle of oceanic ice clouds are more complicated and not well simulated by the current climate models

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Section: B Diurnal Cycle Over Oceanssupporting

confidence: 88%

Section: Climate Models and Merra Reanalysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating the Diurnal Cycle of Upper-Tropospheric Ice Clouds in Climate Models Using SMILES Observations

Jiang

Zhai

et al. 2015

Journal of the Atmospheric Sciences

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“…The mean state and variability of the CDC is linked to atmospheric state anomalies on various timescales (e.g., Adams et al, ; Betts et al, , ; Derbyshire et al, ; Dodson & Taylor, ; Itterly et al, ; Lintner et al, ; Taylor, , ; Zelinka & Hartmann, ; Zhang & Klein, ; Zhao et al, ). For example, Zhang and Klein () show robust relationships between preconvective environmental parameters and afternoon deep convection in the U.S. Southern Great Plains indicating that higher convective available potential energy (CAPE) is associated with a later onset time and shorter duration of precipitation, whereas higher humidity above the boundary layer leads to an earlier onset time and longer duration of precipitation due to reduced entrainment of developing cumulus clouds (Derbyshire et al, ; Zhang & Klein, ).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of the Amazonian Convective Diurnal Cycle to Its Environment in Observations and Reanalysis

2018

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Atmospheric model parameterizations of tropical deep convection struggle to reproduce the observed diurnal variability of convection in the Amazon leading to climatological biases in the energy budget and water cycle. To identify the physical process contributions to these biases, we analyze the relationships between the convective diurnal cycle and atmosphere state variables relevant to convection in the Amazon using satellite observations and reanalysis data sets for wet and dry seasons between 2002 and 2016 and two Green Ocean Amazon periods. The analysis first stratifies the diurnal cycle into convective and nonconvective days using a daily maximum rain rate threshold of 0.5 mm/hr. Second, the population of days is constrained by requiring reanalysis and observations to agree on the occurrence of convective rain rates, controlling for frequency‐dependent biases in convection. The model‐generated precipitation phase in Modern‐Era Retrospective Analysis for Research and Applications‐2 is closer to observations than ERA during 2002–2016, which exhibits a systematic noontime bias and exaggerated diurnal amplitude. Despite the systematic noontime precipitation bias, ERA produces better agreement with Green Ocean Amazon observations due to the frequent midmorning arrival of the coastal front acting to shift the observed diurnal cycle closer to noon. Model disagreement between middle‐tropospheric vertical velocity is largest overnight during the dissipation stage of convection, acting to sustain biases through radiative effects. Specifically, the slower dissipation of convection in Modern‐Era Retrospective Analysis for Research and Applications‐2 acts to reduce morning surface fluxes and increase convective inhibition, whereas enhanced nocturnal midtropospheric subsidence and higher boundary layer humidity in ERA reduce morning convective inhibition leading to an earlier initiation of afternoon deep convection.

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“…Recent research 1 suggests that some of the uncertainty is tied to the ability to realistically represent the radiative diurnal cycle (RDC). The monthly variability in regional RDCs is as large as 7 W m -2 , which is 80% of the total interannual variability.…”

Section: Introductionmentioning

confidence: 99%

Monthly covariability of Amazonian convective cloud properties and radiative diurnal cycle

Dodson¹,

Taylor²

2017

AIP Conference Proceedings

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Abstract. The diurnal cycle of convective clouds greatly influences the top-of-atmosphere radiative energy balance in convectively active regions of Earth, through both direct presence and the production of anvil and stratiform clouds. CloudSat and CERES data are used to further examine these connections by determining the sensitivity of monthly anomalies in the radiative diurnal cycle to monthly anomalies in multiple cloud variables. During months with positive anomalies in convective frequency, the longwave diurnal cycle is shifted and skewed earlier in the day by the increased longwave cloud forcing during the afternoon from mature deep convective cores and associated anvils. This is consistent with previous studies using reanalysis data to characterize anomalous convective instability. Contrary to this, months with positive anomalies in convective cloud top height (commonly associated with more intense convection) shifts the longwave diurnal cycle later in the day. The contrary results are likely an effect of the inverse relationships between cloud top height and frequency. The albedo diurnal cycle yields inconsistent results when using different cloud variables.

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Variability of Monthly Diurnal Cycle Composites of TOA Radiative Fluxes in the Tropics

Cited by 12 publications

References 29 publications

Evaluating the Diurnal Cycle of Upper-Tropospheric Ice Clouds in Climate Models Using SMILES Observations

Evaluating the Diurnal Cycle of Upper-Tropospheric Ice Clouds in Climate Models Using SMILES Observations

Sensitivity of the Amazonian Convective Diurnal Cycle to Its Environment in Observations and Reanalysis

Monthly covariability of Amazonian convective cloud properties and radiative diurnal cycle

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