Paul C. Loikith

Characteristics of Observed Atmospheric Circulation Patterns Associated with Temperature Extremes over North America

Motivated by a desire to understand the physical mechanisms involved in future anthropogenic changes in extreme temperature events, the key atmospheric circulation patterns associated with extreme daily temperatures over North America in the current climate are identified. The findings show that warm extremes at most locations are associated with positive 500-hPa geopotential height and sea level pressure anomalies just downstream with negative anomalies farther upstream. The orientation, physical characteristics, and spatial scale of these circulation patterns vary based on latitude, season, and proximity to important geographic features (i.e., mountains, coastlines). The anomaly patterns associated with extreme cold events tend to be similar to, but opposite in sign of, those associatedwith extreme warmevents, especially within the westerlies, and tend to scale with temperature in the same locations. Circulation patterns aloft are more coherent across the continent than those at the surface where local surface features influence the occurrence of and patterns associated with extreme temperature days. Temperature extremes may be more sensitive to small shifts in circulation at locations where temperature is strongly influenced by mountains or large water bodies, or at the margins of important large-scale circulation patterns making such locations more susceptible to nonlinear responsestofutureclimatechange.Theidentificationofthesepatternsandprocesseswillallowforathorough evaluation of the ability of climate models to realistically simulate extreme temperatures and their future trends.

Impact of Soil Moisture–Atmosphere Interactions on Surface Temperature Distribution

AbstractUnderstanding how different physical processes can shape the probability distribution function (PDF) of surface temperature, in particular the tails of the distribution, is essential for the attribution and projection of future extreme temperature events. In this study, the contribution of soil moisture–atmosphere interactions to surface temperature PDFs is investigated. Soil moisture represents a key variable in the coupling of the land and atmosphere, since it controls the partitioning of available energy between sensible and latent heat flux at the surface. Consequently, soil moisture variability driven by the atmosphere may feed back onto the near-surface climate—in particular, temperature. In this study, two simulations of the current-generation Geophysical Fluid Dynamics Laboratory (GFDL) Earth System Model, with and without interactive soil moisture, are analyzed in order to assess how soil moisture dynamics impact the simulated climate. Comparison of these simulations shows that soil moist...

The Influence of Recurrent Modes of Climate Variability on the Occurrence of Winter and Summer Extreme Temperatures over North America

The influence of the Pacific‐North American (PNA) pattern, the northern annular mode (NAM), and the El Ni~ Oscillation (ENSO) on extreme temperature days and months over North America is examined. Associations between extreme temperature days and months are strongest with the PNA and NAM andweakerforENSO.Ingeneral,theassociationwithextremestendstobestrongeronmonthlythandailytime scales and for winter as compared tosummer. Extreme temperatures are associated with the PNA and NAM in the vicinity of the centers of action of these circulation patterns; however, many extremes also occur on days when the amplitude and polarity of these patterns do not favor their occurrence. In winter, synoptic-scale, transient weather disturbances are important drivers of extreme temperature days; however, many of these smaller-scale events are concurrent with amplified PNA or NAM patterns. Associations are weaker in summer when other physical mechanisms affecting the surface energybalance, such as anomaloussoil moisture content, also influence the occurrence of extreme temperatures.

Evaluation of the Surface Climatology over the Conterminous United States in the North American Regional Climate Change Assessment Program Hindcast Experiment Using a Regional Climate Model Evaluation System

AbstractSurface air temperature, precipitation, and insolation over the conterminous United States region from the North American Regional Climate Change Assessment Program (NARCCAP) regional climate model (RCM) hindcast study are evaluated using the Jet Propulsion Laboratory (JPL) Regional Climate Model Evaluation System (RCMES). All RCMs reasonably simulate the observed climatology of these variables. RCM skill varies more widely for the magnitude of spatial variability than the pattern. The multimodel ensemble is among the best performers for all these variables. Systematic biases occur across these RCMs for the annual means, with warm biases over the Great Plains (GP) and cold biases in the Atlantic and the Gulf of Mexico (GM) coastal regions. Wet biases in the Pacific Northwest and dry biases in the GM/southern Great Plains also occur in most RCMs. All RCMs suffer problems in simulating summer rainfall in the Arizona–New Mexico region. RCMs generally overestimate surface insolation, especially in the...

Comparison between Observed and Model-Simulated Atmospheric Circulation Patterns Associated with Extreme Temperature Days over North America Using CMIP5 Historical Simulations

Circulation patterns associated with extreme temperature days over North America, as simulated by a suite of climate models, are compared with those obtained from observations. The authors analyze 17 coupled atmosphere‐ocean general circulation models contributing to the fifth phase of the Coupled Model Intercomparison Project. Circulation patterns are defined as composites of anomalies in sea level pressure and 500-hPa geopotential height concurrent with days in the tails of temperature distribution. Several metrics used to systematically describe circulation patterns associated with extreme temperature days are applied to both the observed and model-simulated data. Additionally, self-organizing maps are employed as a means of comparing observed and model-simulated circulation patterns across the North American domain. In general, the multimodel ensemble resembles the observed patterns well, especially in areas removed from complex geographic features (e.g., mountains and coastlines). Individual model results vary; however, the majority of models capture the major features observed. The multimodel ensemble captures several key features, including regional variations in the strength and orientation of atmospheric circulation patterns associated with extreme temperatures, both near the surface and aloft, as well as variations with latitude and season. The results from this work suggest that these models can be used to comprehensively examine the role that changes in atmospheric circulation will play in projected changes in temperature extremes because of future anthropogenic climate warming.

Cloud computing and virtualization within the regional climate model and evaluation system

The Regional Climate Model Evaluation System (RCMES) facilitates the rapid, flexible inclusion of NASA observations into climate model evaluations. RCMES provides two fundamental components. A database (RCMED) is a scalable point-oriented cloud database used to elastically store remote sensing observations and to make them available using a space time query interface. The analysis toolkit (RCMET) is a Python-based toolkit that can be delivered as a cloud virtual machine, or as an installer package deployed using Python Buildout to users in order to allow for temporal and spatial regridding, metrics calculation (RMSE, bias, PDFs, etc.) and end-user visualization. RCMET is available to users in an “offline”, lone scientist mode based on a virtual machine dynamically constructed with model outputs and observations to evaluate; or on an institution’s computational cluster seated close to the observations and model outputs. We have leveraged RCMES within the content of the Coordinated Regional Downscaling Experiment (CORDEX) project, working with the University of Cape Town and other institutions to compare the model output to NASA remote sensing data; in addition we are also working with the North American Regional Climate Change Assessment Program (NARCCAP). In this paper we explain the contribution of cloud computing to RCMES’s specifically describing studies of various cloud databases we evaluated for RCMED, and virtualization toolkits for RCMET, and their potential strengths in delivering user-created dynamic regional climate model evaluation virtual machines for our users.

Surface Temperature Probability Distributions in the NARCCAP Hindcast Experiment: Evaluation Methodology, Metrics, and Results

AbstractMethodology is developed and applied to evaluate the characteristics of daily surface temperature distributions in a six-member regional climate model (RCM) hindcast experiment conducted as part of the North American Regional Climate Change Assessment Program (NARCCAP). A surface temperature dataset combining gridded station observations and reanalysis is employed as the primary reference. Temperature biases are documented across the distribution, focusing on the median and tails. Temperature variance is generally higher in the RCMs than reference, while skewness is reasonably simulated in winter over the entire domain and over the western United States and Canada in summer. Substantial differences in skewness exist over the southern and eastern portions of the domain in summer. Four examples with observed long-tailed probability distribution functions (PDFs) are selected for model comparison. Long cold tails in the winter are simulated with high fidelity for Seattle, Washington, and Chicago, Illi...

Short‐tailed temperature distributions over North America and implications for future changes in extremes

Some regions of North America exhibit nonnormal temperature distributions. Shorter-than-Gaussian warm tails are a special subset of these cases, with potentially meaningful implications for future changes in extreme warm temperatures under anthropogenic global warming. Locations exhibiting shorter-than-Gaussian warm tails would experience a greater increase in extreme warm temperature exceedances than a location with a Gaussian or long warm-side tail under a simple uniform warm shift in the distribution. Here we identify regions exhibiting such behavior over North America and demonstrate the effect of a simple warm shift on changes in extreme warm temperature exceedances. Some locations exceed the 95th percentile of the original distribution by greater than 40% of the time after this uniform shift. While the manner in which distributions change under global warming may be more complex than a simple shift, these results provide an observational baseline for climate model evaluation.

Exploring a graph theory based algorithm for automated identification and characterization of large mesoscale convective systems in satellite datasets

Mesoscale convective systems are high impact convectively driven weather systems that contribute large amounts to the precipitation daily and monthly totals at various locations globally. As such, an understanding of the lifecycle, characteristics, frequency and seasonality of these convective features is important for several sectors and studies in climate studies, agricultural and hydrological studies, and disaster management. This study explores the applicability of graph theory to creating a fully automated algorithm for identifying mesoscale convective systems and determining their precipitation characteristics from satellite datasets. Our results show that applying graph theory to this problem allows for the identification of features from infrared satellite data and the seamlessly identification in a precipitation rate satellite-based dataset, while innately handling the inherent complexity and non-linearity of mesoscale convective systems.

Using joint probability distribution functions to evaluate simulations of precipitation, cloud fraction and insolation in the North America Regional Climate Change Assessment Program (NARCCAP)

This study evaluates model fidelity in simulating relationships between seasonally averaged precipitation, cloud fraction and surface insolation from the North American Regional Climate Change Assessment Project (NARCCAP) hindcast using observational data from ground stations and satellites. Model fidelity is measured in terms of the temporal correlation coefficients between these three variables and the similarity between the observed and simulated joint probability distribution functions (JPDFs) in 14 subregions over the conterminous United States. Observations exhibit strong negative correlations between precipitation/cloud fraction and surface insolation for all seasons, whereas the relationship between precipitation and cloud fraction varies according to regions and seasons. The skill in capturing these observed relationships varies widely among the NARCCAP regional climate models, especially in the Midwest and Southeast coast regions where observations show weak (or even negative) correlations between precipitation and cloud fraction in winter due to frequent non-precipitating stratiform clouds. Quantitative comparison of univariate and JPDFs indicates that model performance varies markedly between regions as well as seasons. This study also shows that comparison of JPDFs is useful for summarizing the performance of and highlighting problems with some models in simulating cloud fraction and surface insolation. Our quantitative metric may be useful in improving climate models by highlighting shortcomings in the formulations related with the physical processes involved in precipitation, clouds and radiation or other multivariate processes in the climate system.