Long temporal autocorrelations in tropical precipitation data and spike train prototypes

Abbott, Tristan H.; Stechmann, Samuel N.; Neelin, J. David

doi:10.1002/2016gl071282

Cited by 9 publications

(13 citation statements)

References 24 publications

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“…To demonstrate the validity of the statements pertaining to spatiotemporal averaging and the effects of neglected influences on precipitation, we present analysis from a simple stochastic model of tropical precipitation. Variants of this model have featured in several prior works Neelin 2011, 2014;Abbott et al 2016;Neelin et al 2017;Martinez-Villalobos and Neelin 2019), so only a brief description is provided here. This model contains a single prognostic equation for columnintegrated moistureq:…”

Section: B Validation With a Stochastic Modelmentioning

confidence: 99%

Deep Convective Adjustment of Temperature and Moisture

Ahmed

Adames

Neelin

2020

Journal of the Atmospheric Sciences

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Simple process models and complex climate models are remarkably sensitive to the time scale of convective adjustment τ, but this parameter remains poorly constrained and understood. This study uses the linear-range slope of a semiempirical relationship between precipitation and a lower-free-tropospheric buoyancy measure BL. The BL measure is a function of layer-averaged moist enthalpy in the boundary layer (150-hPa-thick layer above surface), and temperature and moisture in the lower free troposphere (boundary layer top to 500 hPa). Sensitivity parameters with units of time quantify the BL response to its component perturbations. In moist enthalpy units, BL is more sensitive to temperature than equivalent moisture perturbations. However, column-integrated moist static energy conservation ensures that temperature and moisture are equally altered during the adjustment process. Multiple adjusted states with different temperature–moisture combinations exist; the BL sensitivity parameters govern the relationship between adjusted states, and also combine to yield a time scale of convective adjustment ~2 h. This value is comparable to τ values used in cumulus parameterization closures. Disparities in previously reported values of τ are attributed to the neglect of the temperature contribution to precipitation, and to averaging operations that include data from both precipitating and nonprecipitating regimes. A stochastic model of tropical convection demonstrates how either averaging operations or neglected environmental influences on precipitation can yield τ estimates longer than the true τ value built into the model. The analysis here culminates in construction of a precipitation closure with both moisture and temperature adjustment (q–T closure), suitable for use in both linearized and nonlinear, intermediate-complexity models.

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Section: B Validation With a Stochastic Modelmentioning

confidence: 99%

Deep Convective Adjustment of Temperature and Moisture

Ahmed

Adames

Neelin

2020

Journal of the Atmospheric Sciences

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“…It has been proposed that shallow clouds may also exhibit phase transitions, where the two phases could be open-cell and closed-cell stratocumulus phases, and/or cloudy and non-cloudy phases as indicated by cloud fraction [22,38]. Other possible phase transitions related to deep convection have also been proposed and investigated in some detail [1,18,19,28,30,39,40]. For shallow clouds, the present model provides an opportunity to look for phase transitions and to study the relation with many atmospheric quantities of interest, such as cloud fraction, temperature, and environmental factors.…”

Section: Phase Transitionmentioning

confidence: 99%

Shallow-cloud impact on climate and uncertainty: A simple stochastic model

Mueller

Stechmann

2020

Mathematics of Climate and Weather Forecasting

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Shallow clouds are a major source of uncertainty in climate predictions. Several different sources of the uncertainty are possible—e.g., from different models of shallow cloud behavior, which could produce differing predictions and ensemble spread within an ensemble of models, or from inherent, natural variability of shallow clouds. Here, the latter (inherent variability) is investigated, using a simple model of radiative statistical equilibrium, with oceanic and atmospheric boundary layer temperatures, To and Ta, and with moisture q and basic cloud processes. Stochastic variability is used to generate a statistical equilibrium with climate variability. The results show that the intrinsic variability of the climate is enhanced due to the presence of shallow clouds. In particular, the on-and-off switching of cloud formation and decay is a source of additional climate variability and uncertainty, beyond the variability of a cloud-free climate. Furthermore, a sharp transition in the mean climate occurs as environmental parameters are changed, and the sharp transition in the mean is also accompanied by a substantial enhancement of climate sensitivity and uncertainty. Two viewpoints of this behavior are described, based on bifurcations and phase transitions/statistical physics. The sharp regime transitions are associated with changes in several parameters, including cloud albedo and longwave absorptivity/carbon dioxide concentration, and the climate state transitions between a partially cloudy state and a state of full cloud cover like closed-cell stratocumulus clouds. Ideas of statistical physics can provide a conceptual perspective to link the climate state transitions, increased climate uncertainty, and other related behavior.

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“…The model presented here was previously studied in [1,13,20,21]. The underlying process is a one dimensional continuous-time stochastic process modeling the moisture q(t ) ∈ (−∞, ∞) for t ≥ 0, typically in cm, for a parcel of air.…”

Section: Introductionmentioning

confidence: 99%

“…In GCMs, the models are run for long times to examine potential effects, for example, of climate change. In long times, the model (2) was studied in [1]. In a long time view, the dry events dominate the rain events.…”

Section: Introductionmentioning

confidence: 99%

“…Only after this decoupling can convergence of the evaporation processes be shown rigorously with respect to the uniform metric on the space of continuous functions. Finally, the rain process σ(t ) is shown to converge rigorously with of [1] with permission from the authors. Panels (c) and (d) are realizations of the model of the rain rate process σ (t ) with finite rain rate r and σ(t ) the point process, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Convergence of Rain Process Models to Point Processes

Hottovy¹,

Stechmann²

2020

Preprint

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A moisture process with dynamics that switch after hitting a threshold gives rise to a rainfall process. This rainfall process is characterized by its random holding times for dry and wet periods. On average, the holding times for the wet periods are much shorter than the dry. Here convergence is shown for the rain fall process to a point process that is a spike train. The underlying moisture process for the point process is a threshold model with a teleporting boundary condition. This approximation allows simplification of the model with many exact formulas for statistics. The convergence is shown by a Fokker-Planck derivation, convergence in mean-square with respect to continuous functions, of the moisture process, and convergence in meansquare with respect to generalized functions, of the rain process.

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Long temporal autocorrelations in tropical precipitation data and spike train prototypes

Cited by 9 publications

References 24 publications

Deep Convective Adjustment of Temperature and Moisture

Deep Convective Adjustment of Temperature and Moisture

Shallow-cloud impact on climate and uncertainty: A simple stochastic model

Convergence of Rain Process Models to Point Processes

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