Search Results

You are looking at 31 - 40 of 82 items for

  • Author or Editor: Michael K. Tippett x
  • Refine by Access: All Content x
Clear All Modify Search
Chiara Lepore
,
John T. Allen
, and
Michael K. Tippett

Abstract

Rainfall intensity displays relationships with atmospheric conditions such as surface temperature, and these relationships have implications for how the intensity of rainfall varies with climate. Here, hourly gauge measurements of rainfall over the contiguous United States (CONUS) are related to atmospheric variables taken from the North American Regional Reanalysis for the period 1979–2012. This analysis extends previous work relating the rainfall process to the environment by including a wider range of variables in univariate and bivariate quantile regressions. Known covariate relationships are used to quantify the regional contributions of different weather regimes to rainfall occurrence and to identify preferential atmospheric states for rainfall occurrence. The efficiency of different sets of regressors is evaluated, and the results show that both moisture availability and vertical instability should be taken into account, with CAPE in combination with specific humidity or dewpoint temperature being the most powerful regressors. Different regions and seasons behave differently, pointing to the challenges of constructing global or CONUS-wide models for representing the rainfall process. In particular, the relationships between environment and rainfall in the west of the United States are different from other regions, reflecting nonlocal rainfall processes. Most of the coastal and eastern United States display relatively low regional variability in the relationships between rainfall and environment.

Full access
Michelle L. L’Heureux
,
Michael K. Tippett
, and
Anthony G. Barnston

Abstract

Two questions are addressed in this paper: whether ENSO can be adequately characterized by simple, seasonally invariant indices and whether the time series of a single component—SST or OLR—provides a sufficiently complete representation of ENSO for the purpose of quantifying U.S. climate impacts. Here, ENSO is defined as the leading mode of seasonally varying canonical correlation analysis (CCA) between anomalies of tropical Pacific SST and outgoing longwave radiation (OLR). The CCA reveals that the strongest regions of coupling are mostly invariant as a function of season and correspond to an OLR region located in the central Pacific Ocean (CP-OLR) and an SST region in the eastern Pacific that coincides with the Niño-3 region. In a linear context, the authors explore whether the use of a combined index of these SST and OLR regions explains additional variance of North American temperature and precipitation anomalies beyond that described by using a single index alone. Certain seasons and regions benefit from the use of a combined index. In particular, a combined index describes more variability in winter/spring precipitation and summer temperature.

Full access
Michael K. Tippett
,
Timothy DelSole
, and
Anthony G. Barnston

Abstract

Regression is often used to calibrate climate model forecasts with observations. Reliability is an aspect of forecast quality that refers to the degree of correspondence between forecast probabilities and observed frequencies of occurrence. While regression-corrected climate forecasts are reliable in principle, the estimated regression parameters used in practice are affected by sampling error. The low skill and small sample sizes typically encountered in climate prediction imply substantial sampling error in the estimated regression parameters. Here the reliability of regression-corrected climate forecasts is analyzed for the case of joint-Gaussian distributed ensemble forecasts and observations with regression parameters estimated by least squares. Hypothesis testing of the regression parameters provides direct information about the skill and reliability of the uncorrected ensemble-based probability forecasts. However, the regression-corrected probability forecasts with estimated parameters are systematically “overconfident” because sampling error causes a positive bias in the regression forecast signal variance, despite the fact that the estimates of the regression parameters are themselves unbiased. An analytical description of the reliability diagram of a generic regression-corrected climate forecast is derived and is shown to depend on sample size and population correlation skill, with small sample size and low skill being factors that increase overconfidence. The analytical reliability estimate is shown to capture the effect of sampling error in synthetic data experiments and in a 29-yr dataset of NOAA Climate Forecast System version 2 predictions of seasonal precipitation totals over the Americas. The impact of sampling error on the reliability of regression-corrected forecast has been previously unrecognized and affects all regression-based forecasts. The use of regression parameters estimated by shrinkage methods such as ridge regression substantially reduces overconfidence.

Full access
Michael K. Tippett
,
Anthony G. Barnston
, and
Shuhua Li

Abstract

The performance of the International Research Institute for Climate and Society “ENSO forecast plume” during the 2002–11 period is evaluated using deterministic and probabilistic verification measures. The plume includes multiple model forecasts of the Niño-3.4 index for nine overlapping 3-month periods beginning the month following the latest observations. Skills decrease with increasing lead time and are highest for forecasts made after the northern spring predictability barrier for target seasons occurring prior to the forthcoming such barrier. Forecasts are found to verify systematically better against observations occurring earlier than the intended forecast targets, an effect that becomes greater with increasing lead time. During the study period, the mean forecasts of dynamical models appear to slightly (and statistically insignificantly) outperform those of statistical models, representing a subtle shift from earlier studies. The mean forecasts of dynamical models have overall larger anomalies but similar errors to those of statistical models. Intermodel spread is related to forecast error in an average sense with changes in forecast error due to changes in lead and verification season being properly reflected in changes in spread. The intermodel spread underestimates the forecast error variance, to a greater extent for statistical forecasts than for dynamical ones. Year-to-year changes in plume spread provide little additional information relative to climatological ones.

Full access
Laurie Trenary
,
Timothy DelSole
,
Michael K. Tippett
, and
Brian Doty
Full access
Laurie Trenary
,
Timothy DelSole
,
Brian Doty
, and
Michael K. Tippett
Full access
Youmin Tang
,
Hai Lin
,
Jacques Derome
, and
Michael K. Tippett

Abstract

In this study, ensemble seasonal predictions of the Arctic Oscillation (AO) were conducted for 51 winters (1948–98) using a simple global atmospheric general circulation model. A means of estimating a priori the predictive skill of the AO ensemble predictions was developed based on the relative entropy (R) of information theory, which is a measure of the difference between the forecast and climatology probability density functions (PDFs). Several important issues related to the AO predictability, such as the dominant precursors of forecast skill and the degree of confidence that can be placed in an individual forecast, were addressed. It was found that R is a useful measure of the confidence that can be placed on dynamical predictions of the AO. When R is large, the prediction is likely to have a high confidence level whereas when R is small, the prediction skill is more variable. A small R is often accompanied by a relatively weak AO index. The value of R is dominated by the predicted ensemble mean. The relationship identified here, between model skills and the R of an ensemble prediction, offers a practical means of estimating the confidence level of a seasonal forecast of the AO using the dynamical model.

Through an analysis of the global sea surface temperature (SST) forcing, it was found that the winter AO-related R is correlated significantly with the amplitude of the SST anomalies over the tropical central Pacific and the North Pacific during the previous October. A large value of R is usually associated with strong SST anomalies in the two regions, whereas a poor prediction with a small R indicates that SST anomalies are likely weak in these two regions and the observed AO anomaly in the specific winter is likely caused by atmospheric internal dynamics.

Full access
Kelsey Malloy
,
Michael K. Tippett
, and
William J. Koshak

Abstract

Cloud-to-ground (CG) lightning substantially impacts human health and property. However, the relations between U.S. lightning activity and the Madden–Julian oscillation (MJO) and El Niño–Southern Oscillation (ENSO), two predictable drivers of global climate variability, remain uncertain, in part because most lightning datasets have short records that cannot robustly reveal MJO- and ENSO-related patterns. To overcome this limitation, we developed an empirical model of 6-hourly lightning flash count over the contiguous United States (CONUS) using environmental variables (convective available potential energy and precipitation) and National Lightning Detection Network data for 2003–16. This model is shown to reproduce the observed daily and seasonal variability of lightning over most of CONUS. Then, the empirical model was applied to construct a proxy lightning dataset for the period 1979–2021, which was used to investigate the summer MJO–lightning relationship at daily resolution and the winter–spring ENSO–lightning relationship at seasonal resolution. Overall, no robust relationship between MJO phase and lightning patterns was found when seasonality was taken into consideration. El Niño is associated with increased lightning activity over the coastal Southeast United States during early winter, the Southwest during winter through spring, and the Northwest during late spring, whereas La Niña is associated with increased lightning activity over the Tennessee River valley during winter.

Significance Statement

Cloud-to-ground lightning is dangerous for humans via direct strikes or through triggering wildfires, generating air pollution, etc. How lightning activity can be affected by climate remains unclear, and it is challenging to study their links because the data record for lightning is short. With the available lightning record, we developed a model that relates lightning flash counts over the United States to environmental factors. This model well represents observed fluctuations in daily and seasonal lightning over most of the United States. Because the model only needs environmental information to predict lightning flash counts, we were able to construct a longer record of predicted lightning based on the longer data record of environmental variables. With this dataset, we investigated the links between lightning and climate and found that the state of sea surface temperatures in the tropical Pacific (El Niño–Southern Oscillation) is linked to changes in U.S. lightning patterns in winter and spring.

Restricted access
Michael K. Tippett
,
Anthony G. Barnston
, and
Timothy DelSole
Full access
Simon H. Lee
,
Michael K. Tippett
, and
Lorenzo M. Polvani

Abstract

Weather regimes defined through cluster analysis concisely categorize the anomalous regional circulation pattern on any given day. Owing to their persistence and low dimensionality, regimes are increasingly used in subseasonal-to-seasonal prediction and in analysis of climate variability and change. However, a limitation of existing regime classifications for North America is their seasonal dependence, with most existing studies defining regimes for winter only. Here, we normalize the seasonal cycle in daily geopotential height variance and use empirical orthogonal function analysis combined with k-means clustering to define a new set of year-round North American weather regimes: the Pacific Trough, Pacific Ridge, Alaskan Ridge, and Greenland High regimes. We additionally define a “No Regime” state to represent conditions close to climatology. To demonstrate the robustness of the classification, a thorough assessment of the sensitivity of the clustering solution to various methodological choices is provided. The median persistence of all four regimes, obtained without imposing a persistence criterion, is found to be one week, approximately 3 times longer than the median persistence of the No Regime state. Regime-associated temperature and precipitation anomalies are reported, together with the relationship between the regimes and modes of climate variability. We also quantify historical trends in the frequency of the regimes since 1979, finding a decrease in the annual frequency of the Pacific Trough regime and an increase in the summertime frequency of the Greenland High regime. This study serves as a foundation for the future use of these regimes in a variety of weather and climate applications.

Significance Statement

Weather regimes provide a simple way of classifying daily large-scale regional weather patterns into a few predefined types. Existing methods usually define regimes for a specific season (typically winter), which limits their use, or provides only a minimal assessment of their robustness. In this study, we objectively quantify four weather regimes for use year-round over North America, while we classify near-normal conditions as No Regime. The four regimes represent persistent large-scale weather types that last for about a week and occasionally much longer. Our new classification can be applied to subseasonal-to-seasonal forecasts and climate model output to diagnose recurrent weather types across the North American continent.

Restricted access