Search Results
You are looking at 1 - 4 of 4 items for :
- Author or Editor: Ryan L. Sriver x
- Article x
- Refine by Access: Content accessible to me x
Abstract
Tropical cyclone (TC)-induced ocean vertical mixing can alter the upper-ocean temperature structure, influencing ocean heat content variability and meridional ocean heat transport. TC–ocean interactions can influence tropical variability on seasonal to interannual time scales. Here the impacts of TCs on the global ocean and the associated feedbacks are investigated using a hierarchy of high-resolution global ocean model simulations featuring the Community Earth System Model (CESM). The aim is to understand the potential impact of the model’s self-generated transient TC events on the modeled global ocean. Two ocean-only simulations are performed using the atmosphere boundary conditions from a fully coupled preindustrial CESM simulation configured with 0.25° atmosphere resolution and the nominal 1° ocean resolution (with ~0.25° meridional resolution in the tropics). The high-resolution coupled model is capable of directly simulating TC events with wind structure and climatology generally consistent with observations. TC effects at the ocean–atmosphere boundary are filtered out in one of the ocean simulations (OCN_FILT) while fully retained in the other (OCN_TC) in order to isolate the effect of the TCs on regional and global ocean variability across multiple time scales (from intraseasonal to interdecadal). Results show that the model-simulated TCs can 1) alter surface and subsurface ocean temperature patterns and variability; 2) affect ocean energetics, including increasing ocean mixed layer depth and strengthening subtropical gyre and meridional overturning circulations; and 3) influence ocean meridional heat transport and ocean heat content from seasonal to interannual time scales. Results help provide insights into the model behavior and the physical nature of the effect of TCs within the Earth system.
Abstract
Tropical cyclone (TC)-induced ocean vertical mixing can alter the upper-ocean temperature structure, influencing ocean heat content variability and meridional ocean heat transport. TC–ocean interactions can influence tropical variability on seasonal to interannual time scales. Here the impacts of TCs on the global ocean and the associated feedbacks are investigated using a hierarchy of high-resolution global ocean model simulations featuring the Community Earth System Model (CESM). The aim is to understand the potential impact of the model’s self-generated transient TC events on the modeled global ocean. Two ocean-only simulations are performed using the atmosphere boundary conditions from a fully coupled preindustrial CESM simulation configured with 0.25° atmosphere resolution and the nominal 1° ocean resolution (with ~0.25° meridional resolution in the tropics). The high-resolution coupled model is capable of directly simulating TC events with wind structure and climatology generally consistent with observations. TC effects at the ocean–atmosphere boundary are filtered out in one of the ocean simulations (OCN_FILT) while fully retained in the other (OCN_TC) in order to isolate the effect of the TCs on regional and global ocean variability across multiple time scales (from intraseasonal to interdecadal). Results show that the model-simulated TCs can 1) alter surface and subsurface ocean temperature patterns and variability; 2) affect ocean energetics, including increasing ocean mixed layer depth and strengthening subtropical gyre and meridional overturning circulations; and 3) influence ocean meridional heat transport and ocean heat content from seasonal to interannual time scales. Results help provide insights into the model behavior and the physical nature of the effect of TCs within the Earth system.
Abstract
Understanding future changes in extreme temperature events in a transient climate is inherently challenging. A single model simulation is generally insufficient to characterize the statistical properties of the evolving climate, but ensembles of repeated simulations with different initial conditions greatly expand the amount of data available. We present here a new approach for using ensembles to characterize changes in temperature distributions based on quantile regression that more flexibly characterizes seasonal changes. Specifically, our approach uses a continuous representation of seasonality rather than breaking the dataset into seasonal blocks; that is, we assume that temperature distributions evolve smoothly both day to day over an annual cycle and year to year over longer secular trends. To demonstrate our method’s utility, we analyze an ensemble of 50 simulations of the Community Earth System Model (CESM) under a scenario of increasing radiative forcing to 2100, focusing on North America. As previous studies have found, we see that daily temperature bulk variability generally decreases in wintertime in the continental mid- and high latitudes (>40°). A more subtle result that our approach uncovers is that differences in two low quantiles of wintertime temperatures do not shrink as much as the rest of the temperature distribution, producing a more negative skew in the overall distribution. Although the examples above concern temperature only, the technique is sufficiently general that it can be used to generate precise estimates of distributional changes in a broad range of climate variables by exploiting the power of ensembles.
Abstract
Understanding future changes in extreme temperature events in a transient climate is inherently challenging. A single model simulation is generally insufficient to characterize the statistical properties of the evolving climate, but ensembles of repeated simulations with different initial conditions greatly expand the amount of data available. We present here a new approach for using ensembles to characterize changes in temperature distributions based on quantile regression that more flexibly characterizes seasonal changes. Specifically, our approach uses a continuous representation of seasonality rather than breaking the dataset into seasonal blocks; that is, we assume that temperature distributions evolve smoothly both day to day over an annual cycle and year to year over longer secular trends. To demonstrate our method’s utility, we analyze an ensemble of 50 simulations of the Community Earth System Model (CESM) under a scenario of increasing radiative forcing to 2100, focusing on North America. As previous studies have found, we see that daily temperature bulk variability generally decreases in wintertime in the continental mid- and high latitudes (>40°). A more subtle result that our approach uncovers is that differences in two low quantiles of wintertime temperatures do not shrink as much as the rest of the temperature distribution, producing a more negative skew in the overall distribution. Although the examples above concern temperature only, the technique is sufficiently general that it can be used to generate precise estimates of distributional changes in a broad range of climate variables by exploiting the power of ensembles.
Abstract
Extreme temperature events can have considerable negative impacts on sectors such as health, agriculture, and transportation. Observational evidence indicates the severity and frequency of warm extremes are increasing over much of the United States, but there are sizeable challenges both in estimating extreme temperature changes and in quantifying the relevant associated uncertainties. This study provides a simple statistical framework using a block maxima approach to analyze the representation of warm temperature extremes in several recent global climate model ensembles. Uncertainties due to structural model differences, grid resolution, and internal variability are characterized and discussed. Results show that models and ensembles differ greatly in the representation of extreme temperature over the United States, and variability in tail events is dependent on time and anthropogenic warming, which can influence estimates of return periods and distribution parameter estimates using generalized extreme value (GEV) distributions. These effects can considerably influence the uncertainty of model hindcasts and projections of extremes. Several idealized regional applications are highlighted for evaluating ensemble skill and trends, based on quantile analysis and root-mean-square errors in the overall sample and the upper tail. The results are relevant to regional climate assessments that use global model outputs and that are sensitive to extreme warm temperature. Accompanying this manuscript is a simple toolkit using the R statistical programming language for characterizing extreme events in gridded datasets.
Abstract
Extreme temperature events can have considerable negative impacts on sectors such as health, agriculture, and transportation. Observational evidence indicates the severity and frequency of warm extremes are increasing over much of the United States, but there are sizeable challenges both in estimating extreme temperature changes and in quantifying the relevant associated uncertainties. This study provides a simple statistical framework using a block maxima approach to analyze the representation of warm temperature extremes in several recent global climate model ensembles. Uncertainties due to structural model differences, grid resolution, and internal variability are characterized and discussed. Results show that models and ensembles differ greatly in the representation of extreme temperature over the United States, and variability in tail events is dependent on time and anthropogenic warming, which can influence estimates of return periods and distribution parameter estimates using generalized extreme value (GEV) distributions. These effects can considerably influence the uncertainty of model hindcasts and projections of extremes. Several idealized regional applications are highlighted for evaluating ensemble skill and trends, based on quantile analysis and root-mean-square errors in the overall sample and the upper tail. The results are relevant to regional climate assessments that use global model outputs and that are sensitive to extreme warm temperature. Accompanying this manuscript is a simple toolkit using the R statistical programming language for characterizing extreme events in gridded datasets.
Abstract
A new anomaly coupling technique is introduced into a coarse-resolution dynamic climate model [the Liège Ocean Carbon Heteronomous model (LOCH)–Vegetation Continuous Description model (VECODE)–Earth System Models of Intermediate Complexity Climate deBilt (ECBILT)–Coupled Large-Scale Ice–Ocean model (CLIO)–Antarctic and Greenland Ice Sheet Model (AGISM) ensemble (LOVECLIM)], improving the model’s representation of eastern equatorial Pacific surface temperature variability. The anomaly coupling amplifies the surface diabatic atmospheric forcing within a Gaussian-shaped patch applied in the tropical Pacific Ocean. It is implemented with an improved predictive cloud scheme based on empirical relationships between cloud cover and key state variables. Results are presented from a perturbed physics ensemble systematically varying the parameters controlling the anomaly coupling patch size, location, and amplitude. The model’s optimal parameter combination is chosen through calibration against the observed power spectrum of monthly-mean surface temperature anomalies in the Niño-3 region. The calibrated model exhibits substantial improvement in equatorial Pacific interannual surface temperature variability and robustly reproduces El Niño–Southern Oscillation (ENSO)-like variability. The authors diagnose some of the key atmospheric and oceanic feedbacks in the model important for simulating ENSO-like variability, such as the positive Bjerknes feedback and the negative heat flux feedback, and analyze the recharge–discharge of the equatorial Pacific ocean heat content. They find LOVECLIM robustly captures important ocean dynamics related to thermocline adjustment and equatorial Kelvin waves. The calibrated model demonstrates some improvement in simulating atmospheric feedbacks, but the coupling between ocean and atmosphere is relatively weak. Because of the tractability of LOVECLIM and its consequent utility in exploring long-term climate variability and large ensemble perturbed physics experiments, improved representation of tropical Pacific ocean–atmosphere dynamics in the model may more readily allow for the investigation of the role of tropical Pacific ocean–atmosphere dynamics in past climate changes.
Abstract
A new anomaly coupling technique is introduced into a coarse-resolution dynamic climate model [the Liège Ocean Carbon Heteronomous model (LOCH)–Vegetation Continuous Description model (VECODE)–Earth System Models of Intermediate Complexity Climate deBilt (ECBILT)–Coupled Large-Scale Ice–Ocean model (CLIO)–Antarctic and Greenland Ice Sheet Model (AGISM) ensemble (LOVECLIM)], improving the model’s representation of eastern equatorial Pacific surface temperature variability. The anomaly coupling amplifies the surface diabatic atmospheric forcing within a Gaussian-shaped patch applied in the tropical Pacific Ocean. It is implemented with an improved predictive cloud scheme based on empirical relationships between cloud cover and key state variables. Results are presented from a perturbed physics ensemble systematically varying the parameters controlling the anomaly coupling patch size, location, and amplitude. The model’s optimal parameter combination is chosen through calibration against the observed power spectrum of monthly-mean surface temperature anomalies in the Niño-3 region. The calibrated model exhibits substantial improvement in equatorial Pacific interannual surface temperature variability and robustly reproduces El Niño–Southern Oscillation (ENSO)-like variability. The authors diagnose some of the key atmospheric and oceanic feedbacks in the model important for simulating ENSO-like variability, such as the positive Bjerknes feedback and the negative heat flux feedback, and analyze the recharge–discharge of the equatorial Pacific ocean heat content. They find LOVECLIM robustly captures important ocean dynamics related to thermocline adjustment and equatorial Kelvin waves. The calibrated model demonstrates some improvement in simulating atmospheric feedbacks, but the coupling between ocean and atmosphere is relatively weak. Because of the tractability of LOVECLIM and its consequent utility in exploring long-term climate variability and large ensemble perturbed physics experiments, improved representation of tropical Pacific ocean–atmosphere dynamics in the model may more readily allow for the investigation of the role of tropical Pacific ocean–atmosphere dynamics in past climate changes.
