A document by Omon Obarein. Click on the document to view its contents.

Surface meteorological conditions in the midlatitudes are embedded within and affected by synoptic-scale systems, including the movement and persistence of air masses (AMs). Changes in the frequencies of different AMs over the past several decades could potentially have large effects on ecosystems: each organism is exposed to the synergistic effects of the entire suite of atmospheric variables acting upon it – an inherently multivariate environment – which is best captured using AMs. Utilizing a global-scale AM classification system and a global network of tree-ring widths, we investigate how variation in AM frequency impacts tree growth at over 900 locations. We find that AM frequencies are well-correlated with tree growth, especially in the 12-month period from July in the year prior to growth through June in the year of growth. The most important AMs are Dry-Warm and Humid-Cool AMs, which exhibit average correlations of ρ=-0.4 and ρ=+0.4 with global tree growth, respectively, among commonly sampled tree species, with correlations at some sites exceeding ρ=+/-0.8 in some seasons. Compared to empirical models based solely on temperature and precipitation, modeling using only AM frequencies proved superior at nearly 60% of the sites and for over 80% of the well-sampled (n≥10) species. These results should provide a foundation for using AMs to improve forecasts of tree growth, tree stress and wildfire potential. Long-term reconstructions of AM frequencies back several centuries may also be feasible using tree-ring data, which will help contextualize and temporally extend multivariate perspectives of climate change that utilize such air masses.

Accurate sub-seasonal to seasonal (S2S) weather forecast between 2 weeks and several months is crucial to making informed decisions regarding changes in the risk of extreme weather events, resource management, agriculture, forestry, public health, energy, etc. However, significant gaps exist between the needs of society and what forecasters can produce, especially over longer lead times. Using three goodness-of-fit metrics, this study examined the ability of the SOMs-generated CFSv2 to forecast the correct (observed) circulation pattern, as opposed to the actual observed gridded field over a 90-day forecast period. Mean sea-level pressure (MSLP), near-surface wind (wnd10m), 850-mb temperature (t850), and 700-mb geopotential heights (z700) from the North American Regional Reanalysis were used to categorize the synoptic-scale circulation for three regions (East, West, and Gulf) across North America from January 1979 – December 2016. Expectedly, forecast skill generally decreased from the first day down to the skill of climatology (after 10 -15 days) and also varied regionally, seasonally, and between variables. The forecasts for the winter and summer seasons outperformed others, while t850 and z700 forecasts outperformed wnd10m, except in the west region. More importantly, this study found that the SOMs-generated CFSv2 forecasts improve upon the skill of the raw CFSv2 forecast near the one 1 – 2 weeks lead time. This study thus demonstrates the potential utility of a SOMs-based forecasting method in medium-range weather forecasts.

While many studies comparing atmospheric reanalysis and surface observations have focused on the similarity of mean fields, trends, or frequencies of extreme events, very few have assessed how similar surface observations and reanalysis data sets are in terms of their specific identification of extreme temperature event days. Here, we assess the similarity between surface observations and three reanalysis products: ERA5, ERA5-LAND, and NARR, in terms of the days on which they identify extreme temperature events. We assess similarity from 1979-2016 for 231 locations in the United States and Canada, assessing Extreme Heat and Cold Event days, as well as their counterpart events that are relative for the time of year. Cold Events have a greater match than Heat Events. ERA5 has the greatest match percentage with station data across the study region. Match percentage is greatest in mid-latitude, continental locations, with poorer performance in coastal areas, and the Arctic.

Upon the backdrop of steadily rising global average temperatures, it is the extreme weather events that are arguably more important and impactful than changing averages – especially on human health. This research examines trends in North America of three different parameters of extreme temperature events important to human thermal comfort and public health: their frequency, duration, and spatial extent. Most of the changes are expected; that is, with warmer temperatures there are more frequent extreme heat events that are lasting longer and covering more area. However, we highlight some intriguing divergences from this pattern. For example, despite quickly rising autumn temperatures in northern Canada, a concurrent decrease in temperature variability is resulting in extreme heat events remaining stable and is instead manifest more as significant decreases in extreme cold events. In parts of the western US, even though there is no significant trend in autumn mean temperatures, there is a significant rise in extreme cold events. And, in the southern High Plains in summer, despite little trend in averages, a more negative skew to the distribution is nonetheless leading to significant increases in heat events. Seasonal and geographic variability in the trends of extreme dew point events is also explored. For example, increases in extreme humidity events are ubiquitous throughout most of Canada, particularly in summer; but the US has a northeast (increasing humid events) to southwest (increasing dry events) dichotomy that is strongest in winter. While such nuances might complicate our efforts to broadly generalize the message of climate change, these distinctions suggest a renewed emphasis on local- to regional-scale analyses (rather than larger scales) when providing actionable climate information for planners and policymakers.