Karen A. McKinnon

The Northern Hemisphere Extratropical Atmospheric Circulation Response to ENSO: How Well Do We Know It and How Do We Evaluate Models Accordingly?

AbstractApplication of random sampling techniques to composite differences between 18 El Nino and 14 La Nina events observed since 1920 reveals considerable uncertainty in both the pattern and amplitude of the Northern Hemisphere extratropical winter sea level pressure (SLP) response to ENSO. While the SLP responses over the North Pacific and North America are robust to sampling variability, their magnitudes can vary by a factor of 2; other regions, such as the Arctic, North Atlantic, and Europe are less robust in their SLP patterns, amplitudes, and statistical significance. The uncertainties on the observed ENSO composite are shown to arise mainly from atmospheric internal variability as opposed to ENSO diversity. These observational findings pose considerable challenges for the evaluation of ENSO teleconnections in models. An approach is proposed that incorporates both pattern and amplitude uncertainty in the observational target, allowing for discrimination between true model biases in the forced ENSO ...

U.S. Daily Temperatures: The Meaning of Extremes in the Context of Nonnormality

AbstractVariations in extreme daily temperatures are explored in relation to changes in seasonal mean temperature using 1218 high-quality U.S. temperature stations spanning 1900–2012. Extreme temperatures are amplified (or damped) by as much as ±50% relative to changes in average temperature, depending on region, season, and whether daily minimum or maximum temperature is analyzed. The majority of this regional structure in amplification is shown to follow from regional variations in temperature distributions. More specifically, there exists a close relationship between departures from normality and the degree to which extreme changes are amplified relative to the mean. To distinguish between intraseasonal and interannual contributions to nonnormality and amplification, an additional procedure, referred to as z bootstrapping, is introduced that controls for changes in the mean and variance between years. Application of z bootstrapping indicates that amplification of winter extreme variations is generally ...

The Spatial Structure of the Annual Cycle in Surface Temperature: Amplitude, Phase, and Lagrangian History

The climatological annual cycle in surface air temperature, defined by its amplitude and phase lag with respect to solar insolation, is one of the most familiar aspects of the climate system. Here, the authors identify three first-order features of the spatial structure of amplitude and phase lag and explain them using simple physical models. Amplitude and phase lag 1) are broadly consistent with a land and ocean end-member mixing model but 2) exhibit overlap between land and ocean and, despite this overlap, 3) show a systematically greater lag over ocean than land for a given amplitude. Based on previous work diagnosing relative ocean or land influence as an important control on the extratropical annual cycle, the authors use a Lagrangian trajectory model to quantify this influence as the weighted amount of time that an ensemble of air parcels has spent over ocean or land. This quantity explains 84% of the space‐time variance in the extratropical annual cycle, as well as features 1 and 2. All three features can be explained using a simple energy balance model with land and ocean surfaces and an advecting atmosphere. This model explains 94% of the space‐time variance of the annual cycle in an illustrative midlatitude zonal band when incorporating the results of the trajectory model. The aforementioned features of annual variability in surface air temperature thus appear to be explained by the coupling of land and ocean through mean atmospheric circulation.

The changing shape of Northern Hemisphere summer temperature distributions

The occurrence of recent summer temperature extremes in the midlatitudes has raised questions about whether and how the distributions of summer temperature are changing. While it is clear that in most regions the average temperature is increasing, there is less consensus regarding the presence or nature of changes in the shape of the distributions, which can influence the probability of extreme events. Using data from over 4000 weather stations in the Global Historical Climatology Network-Daily database, we quantify the changes in daily maximum and minimum temperature distributions for peak summer in the Northern Hemisphere midlatitudes during 1980–2015 using quantile regression. A large majority (87–88%) of the trends across percentiles and stations can be explained by a shift of the distributions with no change in shape. The remaining variability is summarized through projections onto orthogonal basis functions that are closely related to changes in variance, skewness, and kurtosis. North America and Eurasia show significant shifts in the estimated distributions of daily maximum and minimum temperatures. Although no general change in summer variance is found, variance has regionally increased in Eurasia and decreased in most of North America. Changes in shape that project onto the skewness and kurtosis basis functions have a much smaller spatial scale and are generally insignificant.

Seasonally Resolved Distributional Trends of North American Temperatures Show Contraction of Winter Variability

AbstractThere is considerable interest in determining whether recent changes in the temperature distribution extend beyond simple shifts in the mean. The authors present a framework based on quantile regression, wherein trends are estimated across percentiles. Pointwise trends from surface station observations are mapped into continuous spatial fields using thin-plate spline regression. This procedure allows for resolving spatial dependence of distributional trends, providing uncertainty estimates that account for spatial covariance and varying station density. The method is applied to seasonal near-surface temperatures between 1979 and 2014 to unambiguously assess distributional changes in the densely sampled North American region. Strong seasonal differences are found, with summer trends exhibiting significant warming throughout the domain with little distributional dependence, while the spatial distribution of spring and fall trends show a dipole structure. In contrast, the spread between the 95th and ...

How Well Do We Know ENSO’s Climate Impacts over North America, and How Do We Evaluate Models Accordingly?

abstractThe role of sampling variability in ENSO composites of winter surface air temperature and precipitation over North America during the period 1920–2013 is assessed for observations and ensem...

An “Observational Large Ensemble” to Compare Observed and Modeled Temperature Trend Uncertainty due to Internal Variability

AbstractEstimates of the climate response to anthropogenic forcing contain irreducible uncertainty due to the presence of internal variability. Accurate quantification of this uncertainty is critical for both contextualizing historical trends and determining the spread of climate projections. The contribution of internal variability to uncertainty in trends can be estimated in models as the spread across an initial condition ensemble. However, internal variability simulated by a model may be inconsistent with observations due to model biases. Here, statistical resampling methods are applied to observations in order to quantify uncertainty in historical 50-yr (1966–2015) winter near-surface air temperature trends over North America related to incomplete sampling of internal variability. This estimate is compared with the simulated trend uncertainty in the NCAR CESM1 Large Ensemble (LENS). The comparison suggests that uncertainty in trends due to internal variability is largely overestimated in LENS, which ...

On using the seasonal cycle to interpret extratropical temperature changes since 1950

Extratropical near-surface air temperature variability is explored on three different time scales: the seasonal cycle, observed changes in temperature since 1950, and the equilibrium response to increasing CO2 in an atmospheric general circulation simulation with fixed sea surface temperatures. Exploration is undertaken using an energy balance model (EBM) that parameterizes advective land-ocean heat fluxes. The EBM is tuned only to the climatological seasonal cycle yet captures 47% of the variability in observed multidecadal temperature changes in the extratropics and 78% of the variability in the equilibrated model simulation. The subseasonal time scale of atmosphere-surface heat fluxes explains, at least in the context of this EBM, the ability to infer patterns of multidecadal change using information primarily drawn from the seasonal cycle.

Internal variability and regional climate trends in an Observational Large Ensemble

AbstractRecent observed climate trends result from a combination of external radiative forcing and internally generated variability. To better contextualize these trends and forecast future ones, i...

Modeled and observed multidecadal variability in the North Atlantic jet stream and its connection to sea surface temperatures

AbstractMultidecadal variability in the North Atlantic jet stream in general circulation models (GCMs) is compared with that in reanalysis products of the twentieth century. It is found that almost...