Using cloud ice flux to parametrise large-scale lightning

Abstract. Lightning is an important natural source of nitrogen oxide especially in the middle and upper troposphere. Hence, it is essential to represent lightning in chemistry transport and coupled chemistry–climate models. Using ERA-Interim meteorological reanalysis data we compare the lightning flash density distributions produced using several existing lightning parametrisations, as well as a new parametrisation developed on the basis of upward cloud ice flux at 440 hPa. The use of ice flux forms a link to the non-inductive charging mechanism of thunderstorms. Spatial and temporal distributions of lightning flash density are compared to tropical and subtropical observations for 2007–2011 from the Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite. The well-used lightning flash parametrisation based on cloud-top height has large biases but the derived annual total flash density has a better spatial correlation with the LIS observations than other existing parametrisations. A comparison of flash density simulated by the different schemes shows that the cloud-top height parametrisation has many more instances of moderate flash densities and fewer low and high extremes compared to the other parametrisations. Other studies in the literature have shown that this feature of the cloud-top height parametrisation is in contrast to lightning observations over certain regions. Our new ice flux parametrisation shows a clear improvement over all the existing parametrisations with lower root mean square errors (RMSEs) and better spatial correlations with the observations for distributions of annual total, and seasonal and interannual variations. The greatest improvement with the new parametrisation is a more realistic representation of the zonal distribution with a better balance between tropical and subtropical lightning flash estimates. The new parametrisation is appropriate for testing in chemistry transport and chemistry–climate models that use a lightning parametrisation.

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Using cloud ice flux to parametrise large-scale lightning

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-14-17817-2014 ◽

2014 ◽

Vol 14 (12) ◽

pp. 17817-17856 ◽

Cited By ~ 1

Author(s):

D. L. Finney ◽

R. M. Doherty ◽

O. Wild ◽

H. Huntrieser ◽

H. C. Pumphrey ◽

...

Keyword(s):

Large Scale ◽

Climate Models ◽

Tropical Rainfall Measuring Mission ◽

Reanalysis Data ◽

Lightning Flash ◽

Spatial Correlations ◽

Zonal Distribution ◽

Annual Total ◽

Cloud Ice ◽

Mean Square Errors

Abstract. Lightning is an important natural source of nitrogen oxide especially in the middle and upper troposphere. Hence, it is essential to represent lightning in chemistry transport and coupled chemistry-climate models. Using ERA-Interim meteorological reanalysis data we compare the lightning flash density distributions produced using several existing lightning parametrisations, as well as a new parametrisation developed on the basis of upward cloud ice flux at 440 hPa. The use of ice flux forms a link to the non-inductive charging mechanism of thunderstorms. Spatial and temporal distributions of lightning flash density are compared to tropical and subtropical observations for 2007–2011 from the Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission satellite. The well-used lightning flash parametrisation based on cloud-top height has large biases but the derived annual total flash density has a better spatial correlation with the LIS observations than other existing parametrisations. A comparison of flash density simulated by the different schemes shows that the cloud-top height parametrisation has many more instances of moderate flash densities and fewer low and high extremes compared to the other parametrisations. Other studies in the literature have shown that this feature of the cloud-top height parametrisation is in contrast to lightning observations over certain regions. Our new ice flux parametrisation shows a clear improvement over all the existing parametrisations with lower root mean square errors and better spatial correlations with the observations for distributions of annual total, and seasonal and interannual variations. The greatest improvement with the new parametrisation is a more realistic representation of the zonal distribution with a better balance between tropical and subtropical lightning flash estimates. The new parametrisation is appropriate for testing in chemistry transport and chemistry-climate models that use a lightning parametrisation.

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Using Empirical Orthogonal Teleconnections to evaluate interannual rainfall variability over China in the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations

10.5194/gmd-2017-252 ◽

2017 ◽

Author(s):

Claudia Christine Stephan ◽

Nicholas P. Klingaman ◽

Pier Luigi Vidale ◽

Andrew G. Turner ◽

Marie-Estelle Demory ◽

...

Keyword(s):

Large Scale ◽

Climate Models ◽

Rainfall Variability ◽

Reanalysis Data ◽

Horizontal Resolution ◽

Unified Model ◽

Seasonal Precipitation ◽

Climate Simulations ◽

Interannual Rainfall Variability ◽

The Mean

Abstract. Six climate simulations of the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations are evaluated against observations and reanalysis data for their ability to simulate the mean state and year-to-year variability of precipitation over China. To analyze the sensitivity to air-sea coupling and horizontal resolution, atmosphere-only and coupled integrations at atmospheric horizontal resolutions of N96, N216 and N512 (corresponding to ~ 200, 90, and 40 km in the zonal direction at the equator, respectively) are analyzed. The mean and interannual variance of seasonal precipitation are too high in all simulations over China, but improve with finer resolution and coupling. Empirical Orthogonal Teleconnection (EOT) analysis is applied to simulated and observed precipitation to identify spatial patterns of temporally coherent interannual variability in seasonal precipitation. To connect these patterns to large-scale atmospheric and coupled air-sea processes, atmospheric and oceanic fields are regressed onto the corresponding seasonal-mean timeseries. All simulations reproduce the observed leading pattern of interannual rainfall variability in winter, spring and autumn; the leading pattern in summer is present in all but one simulation. However, only in two simulations are the four leading patterns associated with the observed physical mechanisms. Coupled simulations capture more observed patterns of variability and associate more of them with the correct physical mechanism, compared to atmosphere-only simulations at the same resolution. However, finer resolution does not improve the fidelity of these patterns or their associated mechanisms. This shows that evaluating climate models by only geographical distribution of mean precipitation and its interannual variance is insufficient. The EOT analysis adds knowledge about coherent variability and associated mechanisms.

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Retrieval of Latent Heating from TRMM Measurements

Bulletin of the American Meteorological Society ◽

10.1175/bams-87-11-1555 ◽

2006 ◽

Vol 87 (11) ◽

pp. 1555-1572 ◽

Cited By ~ 108

Author(s):

W.-K. Tao ◽

E. A. Smith ◽

R. F. Adler ◽

Z. S. Haddad ◽

A. Y. Hou ◽

...

Keyword(s):

Latent Heat ◽

Large Scale ◽

Climate Models ◽

Hydrological Cycle ◽

Tropical Rainfall Measuring Mission ◽

Phase Changes ◽

Latent Heating ◽

Middle Latitudes ◽

Rate Profile ◽

The Tropics

Rainfall is a fundamental process within the Earth's hydrological cycle because it represents a principal forcing term in surface water budgets, while its energetics corollary, latent heating, is the principal source of atmospheric diabatic heating well into the middle latitudes. Latent heat production itself is a consequence of phase changes between the vapor, liquid, and frozen states of water. The properties of the vertical distribution of latent heat release modulate large-scale meridional and zonal circulations within the Tropics, as well as modify the energetic efficiencies of midlatitude weather systems. This paper highlights the retrieval of latent heating from satellite measurements generated by the Tropical Rainfall Measuring Mission (TRMM) satellite observatory, which was launched in November 1997 as a joint American–Japanese space endeavor. Since then, TRMM measurements have been providing credible four-dimensional accounts of rainfall over the global Tropics and subtropics, information that can be used to estimate the space–time structure of latent heating across the Earth's low latitudes. A set of algorithm methodologies for estimating latent heating based on precipitation-rate profile retrievals obtained from TRMM measurements has been under continuous development since the advent of the mission s research program. These algorithms are briefly described, followed by a discussion of the latent heating products that they generate. The paper then provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, concluding with remarks intended to stimulate further research on latent heating retrieval from satellites.

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Downscale of future climate change scenarios applied to Recife-PE

Journal of Hyperspectral Remote Sensing ◽

10.29150/jhrs.v9.6.p361-372 ◽

2020 ◽

Vol 9 (6) ◽

pp. 361

Author(s):

Rafaela Lisboa Costa ◽

Heliofábio Barros Gomes ◽

Fabrício Daniel Dos Santos Silva ◽

Rodrigo Lins Da Rocha Júnior

Keyword(s):

Large Scale ◽

Climate Models ◽

Global Climate ◽

Reanalysis Data ◽

Global Climate Models ◽

Future Climate ◽

Future Climate Change ◽

Climate Change Scenarios ◽

The Future ◽

Maximum Temperatures

The objective of this work was to analyze and compare results from two generations of global climate models (GCMs) simulations for the city of Recife-PE: CMIP3 and CMIP5. Differences and similarities in historical and future climate simulations are presented for four GCMs using CMIP3 scenarios A1B and A2 and for seven CMIP5 scenarios RCP4.5 and RCP8.5. The scale reduction technique applied to GCMs scenarios is statistical downscaling, employing the same set of large-scale atmospheric variables as predictors for both sets of scenarios, differing only in the type of reanalysis data used to characterize surface variables precipitation, maximum and minimum temperatures. For CMIP3 scenarios the simulated historical climate is 1961-1990 and CMIP5 is 1979-2000, and the validation period is ten years, 1991-2000 for CMIP3 and 2001-2010 for CMIP5. However, for both the future period analyzed is 2021-2050 and 2051-2080. Validation metrics indicated superior results from the historical simulations of CMIP5 over those of CMIP3 for precipitation and minimum and similar temperatures for maximum temperatures. For the future, both CMIP3 and CMIP5 scenarios indicate reduced precipitation and increased temperatures. The potencial evapotranspiration was calculated, projected to increase in scenarios A1B and A2 of CMIP3 and with behavior similar to that observed historically in scenarios RCP4.5 and 8.5.

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Copula Bias Correction for Extreme Precipitation in Reanalysis Data over a Greek Catchment

Proceedings ◽

10.3390/ecws-3-05817 ◽

2018 ◽

Vol 7 (1) ◽

pp. 4

Author(s):

Georgia Lazoglou ◽

Christina Anagnostopoulou ◽

Charalampos Skoulikaris ◽

Konstantia Tolika

Keyword(s):

Bias Correction ◽

Extreme Precipitation ◽

Large Scale ◽

Climate Models ◽

Reanalysis Data ◽

Distribution Method ◽

Maximum Rainfall ◽

The Real ◽

Maximum Precipitation ◽

Extreme Precipitation Events

The projection of extreme precipitation events with higher accuracy and reliability that engender severe socioeconomic impacts more frequently is considered a priority research topic in the scientific community. Although large-scale initiatives for monitoring meteorological and hydrological variables exist, the lack of data is still evident particularly in regions with complex topographic characteristics. The latter results in the use of reanalysis data or data derived from regional climate models, however both datasets are biased to the observations resulting in nonaccurate results in hydrological studies. The current research presents a newly developed statistical method for the bias correction of the maximum rainfall amount at watershed scale. In particular, the proposed approach necessitates the coupling of a spatial distribution method, namely Thiessen polygons, with a multivariate probabilistic distribution method, namely copulas, for the bias correction of the maximum precipitation. The case study area is the Nestos River basin where the several extreme episodes that have been recorded have direct impacts to the regional agricultural economy. Thus, using daily data by three monitoring stations and daily reanalysis precipitation values from the grids closest to these stations, the results demonstrated that the bias corrected maximum precipitation totals (greater than 90%) is much closer to the real max precipitation totals, while the respective reanalysis value underestimates the real precipitation totals. The overall improvement of the output shows that the proposed Thiessen-copula method could constitute a significant asset to hydrologic simulations.

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Skill in Simulating Australian Precipitation at the Tropical Edge*

Journal of Climate ◽

10.1175/jcli-d-15-0548.1 ◽

2016 ◽

Vol 29 (4) ◽

pp. 1477-1496 ◽

Cited By ~ 3

Author(s):

Penelope Maher ◽

Steven C. Sherwood

Keyword(s):

Large Scale ◽

Climate Models ◽

Linear Independence ◽

Southern Oscillation ◽

Coupled Model ◽

Southern Annular Mode ◽

Reanalysis Data ◽

Hadley Cell ◽

Model Errors ◽

Skill Scores

Abstract Expansion of the tropics will likely affect subtropical precipitation, but observed and modeled precipitation trends disagree with each other. Moreover, the dynamic processes at the tropical edge and their interactions with precipitation are not well understood. This study assesses the skill of climate models to reproduce observed Australian precipitation variability at the tropical edge. A multivariate linear independence approach distinguishes between direct (causal) and indirect (circumstantial) precipitation drivers that facilitate clearer attribution of model errors and skill. This approach is applied to observed precipitation and ERA-Interim reanalysis data and a representative subset of four models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and their CMIP3 counterparts. The drivers considered are El Niño–Southern Oscillation, southern annular mode, Indian Ocean dipole, blocking, and four tropical edge metrics (position and intensity of the subtropical ridge and subtropical jet). These models are skillful in representing the covariability of drivers and their influence on precipitation. However, skill scores have not improved in the CMIP5 subset relative to CMIP3 in either respect. The Australian precipitation response to a poleward-located Hadley cell edge remains uncertain, as opposing drying and moistening mechanisms complicate the net response. Higher skill in simulating driver covariability is not consistently mirrored by higher precipitation skill. This provides further evidence that modeled precipitation does not respond correctly to large-scale flow patterns; further improvements in parameterized moist physics are needed before the subtropical precipitation responses can be fully trusted. The multivariate linear independence approach could be applied more widely for practical model evaluation.

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Process-oriented analysis of aircraft soot-cirrus interactions constrains the climate impact of aviation

Communications Earth & Environment ◽

10.1038/s43247-021-00175-x ◽

2021 ◽

Vol 2 (1) ◽

Author(s):

Bernd Kärcher ◽

Fabian Mahrt ◽

Claudia Marcolli

Keyword(s):

Radiative Forcing ◽

Large Scale ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Climate Impact ◽

Global Climate Models ◽

Soot Particles ◽

Cloud Ice ◽

The Impact

AbstractFully accounting for the climate impact of aviation requires a process-level understanding of the impact of aircraft soot particle emissions on the formation of ice clouds. Assessing this impact with the help of global climate models remains elusive and direct observations are lacking. Here we use a high-resolution cirrus column model to investigate how aircraft-emitted soot particles, released after ice crystals sublimate at the end of the lifetime of contrails and contrail cirrus, perturb the formation of cirrus. By allying cloud simulations with a measurement-based description of soot-induced ice formation, we find that only a small fraction (<1%) of the soot particles succeeds in forming cloud ice alongside homogeneous freezing of liquid aerosol droplets. Thus, soot-perturbed and homogeneously-formed cirrus fundamentally do not differ in optical depth. Our results imply that climate model estimates of global radiative forcing from interactions between aircraft soot and large-scale cirrus may be overestimates. The improved scientific understanding reported here provides a process-based underpinning for improved climate model parametrizations and targeted field observations.

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North American extreme precipitation events and related large-scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

Climate Dynamics ◽

10.1007/s00382-019-04958-z ◽

2019 ◽

Vol 53 (11) ◽

pp. 6835-6875 ◽

Cited By ~ 8

Author(s):

Mathew Barlow ◽

William J. Gutowski ◽

John R. Gyakum ◽

Richard W. Katz ◽

Young-Kwon Lim ◽

...

Keyword(s):

Extreme Precipitation ◽

Large Scale ◽

Climate Models ◽

Early Stage ◽

Reanalysis Data ◽

Model Representation ◽

Dynamics Modeling ◽

Considerable Uncertainty ◽

Extreme Precipitation Events ◽

Precipitation Events

Abstract This paper surveys the current state of knowledge regarding large-scale meteorological patterns (LSMPs) associated with short-duration (less than 1 week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defined based on the characteristic spatial variations of a meteorological field or on the remote circulation response to a known forcing, LSMPs are defined relative to the occurrence of a specific phenomenon—here, extreme precipitation—and with an emphasis on the synoptic scales that have a primary influence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to definitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many different definitions of “extreme” are in use, making it difficult to directly compare different studies. Dynamically, several types of meteorological systems—extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs—and several mechanisms—fronts, atmospheric rivers, and orographic ascent—have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some promising analysis techniques have been identified and the LSMP perspective is useful for evaluating the model dynamics associated with extremes.

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The Role of Mesoscale Cellular Convective Cloud Morphologies in Low Cloud Feedbacks

10.5194/egusphere-egu21-8516 ◽

2021 ◽

Author(s):

Isabel L. McCoy ◽

Daniel T. McCoy ◽

Robert Wood ◽

Paquita Zuidema ◽

Frida A.-M. Bender

Keyword(s):

Large Scale ◽

Climate Models ◽

Global Climate ◽

Convective Cloud ◽

Static Stability ◽

Reanalysis Data ◽

Global Climate Models ◽

Liquid Water Path ◽

Radiative Impacts ◽

Low Cloud

Mesoscale cellular convective (MCC) clouds occur in large-scale patterns over the ocean, are prevalent in sub-tropical cloud regions and mid-latitudes, and have important radiative impacts on the climate system. On average, closed MCC clouds have higher albedos than open or disorganized MCC clouds for the same cloud fraction which suggests differences in micro- and macro-physical characteristics between MCC morphologies. Marine cold air outbreaks (MCAOs) influence the development of open MCC clouds and the transition from closed to open MCC clouds in the mid-latitudes. A MCAO index, M, combines atmospheric surface forcing and static stability and can be used to examine global MCC morphology dependencies. MCC cloud morphology occurrence is also expected to shift with sea surface temperature (SST) changes as the climate warms. Analysis of MCC identifications (derived from a neural network classifier applied to MODIS satellite collection 6 liquid water path retrievals) and ECMWF ERA5 reanalysis data shows that closed MCC cloud occurrence shifts to open or disorganized MCC within an M-SST space. Global climate models (GCMs) predict that M will change regionally in strength as SSTs increase. Based on our derived MCC-M-SST relationship in the current climate, closed MCC occurrence frequency is expected to increase with a weakening of M but decrease with an increase in SSTs. This results in a shift to cloud morphologies with lower albedos. Cloud controlling factor analysis is used to estimate the resulting low cloud morphology feedback which is found to be spatially varied and between &#177;0.15 W m-2 K-1. Because the morphology feedback is estimated to be positive in the extra-tropics and is not currently represented in GCMs, this implies a higher climate sensitivity than GCMs currently estimate.

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Submonthly Indian Ocean Cooling Events and Their Interaction with Large-Scale Conditions

Journal of Climate ◽

10.1175/2009jcli3067.1 ◽

2010 ◽

Vol 23 (3) ◽

pp. 700-716 ◽

Cited By ~ 23

Author(s):

Ian D. Lloyd ◽

Gabriel A. Vecchi

Keyword(s):

Indian Ocean ◽

Large Scale ◽

Climate Models ◽

Global Climate ◽

Low Frequency ◽

Tropical Rainfall Measuring Mission ◽

Tropical Indian Ocean ◽

Global Climate Models ◽

Standard Deviations ◽

Frequency Changes

Abstract The Indian Ocean exhibits strong variability on a number of time scales, including prominent intraseasonal variations in both the atmosphere and ocean. Of particular interest is the south tropical Indian Ocean thermocline ridge, a region located between 12° and 5°S, which exhibits prominent variability in sea surface temperature (SST) due to dominant winds that raise the thermocline and shoal the mixed layer. In this paper, submonthly (less than 30 day) cooling events in the thermocline ridge region are diagnosed with observations and models, and are related to large-scale conditions in the Indo-Pacific region. Observations from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) satellite were used to identify 16 cooling events in the period 1998–2007, which on average cannot be fully accounted for by air–sea enthalpy fluxes. Analysis of observations and a hierarchy of models, including two coupled global climate models (GFDL CM2.1 and GFDL CM2.4), indicates that ocean dynamical changes are important to the cooling events. For extreme cooling events (above 2.5 standard deviations), air–sea enthalpy fluxes account for approximately 50% of the SST signature, and oceanic processes cannot in general be neglected. For weaker cooling events (1.5–2.5 standard deviations), air–sea enthalpy fluxes account for a larger fraction of the SST signature. Furthermore, it is found that cooling events are preconditioned by large-scale, low-frequency changes in the coupled ocean–atmosphere system. When the thermocline is unusually shallow in the thermocline ridge region, cooling events are more likely to occur and are stronger; these large-scale conditions are more (less) likely during La Niña (El Niño/Indian Ocean dipole) events. Strong cooling events are associated with changes in atmospheric convection, which resemble the Madden–Julian oscillation, in both observations and the models.

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