Person:

McKinnon, Karen Aline

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.

Temperature variability and change over land and ocean exhibit characteristic spatial and temporal structures. Understanding the physical mechanisms underlying these structures provides information about the movement and storage of heat in the climate system. In this thesis, I first analyze, and present an energy balance model for, seasonal temperature variability in the extratropics, which supports the idea that the advection of heat between land and ocean by the mean atmospheric circulation can explain the regional characteristics of seasonal variability over both land and ocean. The model is subsequently combined with a large, representative ensemble of Lagrangian atmospheric trajectories to provide a realistic model of the seasonal cycle in the Northern Hemisphere mid-latitudes. Second, based on the Lagrangian trajectories, a new spatially-resolved metric, termed Relative Land Influence, is developed. The metric quantifies the role of land as compared to ocean in influencing the temperature variability at a given location. In addition to explaining the majority of the spatial pattern of seasonal variability, Relative Land Influence is a significant predictor of the observed temperature change over both land and ocean independently since 1950, suggesting that similar physical processes influence temperature variability on seasonal and decadal timescales. Finally, I explore the tails of temperature distributions in the context of identifying the causes of anomalously hot days in the Eastern United States during peak summer. A coupled ocean-atmosphere mode in the central mid-latitude Pacific is identified, which evolves on a characteristic timescale and ultimately leads to the amplification of a mid-latitude wave train that includes a blocking high over the Eastern United States. The early identification of the sea surface temperature precursors to this mode allows for skillful prediction of heat events at lead times greater than 40 days. The identification of physical processes underlying temperature variability on a range of timescales can inform predictions of how temperature variability may change in the future.

Variations 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 consistent with nonnormal intraseasonal variability. Summer variability, in contrast, shows interannual variations in the spread of the temperature distribution related to changes in the mean, especially in the Midwest. Changes in midwestern temperature variability are qualitatively consistent with those expected from decreases in evapotranspiration and are strongly correlated with a measure of drought intensity. The identified patterns of interannual variations in means and extremes may serve as an analog for modes of variability that can be expected at longer time scales.

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.

Historical observations of temperature underpin our ability to monitor Earth’s climate. We identify a pervasive issue in archived observations from surface stations, wherein the use of varying conventions for units and precision has led to distorted distributions of the data. Apart from the original precision being generally unknown, the majority of archived temperature data are found to be misaligned with the original measurements because of rounding on a Fahrenheit scale, conversion to Celsius, and re-rounding. Furthermore, we show that commonly used statistical methods including quantile regression are sensitive to the finite precision and to double-rounding of the data after unit conversion. To remedy these issues, we present a Hidden Markov Model that uses the differing frequencies of specific recorded values to recover the most likely original precision and units associated with each observation. This precision-decoding algorithm is used to infer the precision of the 644 million daily surface temperature observations in the Global Historical Climate Network database, providing more accurate values for the 63% of samples found to have been biased by double-rounding. The average absolute bias correction across the dataset is 0.018 ◦C, and the average inferred precision is 0.41 ◦C, even though data are archived at 0.1 ◦C precision. These results permit better inference of when record temperatures occurred, correction of rounding effects, and identification of inhomogeneities in surface temperature time series, amongst other applications. The precision-decoding algorithm is generally applicable to rounded observations–including surface pressure, humidity, precipitation, and other temperature data–thereby offering the potential to improve quality-control procedures for many datasets.

High temperature extremes during the growing season can reduce agricultural production. At the same time, agricultural practices can modify temperatures by altering the surface energy budget. Here we identify centennial trends towards more favourable growing conditions in the US Midwest, including cooler summer temperature extremes and increased precipitation, and investigate the origins of these shifts. Statistically significant correspondence is found between the cooling pattern and trends in cropland intensification, as well as with trends towards greater irrigated land over a small subset of the domain. Land conversion to cropland, often considered an important influence on historical temperatures, is not significantly associated with cooling. We suggest that agricultural intensification increases the potential for evapotranspiration, leading to cooler temperatures and contributing to increased precipitation. The tendency for greater evapotranspiration on hotter days is consistent with our finding that cooling trends are greatest for the highest temperature percentiles. Temperatures over rainfed croplands show no cooling trend during drought conditions, consistent with evapotranspiration requiring adequate soil moisture, and implying that modern drought events feature greater warming as baseline cooler temperatures revert to historically high extremes.

Planetary heating can be quantified using top of the atmosphere energy fluxes or through monitoring the heat content of the Earth system. It has been difficult, however, to compare the two methods with each other because of biases in satellite measurements and incomplete spatial coverage of ocean observations. Here we focus on the the seasonal cycle whose amplitude is large relative to satellite biases and observational errors. The seasonal budget can be closed through inferring contributions from high-latitude oceans and marginal seas using the covariance structure of National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM1). In contrast, if these regions are approximated as the average across well-observed regions, the amplitude of the seasonal cycle is overestimated relative to satellite constraints. Analysis of the same CESM1 simulation indicates that complete measurement of the upper ocean would increase the magnitude and precision of interannual trend estimates in ocean heating more than fully measuring the deep ocean.