Ecosystems are the largest source of atmospheric methane, an important greenhouse gas whose atmospheric concentration is increasing 1% annually. My dissertation research on the environmental controls over methane flux suggests that there is the potential for significant feedback between climatic change and methane flux from ecosystems.

From 1991 to 1993, I conducted a variety of field experiments in Alaskan tundra, California annual grasslands, and Colorado montane meadow, measuring methane flux with static chambers. Emission rates from the Alaskan wet tundra averaged 52 mg CH4 m-2 d-1 and from California pond margin averaged 38 mg CH4 m-2 d-1. Methane uptake in the Colorado montane meadow averaged -1.2 mg CH4 m-2 d-1 and in the California grassland averaged -0.7 mg CH4 m-2 d-1.

Over the annual cycle and across landscape gradients, methane flux was positively correlated with soil moisture and temperature at all three sites. Moisture was the dominant factor, since unsaturated soils took up methane (small negative flux) while saturated soils emitted methane (large positive flux). The response of flux to changes in water level was rapid and direct, rather than occurring via moisture-induced changes in vegetation. Plants did provide an important pathway for methane emissions, however, by allowing methane to bypass the zone of methane oxidation at the soil or water surface. In the grassland, soil carbon and nitrogen explained 97% of the spatial variation in annually-averaged uptake rates, independent of sheep-grazing.

My research showed that emissions and uptake respond differently to changes in soil micro-climate. An increase in moisture increased emissions but decreased uptake (with an important exception in very dry soils, when further drying decreased uptake rates). Emissions increased exponentially with temperature while uptake showed a weak response to temperature.

A model that takes into account both the differential effect of temperature on emissions and uptake, and the fact that net emissions are the balance of production and consumption, predicts three times more change in global ecosystem emissions in response to a 10°C warming than does a model that ignores these two features and includes only net methane release. Therefore, the magnitude of the eedback generated between methane flux from ecosystems and climate is probably much greater than would be estimated by traditional methane-cycle models, which ignore these two features.

Both biogeophysical and biogeochemical feedbacks to climate change from high-latitude terrestrial ecosystems may be mediated by permafrost thaw. For instance, lakes may expand as relatively intact permafrost begins to thaw but experience drainage with continued permafrost degradation; thawing permafrost also increases the vulnerability of soil carbon to decomposition. In order to quantify these feedbacks, Earth System Models (ESMs) need to adequately represent a number of sub-surface processes.

I improved the lake model in the land-surface component (CLM4) of an ESM by including the physics of snow, ice, and underlying sediment, allowing shallow high-latitude lakes to be adequately simulated. After evaluating the model, I investigated the sensitivity of regional surface fluxes to included lake model processes. The inclusion of snow insulation and lake water phase change each cause 15-30 W m^-2 changes in seasonal surface fluxes, altering the forcing of lakes on the atmosphere. With realistic inclusion of these processes, I predict that the presence of high-latitude lakes causes little net change in mean annual air temperature. However, due to increased sub-surface thermal inertia, lakes moderate the seasonal and diurnal cycle in regions of large lake area like the Canadian Shield. Consequently, the loss of lake area in areas of disappearing permafrost could exacerbate increases in summer daily maximum temperatures due to climate warming by up to 1 °C.

Snow insulation has long been recognized as a key control on permafrost soil thermal regime. I show that the snow thermal rectifier can interact with hydrology to cause significant changes in soil temperature associated with non-thermal anthropogenic forcings. Elevated CO₂ or increased summer rainfall could increase soil water-filled pore space by 0.1-0.2 in some permafrost areas, associated with 1-2 °C increases in mean soil temperature. This warming results from both the increased thermal conductivity of the soil and the increased latent heat of fusion. Because these mechanisms saturate once soils are relatively saturated, the initial soil moisture state is crucial in determining susceptibility to these warming mechanisms. In this model, these warming mechanisms were not effective in substantially increasing permafrost vulnerability to thaw in a severe 21st century transient warming scenario.

In summary, I have shown that explicit representation of snow insulation and of freezing and thawing in lakes and soils can alter the predicted responses of soil and air temperatures to changes in climate.

The observational temperature record is a critical part of our understanding of changes in Earth’s climate. However, large uncertainties remain in our historical measurements of surface, ocean, and atmospheric temperatures. Many of these are introduced by changes in measurement techniques over time, such as changing instrumentation, time of observation, or changes to the surrounding environment not representative of the broader region. Reducing these uncertainties is important to improve our understanding of long-term climate change, and has implications for assessing the magnitude of inter-decadal climate variability, evaluating the performance of climate models, determining the remaining carbon budget to achieve mitigation targets, among other issues.

This dissertation is structured around four lead-authored papers that advance our understanding of the observational temperature record. The first paper, titled Quantifying the Effect of Urbanization on U.S. Historical Climatology Network Temperature Records, quantifies the extent to which changes in urban form surrounding measurement stations have biased long-term temperature records. By comparing temperature trends at urban and rural stations using four different proxy measures of urbanity, we find systematic differences between the raw (unadjusted) urban and rural temperature trends throughout the USHCN period of record. Based on these classifications, urbanization accounts for 14% to 21% of the rise in unadjusted minimum temperatures since 1895 and 6% to 9% since 1960. The homogenization process employed by NOAA effectively removes this urban signal such that it becomes insignificant during the last 50-80 years. In contrast, prior to 1930, only about half of the urban signal is removed. This suggests that biases in the land temperature record from urbanization are potentially significant, but can be effectively detected and removed when the network of observation stations is sufficiently dense to allow for neighbor-based pairwise homogenization.

The second paper is titled Evaluating the Impact of U.S. Historical Climatology Network Homogenization Using the U.S. Climate Reference Network. In this paper the homogenization of surface temperature records in the U.S. is assessed by comparing the old weather station network (USHCN) to a new state-of-the-art U.S. Climate Reference Network (USCRN). The new U.S. Climate Reference Network provides a homogenous set of surface temperature observations that can serve as an effective empirical test of adjustments to raw USHCN stations. By comparing nearby pairs of USHCN and USCRN stations, we find that adjustments make both trends and monthly anomalies from USHCN stations much more similar to those of neighboring USCRN stations for the period from 2004-2015 when the networks overlap. These results improve our confidence in the reliability of homogenized surface temperature records.

The third paper, titled Assessing Recent Warming Using Instrumentally Homogeneous sea Surface Temperature Records, seeks to solve a substantial disagreement between warming rates in different Sea surface temperature (SST) records over the past two decades. SST records are subject to potential biases due to changing instrumentation and measurement practices. Significant differences exist between commonly-used composite sea surface temperature reconstructions from NOAA’s Extended Reconstruction Sea Surface Temperature (ERSST), the Hadley Centre SST data set (HadSST3), and the Japanese Meteorological Agency’s Centennial Observation-Based Estimates of SSTs (COBE-SST) in recent years. The update from ERSST version 3b to version 4 resulted in an increase in the SST trend estimate during the last 18 years from 0.07°C/decade to 0.12°C/decade, indicating a higher rate of warming in recent years and eliminating some of the apparent “pause” in global surface temperatures over that period. We show that ERSST version 4 trends generally agree with largely-independent, near-global and instrumentally-homogeneous SST measurements from floating buoys, Argo floats, and radiometer-based satellite measurements that have been developed and deployed during the past two decades. We find a large cooling bias in ERSSTv3b and smaller but significant cooling biases in HadSST3 and COBE-SST from 2003 to present with respect to most series examined. These results suggest that reported rates of SST warming in recent years have been underestimated in these three datasets due to biases in ship-based measurements.

The fourth paper, titled Evaluating the Performance of Past Climate Model Projections, looks at how well historical climate models published since 1970 have performed compared to observed temperature changes in the years after they were published. Climate models provide an important way to understand future changes in the Earth’s climate. Model projections rely on two things to accurately match observations: accurate modeling of climate physics, and accurate assumptions around future emissions of CO2 and other factors affecting the climate. The best physics-based model will still be inaccurate if it projects future changes in emissions that differ from reality. To account for this, we look at how the relationship between temperature and atmospheric CO2 (and other climate drivers) differs between models and observations. We find that climate models published over the past five decades were generally quite accurate in predicting global warming in the years after publication, particularly when accounting for differences between modeled and actual changes in atmospheric CO2 and other climate drivers. This research should help resolve public confusion around the performance of past climate modeling efforts, and increases our confidence that models are accurately projecting global warming.

Work done in this dissertation has had a notable impact on our understanding and estimates of temperatures. This includes ensuring that urbanization is not biasing our record of land temperatures, testing the performance of land temperature homogenization, resolving differences between ocean temperature records in recent decades, developing a novel sea surface temperature record to help better understand WW2-era uncertainties, and evaluating recent changes in ocean heat content. In an encouraging sign of the impact of our work, the new HadSST4 temperature product from the UK Met Office prominently features comparisons with the instrumentally homogenous sea surface temperature records we developed.

Similarly, the work that I and coauthors have undertaken has changed the approach used in evaluating the performance of GMST climate model projections, demonstrating the need to use common coverage and blended SAT/SST fields to ensure like-to-like comparisons with observations. Evaluating the future projections of old climate models improves our confidence that the current generation of models is accurately capturing the physical processes driving GMST change. This work on evaluating old climate models will be featured prominently in Chapter 1 of the IPCC 6th Assessment Report, where I serve as a contributing author.

With climate change in the Arctic, temperatures are expected to rise at twice the rate as in temperate latitudes. This rapid change has the potential to disrupt local ecosystems and feed back to the global climate as frozen soils thaw and warm. Large stocks of carbon have accumulated in Arctic soils, protected from decomposition by cold, wet, and frozen conditions. With warming and thawing due to climate change, decomposition of this carbon is expected to increase, releasing it to the atmosphere as the greenhouse gases CO2 and methane. While a number of modeling efforts have attempted to quantify this potential feedback, the future Arctic carbon balance remains unknown due to uncertain mechanisms stabilizing soil carbon and complex interactions between vegetation and soils. In studies based in Barrow, Alaska, I address three sources of this uncertainty: (1) the magnitude of methane emissions following soil thaw, (2) interactions between plants, soil carbon, and microbial decomposers, and (3) the sensitivity of soil carbon cycling changes in microclimate.

First, I ask how methane formation, consumption within the soil, and net emission to the atmosphere may change with soil thaw in the Arctic. Loss of permafrost (perennially frozen ground) can lead to large-scale landscape changes, redistributing water and soil. Such physical changes can strongly influence emissions of methane, a greenhouse gas roughly 25 times as potent as CO2, whose future emission rates are highly uncertain. Combining field measurements with statistical modeling, I assess soil methane emissions and microbial methane processes (production and consumption) across a gradient of permafrost thaw. In contrast with many previous studies, I find that more degraded sites have lower methane emissions, a different primary methanogenic pathway, and greater methane oxidation than intact permafrost sites. These differences are greater than soil moisture or temperature data can explain. Additional microtopographic controls accounting for these observations may include differences in water flow and vegetation between intact and degraded polygons.

Second, I ask how changes in plant activity due to climate change may influence the rate of soil carbon decomposition (the priming effect), through interactions between plant roots, microbial decomposers, and soil carbon compounds. In a two-year field experiment, I simulate increased plant root activity and measure its influence on soil carbon decomposition, plant CO2 uptake, mineral nitrogen availability, and microbial communities. I find no measurable relationship between substrate additions and soil organic matter decomposition, nutrient supply, or microbial population size. Treatment-level differences in primary production, however, indicate possible longer-term interactions between vegetation and decomposition. The absence of a measurable priming effect contrasts with numerous published reports documenting a positive priming effect under tightly controlled conditions. This difference may be due to high background variability in ecosystem respiration, a property of this in situ experiment. This chapter is one of the first studies evaluating this plant-soil interaction in a field experimental context, with a representative degree of environmental variability.

Third, I ask how decomposition rates of fast-cycling and slow-cycling soil carbon may be influenced by microclimatic changes. The rate of soil carbon turnover and its sensitivity to environmental variables such as temperature and oxygen availability are both highly uncertain and highly influential for model predictions of the global carbon cycle. In two laboratory incubations, I use natural abundance radiocarbon measurements of CO2 and soil organic matter to determine how fast-cycling and slow-cycling carbon pools respond to temperature changes and transitions between anaerobic and aerobic conditions. Using a novel analytical approach, I find that fast- and slow-cycling carbon pools from these Barrow, Alaska soils have comparable temperature sensitivities, with decomposition from both pools increasing by ~40 % for a 5°C temperature increase. Similarly, decomposition rates were highly sensitive to aerobic vs. anaerobic conditions, with no significant difference in sensitivity between pools. Radiocarbon contents of CO2 and soil organic matter indicate that ancient, slow-cycling carbon is sensitive to decomposition under soil temperature increases and water table changes.

Proposed strategies for managing terrestrial carbon in order to mitigate anthropogenic climate change largely ignore the direct effects of land use change on climate via biophysical processes that alter surface energy and water budgets. Subsequent influences on temperature, hydrology, and atmospheric circulation at regional and global scales could potentially help or hinder climate stabilization efforts. However, due to geographic variability, incomplete understanding of the relevant physical processes, and differences in the spatial scale of biophysical influences compared to greenhouse gases, the best strategies for addressing biophysical aspects of land use change are not clear. To provide insight into this problem, I explore the theoretical implications of various metrics for characterizing the full climate effects of land use change. Furthermore, using a state-of-the-art earth system model coupled to an integrated assessment model that generates scenarios of future anthropogenic climate forcing, I address policy-relevant questions regarding the physical climate system response to land use change.

I demonstrate that the biophysical effects of land use change can be large and vary significantly among policies. Thus ignoring these effects in greenhouse gas policies can lead to unintended consequences. Different hypothetical strategies for meeting an identical global greenhouse gas concentration target - either one with modest afforestation or one with large-scale deforestation for biofuel production - yield different global and regional patterns of climate change, particularly when Boreal forests are converted to agriculture. Additional simulations illuminate the forcing and feedbacks processes that drive regional differences between these scenarios.

Many policies and programs rely on a measure of net CO2 emissions to rank the climate damages of different activities or to generate credits within a trading scheme designed to minimize negative climate outcomes. While it is relatively straightforward to include well-mixed non-CO2 greenhouse gases in such schema using metrics based on radiative forcing, the climate system response to biophysical forcing differs from that of greenhouse gases. I examine the size and nature of this theoretical difference by modeling the equilibrium climate response to equivalent levels of radiative forcing from both land use change and elevated atmospheric CO2 concentrations, drawing conclusions about the climate consequences of including biophysical forcing in carbon markets using this metric.