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- Author or Editor: Yanluan Lin x
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Abstract
Tropical cyclone (TC) genesis is a problem of great significance in climate and weather research. Although various environmental conditions necessary for TC genesis have been recognized for a long time, prediction of TC genesis remains a challenge due to complex and stochastic processes involved during TC genesis. Different from traditional statistical and dynamical modeling of TC genesis, in this study, a machine learning framework is developed to determine whether a mesoscale convective system (MCS) would evolve into a tropical cyclone. The machine learning models 1) are built upon a number of essential environmental predictors associated with MCSs/TCs, 2) predict whether MCSs can become TCs at different lead times, and 3) provide information about the relative importance of each predictor, which can be conducive to discovering new aspects of TC genesis. The results indicate that the machine learning classifier, AdaBoost, is able to achieve a 97.2% F1-score accuracy in predicting TC genesis over the entire tropics at a 6-h lead time using a comprehensive set of environmental predictors. A robust performance can still be attained when the lead time is extended to 12, 24, and 48 h, and when this machine learning classifier is separately applied to the North Atlantic Ocean and the western North Pacific Ocean. In contrast, the conventional approach based on the genesis potential index can have no more than an 80% F1-score accuracy. Furthermore, the machine learning classifier suggests that the low-level vorticity and genesis potential index are the most important predictors to TC genesis, which is consistent with previous discoveries.
Abstract
Tropical cyclone (TC) genesis is a problem of great significance in climate and weather research. Although various environmental conditions necessary for TC genesis have been recognized for a long time, prediction of TC genesis remains a challenge due to complex and stochastic processes involved during TC genesis. Different from traditional statistical and dynamical modeling of TC genesis, in this study, a machine learning framework is developed to determine whether a mesoscale convective system (MCS) would evolve into a tropical cyclone. The machine learning models 1) are built upon a number of essential environmental predictors associated with MCSs/TCs, 2) predict whether MCSs can become TCs at different lead times, and 3) provide information about the relative importance of each predictor, which can be conducive to discovering new aspects of TC genesis. The results indicate that the machine learning classifier, AdaBoost, is able to achieve a 97.2% F1-score accuracy in predicting TC genesis over the entire tropics at a 6-h lead time using a comprehensive set of environmental predictors. A robust performance can still be attained when the lead time is extended to 12, 24, and 48 h, and when this machine learning classifier is separately applied to the North Atlantic Ocean and the western North Pacific Ocean. In contrast, the conventional approach based on the genesis potential index can have no more than an 80% F1-score accuracy. Furthermore, the machine learning classifier suggests that the low-level vorticity and genesis potential index are the most important predictors to TC genesis, which is consistent with previous discoveries.
Abstract
This study evaluates the performances of seven single-column models (SCMs) by comparing simulated cloud fraction with observations at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site from January 1999 to December 2001. Compared with the 3-yr mean observational cloud fraction, the ECMWF SCM underestimates cloud fraction at all levels and the GISS SCM underestimates cloud fraction at levels below 200 hPa. The two GFDL SCMs underestimate lower-to-middle level cloud fraction but overestimate upper-level cloud fraction. The three Community Atmosphere Model (CAM) SCMs overestimate upper-level cloud fraction and produce lower-level cloud fraction similar to the observations but as a result of compensating overproduction of convective cloud fraction and underproduction of stratiform cloud fraction. Besides, the CAM3 and CAM5 SCMs both overestimate midlevel cloud fraction, whereas the CAM4 SCM underestimates. The frequency and partitioning analyses show a large discrepancy among the seven SCMs: Contributions of nonstratiform processes to cloud fraction production are mainly in upper-level cloudy events over the cloud cover range 10%–80% in SCMs with prognostic cloud fraction schemes and in lower-level cloudy events over the cloud cover range 15%–50% in SCMs with diagnostic cloud fraction schemes. Further analysis reveals different relationships between cloud fraction and relative humidity (RH) in the models and observations. The underestimation of lower-level cloud fraction in most SCMs is mainly due to the larger threshold RH used in models. The overestimation of upper-level cloud fraction in the three CAM SCMs and two GFDL SCMs is primarily due to the overestimation of RH and larger mean cloud fraction of cloudy events plus more occurrences of RH around 40%–80%, respectively.
Abstract
This study evaluates the performances of seven single-column models (SCMs) by comparing simulated cloud fraction with observations at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site from January 1999 to December 2001. Compared with the 3-yr mean observational cloud fraction, the ECMWF SCM underestimates cloud fraction at all levels and the GISS SCM underestimates cloud fraction at levels below 200 hPa. The two GFDL SCMs underestimate lower-to-middle level cloud fraction but overestimate upper-level cloud fraction. The three Community Atmosphere Model (CAM) SCMs overestimate upper-level cloud fraction and produce lower-level cloud fraction similar to the observations but as a result of compensating overproduction of convective cloud fraction and underproduction of stratiform cloud fraction. Besides, the CAM3 and CAM5 SCMs both overestimate midlevel cloud fraction, whereas the CAM4 SCM underestimates. The frequency and partitioning analyses show a large discrepancy among the seven SCMs: Contributions of nonstratiform processes to cloud fraction production are mainly in upper-level cloudy events over the cloud cover range 10%–80% in SCMs with prognostic cloud fraction schemes and in lower-level cloudy events over the cloud cover range 15%–50% in SCMs with diagnostic cloud fraction schemes. Further analysis reveals different relationships between cloud fraction and relative humidity (RH) in the models and observations. The underestimation of lower-level cloud fraction in most SCMs is mainly due to the larger threshold RH used in models. The overestimation of upper-level cloud fraction in the three CAM SCMs and two GFDL SCMs is primarily due to the overestimation of RH and larger mean cloud fraction of cloudy events plus more occurrences of RH around 40%–80%, respectively.
Abstract
This study evaluates the performances of seven single-column models (SCMs) by comparing simulated surface precipitation with observations at the Atmospheric Radiation Measurement Program Southern Great Plains (SGP) site from January 1999 to December 2001. Results show that although most SCMs can reproduce the observed precipitation reasonably well, there are significant and interesting differences in their details. In the cold season, the model–observation differences in the frequency and mean intensity of rain events tend to compensate each other for most SCMs. In the warm season, most SCMs produce more rain events in daytime than in nighttime, whereas the observations have more rain events in nighttime. The mean intensities of rain events in these SCMs are much stronger in daytime, but weaker in nighttime, than the observations. The higher frequency of rain events during warm-season daytime in most SCMs is related to the fact that most SCMs produce a spurious precipitation peak around the regime of weak vertical motions but rich in moisture content. The models also show distinct biases between nighttime and daytime in simulating significant rain events. In nighttime, all the SCMs have a lower frequency of moderate-to-strong rain events than the observations for both seasons. In daytime, most SCMs have a higher frequency of moderate-to-strong rain events than the observations, especially in the warm season. Further analysis reveals distinct meteorological backgrounds for large underestimation and overestimation events. The former occur in the strong ascending regimes with negative low-level horizontal heat and moisture advection, whereas the latter occur in the weak or moderate ascending regimes with positive low-level horizontal heat and moisture advection.
Abstract
This study evaluates the performances of seven single-column models (SCMs) by comparing simulated surface precipitation with observations at the Atmospheric Radiation Measurement Program Southern Great Plains (SGP) site from January 1999 to December 2001. Results show that although most SCMs can reproduce the observed precipitation reasonably well, there are significant and interesting differences in their details. In the cold season, the model–observation differences in the frequency and mean intensity of rain events tend to compensate each other for most SCMs. In the warm season, most SCMs produce more rain events in daytime than in nighttime, whereas the observations have more rain events in nighttime. The mean intensities of rain events in these SCMs are much stronger in daytime, but weaker in nighttime, than the observations. The higher frequency of rain events during warm-season daytime in most SCMs is related to the fact that most SCMs produce a spurious precipitation peak around the regime of weak vertical motions but rich in moisture content. The models also show distinct biases between nighttime and daytime in simulating significant rain events. In nighttime, all the SCMs have a lower frequency of moderate-to-strong rain events than the observations for both seasons. In daytime, most SCMs have a higher frequency of moderate-to-strong rain events than the observations, especially in the warm season. Further analysis reveals distinct meteorological backgrounds for large underestimation and overestimation events. The former occur in the strong ascending regimes with negative low-level horizontal heat and moisture advection, whereas the latter occur in the weak or moderate ascending regimes with positive low-level horizontal heat and moisture advection.
Abstract
A set of Geophysical Fluid Dynamics Laboratory (GFDL) Atmospheric Model version 2 (AM2) sensitivity simulations by varying an entrainment threshold rate to control deep convection occurrence are used to investigate how cumulus parameterization impacts tropical cloud and precipitation characteristics. In the tropics, model convective precipitation (CP) is frequent and light, while large-scale precipitation (LSP) is intermittent and strong. With deep convection inhibited, CP decreases significantly over land and LSP increases prominently over ocean. This results in an overall redistribution of precipitation from land to ocean. A composite analysis reveals that cloud fraction (low and middle) and cloud condensate associated with LSP are substantially larger than those associated with CP. With about the same total precipitation and precipitation frequency distribution over the tropics, simulations having greater LSP fraction tend to have larger cloud condensate and low and middle cloud fraction.
Simulations having a greater LSP fraction tend to be drier and colder in the upper troposphere. The induced unstable stratification supports strong transient wind perturbations and LSP. Greater LSP also contributes to greater intraseasonal (20–100 days) precipitation variability. Model LSP has a close connection to the low-level convergence via the resolved grid-scale dynamics and, thus, a close coupling with the surface heat flux. Such wind–evaporation feedback is essential to the development and maintenance of LSP and enhances model precipitation variability. LSP has stronger dependence and sensitivity on column moisture than CP. The moisture–convection feedback, critical to tropical intraseasonal variability, is enhanced in simulations with large LSP. Strong precipitation variability accompanied by a worse mean state implies that an optimal precipitation partitioning is critical to model tropical climate simulation.
Abstract
A set of Geophysical Fluid Dynamics Laboratory (GFDL) Atmospheric Model version 2 (AM2) sensitivity simulations by varying an entrainment threshold rate to control deep convection occurrence are used to investigate how cumulus parameterization impacts tropical cloud and precipitation characteristics. In the tropics, model convective precipitation (CP) is frequent and light, while large-scale precipitation (LSP) is intermittent and strong. With deep convection inhibited, CP decreases significantly over land and LSP increases prominently over ocean. This results in an overall redistribution of precipitation from land to ocean. A composite analysis reveals that cloud fraction (low and middle) and cloud condensate associated with LSP are substantially larger than those associated with CP. With about the same total precipitation and precipitation frequency distribution over the tropics, simulations having greater LSP fraction tend to have larger cloud condensate and low and middle cloud fraction.
Simulations having a greater LSP fraction tend to be drier and colder in the upper troposphere. The induced unstable stratification supports strong transient wind perturbations and LSP. Greater LSP also contributes to greater intraseasonal (20–100 days) precipitation variability. Model LSP has a close connection to the low-level convergence via the resolved grid-scale dynamics and, thus, a close coupling with the surface heat flux. Such wind–evaporation feedback is essential to the development and maintenance of LSP and enhances model precipitation variability. LSP has stronger dependence and sensitivity on column moisture than CP. The moisture–convection feedback, critical to tropical intraseasonal variability, is enhanced in simulations with large LSP. Strong precipitation variability accompanied by a worse mean state implies that an optimal precipitation partitioning is critical to model tropical climate simulation.
Abstract
The Pacific decadal oscillation (PDO) is the most dominant decadal climate variability over the North Pacific and has substantial global impacts. However, the interannual and decadal PDO prediction skills are not satisfactory, which may result from the failure of appropriately including the North Pacific midlatitude air–sea interaction (ASI) in the initialization for climate predictions. Here, we present a novel initialization method with a climate model to crack this nutshell and achieve successful PDO index predictions up to 10 years in advance. This approach incorporates oceanic observations under the constraint of ASI, thus obtaining atmospheric initial conditions (ICs) consistent with oceanic ICs. During predictions, positive atmospheric feedback to sea surface temperature changes and time-delayed negative ocean circulation feedback to the atmosphere over the North Pacific play essential roles in the high PDO index prediction skills. Our findings highlight a great potential of ASI constraints during initialization for skillful PDO predictions.
Significance Statement
The Pacific decadal oscillation is a prominent decadal climate variability over the North Pacific. However, accurately predicting the Pacific decadal oscillation remains a challenge. In this study, we use an advanced initialization method where the oceanic observations are incorporated into a climate model constrained by air–sea interactions. We can successfully predict the Pacific decadal oscillation up to 10 years in advance, which is hardly achieved by the state-of-the-art climate prediction systems. Our results suggest that the constraint of air–sea interaction during initialization is important to skillful predictions of the climate variability on decadal time scales.
Abstract
The Pacific decadal oscillation (PDO) is the most dominant decadal climate variability over the North Pacific and has substantial global impacts. However, the interannual and decadal PDO prediction skills are not satisfactory, which may result from the failure of appropriately including the North Pacific midlatitude air–sea interaction (ASI) in the initialization for climate predictions. Here, we present a novel initialization method with a climate model to crack this nutshell and achieve successful PDO index predictions up to 10 years in advance. This approach incorporates oceanic observations under the constraint of ASI, thus obtaining atmospheric initial conditions (ICs) consistent with oceanic ICs. During predictions, positive atmospheric feedback to sea surface temperature changes and time-delayed negative ocean circulation feedback to the atmosphere over the North Pacific play essential roles in the high PDO index prediction skills. Our findings highlight a great potential of ASI constraints during initialization for skillful PDO predictions.
Significance Statement
The Pacific decadal oscillation is a prominent decadal climate variability over the North Pacific. However, accurately predicting the Pacific decadal oscillation remains a challenge. In this study, we use an advanced initialization method where the oceanic observations are incorporated into a climate model constrained by air–sea interactions. We can successfully predict the Pacific decadal oscillation up to 10 years in advance, which is hardly achieved by the state-of-the-art climate prediction systems. Our results suggest that the constraint of air–sea interaction during initialization is important to skillful predictions of the climate variability on decadal time scales.
CLOUDS AND MORE: ARM Climate Modeling Best Estimate Data
A New Data Product for Climate Studies
Abstract
No Abstract available.
Abstract
No Abstract available.
Abstract
The Clouds, Aerosol, and Precipitation in the Marine Boundary Layer (CAP-MBL) deployment at Graciosa Island in the Azores generated a 21-month (April 2009–December 2010) comprehensive dataset documenting clouds, aerosols, and precipitation using the Atmospheric Radiation Measurement Program (ARM) Mobile Facility (AMF). The scientific aim of the deployment is to gain improved understanding of the interactions of clouds, aerosols, and precipitation in the marine boundary layer.
Graciosa Island straddles the boundary between the subtropics and midlatitudes in the northeast Atlantic Ocean and consequently experiences a great diversity of meteorological and cloudiness conditions. Low clouds are the dominant cloud type, with stratocumulus and cumulus occurring regularly. Approximately half of all clouds contained precipitation detectable as radar echoes below the cloud base. Radar and satellite observations show that clouds with tops from 1 to 11 km contribute more or less equally to surface-measured precipitation at Graciosa. A wide range of aerosol conditions was sampled during the deployment consistent with the diversity of sources as indicated by back-trajectory analysis. Preliminary findings suggest important two-way interactions between aerosols and clouds at Graciosa, with aerosols affecting light precipitation and cloud radiative properties while being controlled in part by precipitation scavenging.
The data from Graciosa are being compared with short-range forecasts made with a variety of models. A pilot analysis with two climate and two weather forecast models shows that they reproduce the observed time-varying vertical structure of lower-tropospheric cloud fairly well but the cloud-nucleating aerosol concentrations less well. The Graciosa site has been chosen to be a permanent fixed ARM site that became operational in October 2013.
Abstract
The Clouds, Aerosol, and Precipitation in the Marine Boundary Layer (CAP-MBL) deployment at Graciosa Island in the Azores generated a 21-month (April 2009–December 2010) comprehensive dataset documenting clouds, aerosols, and precipitation using the Atmospheric Radiation Measurement Program (ARM) Mobile Facility (AMF). The scientific aim of the deployment is to gain improved understanding of the interactions of clouds, aerosols, and precipitation in the marine boundary layer.
Graciosa Island straddles the boundary between the subtropics and midlatitudes in the northeast Atlantic Ocean and consequently experiences a great diversity of meteorological and cloudiness conditions. Low clouds are the dominant cloud type, with stratocumulus and cumulus occurring regularly. Approximately half of all clouds contained precipitation detectable as radar echoes below the cloud base. Radar and satellite observations show that clouds with tops from 1 to 11 km contribute more or less equally to surface-measured precipitation at Graciosa. A wide range of aerosol conditions was sampled during the deployment consistent with the diversity of sources as indicated by back-trajectory analysis. Preliminary findings suggest important two-way interactions between aerosols and clouds at Graciosa, with aerosols affecting light precipitation and cloud radiative properties while being controlled in part by precipitation scavenging.
The data from Graciosa are being compared with short-range forecasts made with a variety of models. A pilot analysis with two climate and two weather forecast models shows that they reproduce the observed time-varying vertical structure of lower-tropospheric cloud fairly well but the cloud-nucleating aerosol concentrations less well. The Graciosa site has been chosen to be a permanent fixed ARM site that became operational in October 2013.
Abstract
During the presummer rainy season (April–June), southern China often experiences frequent occurrences of extreme rainfall, leading to severe flooding and inundations. To expedite the efforts in improving the quantitative precipitation forecast (QPF) of the presummer rainy season rainfall, the China Meteorological Administration (CMA) initiated a nationally coordinated research project, namely, the Southern China Monsoon Rainfall Experiment (SCMREX) that was endorsed by the World Meteorological Organization (WMO) as a research and development project (RDP) of the World Weather Research Programme (WWRP). The SCMREX RDP (2013–18) consists of four major components: field campaign, database management, studies on physical mechanisms of heavy rainfall events, and convection-permitting numerical experiments including impact of data assimilation, evaluation/improvement of model physics, and ensemble prediction. The pilot field campaigns were carried out from early May to mid-June of 2013–15. This paper: i) describes the scientific objectives, pilot field campaigns, and data sharing of SCMREX; ii) provides an overview of heavy rainfall events during the SCMREX-2014 intensive observing period; and iii) presents examples of preliminary research results and explains future research opportunities.
Abstract
During the presummer rainy season (April–June), southern China often experiences frequent occurrences of extreme rainfall, leading to severe flooding and inundations. To expedite the efforts in improving the quantitative precipitation forecast (QPF) of the presummer rainy season rainfall, the China Meteorological Administration (CMA) initiated a nationally coordinated research project, namely, the Southern China Monsoon Rainfall Experiment (SCMREX) that was endorsed by the World Meteorological Organization (WMO) as a research and development project (RDP) of the World Weather Research Programme (WWRP). The SCMREX RDP (2013–18) consists of four major components: field campaign, database management, studies on physical mechanisms of heavy rainfall events, and convection-permitting numerical experiments including impact of data assimilation, evaluation/improvement of model physics, and ensemble prediction. The pilot field campaigns were carried out from early May to mid-June of 2013–15. This paper: i) describes the scientific objectives, pilot field campaigns, and data sharing of SCMREX; ii) provides an overview of heavy rainfall events during the SCMREX-2014 intensive observing period; and iii) presents examples of preliminary research results and explains future research opportunities.
Abstract
The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a coupled general circulation model (CM3) for the atmosphere, oceans, land, and sea ice. The goal of CM3 is to address emerging issues in climate change, including aerosol–cloud interactions, chemistry–climate interactions, and coupling between the troposphere and stratosphere. The model is also designed to serve as the physical system component of earth system models and models for decadal prediction in the near-term future—for example, through improved simulations in tropical land precipitation relative to earlier-generation GFDL models. This paper describes the dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component (AM3) of this model. Relative to GFDL AM2, AM3 includes new treatments of deep and shallow cumulus convection, cloud droplet activation by aerosols, subgrid variability of stratiform vertical velocities for droplet activation, and atmospheric chemistry driven by emissions with advective, convective, and turbulent transport. AM3 employs a cubed-sphere implementation of a finite-volume dynamical core and is coupled to LM3, a new land model with ecosystem dynamics and hydrology. Its horizontal resolution is approximately 200 km, and its vertical resolution ranges approximately from 70 m near the earth’s surface to 1 to 1.5 km near the tropopause and 3 to 4 km in much of the stratosphere. Most basic circulation features in AM3 are simulated as realistically, or more so, as in AM2. In particular, dry biases have been reduced over South America. In coupled mode, the simulation of Arctic sea ice concentration has improved. AM3 aerosol optical depths, scattering properties, and surface clear-sky downward shortwave radiation are more realistic than in AM2. The simulation of marine stratocumulus decks remains problematic, as in AM2. The most intense 0.2% of precipitation rates occur less frequently in AM3 than observed. The last two decades of the twentieth century warm in CM3 by 0.32°C relative to 1881–1920. The Climate Research Unit (CRU) and Goddard Institute for Space Studies analyses of observations show warming of 0.56° and 0.52°C, respectively, over this period. CM3 includes anthropogenic cooling by aerosol–cloud interactions, and its warming by the late twentieth century is somewhat less realistic than in CM2.1, which warmed 0.66°C but did not include aerosol–cloud interactions. The improved simulation of the direct aerosol effect (apparent in surface clear-sky downward radiation) in CM3 evidently acts in concert with its simulation of cloud–aerosol interactions to limit greenhouse gas warming.
Abstract
The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a coupled general circulation model (CM3) for the atmosphere, oceans, land, and sea ice. The goal of CM3 is to address emerging issues in climate change, including aerosol–cloud interactions, chemistry–climate interactions, and coupling between the troposphere and stratosphere. The model is also designed to serve as the physical system component of earth system models and models for decadal prediction in the near-term future—for example, through improved simulations in tropical land precipitation relative to earlier-generation GFDL models. This paper describes the dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component (AM3) of this model. Relative to GFDL AM2, AM3 includes new treatments of deep and shallow cumulus convection, cloud droplet activation by aerosols, subgrid variability of stratiform vertical velocities for droplet activation, and atmospheric chemistry driven by emissions with advective, convective, and turbulent transport. AM3 employs a cubed-sphere implementation of a finite-volume dynamical core and is coupled to LM3, a new land model with ecosystem dynamics and hydrology. Its horizontal resolution is approximately 200 km, and its vertical resolution ranges approximately from 70 m near the earth’s surface to 1 to 1.5 km near the tropopause and 3 to 4 km in much of the stratosphere. Most basic circulation features in AM3 are simulated as realistically, or more so, as in AM2. In particular, dry biases have been reduced over South America. In coupled mode, the simulation of Arctic sea ice concentration has improved. AM3 aerosol optical depths, scattering properties, and surface clear-sky downward shortwave radiation are more realistic than in AM2. The simulation of marine stratocumulus decks remains problematic, as in AM2. The most intense 0.2% of precipitation rates occur less frequently in AM3 than observed. The last two decades of the twentieth century warm in CM3 by 0.32°C relative to 1881–1920. The Climate Research Unit (CRU) and Goddard Institute for Space Studies analyses of observations show warming of 0.56° and 0.52°C, respectively, over this period. CM3 includes anthropogenic cooling by aerosol–cloud interactions, and its warming by the late twentieth century is somewhat less realistic than in CM2.1, which warmed 0.66°C but did not include aerosol–cloud interactions. The improved simulation of the direct aerosol effect (apparent in surface clear-sky downward radiation) in CM3 evidently acts in concert with its simulation of cloud–aerosol interactions to limit greenhouse gas warming.
