Isoprene emission is an important mechanism for improving the thermotolerance of plant photosystems as temperatures increase. In this study, we measured photosynthesis and isoprene emission in trees along an urban-rural gradient that serves as a proxy for climate change, to understand daily and seasonal responses to changes in temperature and other environmental variables. Leaf-level gas exchange and basal isoprene emission of post oak (Quercus stellata) and sweet gum (Liquidambar styraciflua) were recorded at regular intervals over an entire growing season at urban, suburban, and rural sites in eastern Texas. In addition, the temperature and atmospheric carbon dioxide concentration experienced by leaves were experimentally manipulated in spring, early summer, and late summer. We found that trees experienced lower stomatal conductance and photosynthesis and higher isoprene emission, at the urban and suburban sites compared to the rural site. Path analysis indicated a daily positive effect of isoprene emission on photosynthesis, but unexpectedly, higher isoprene emission from urban trees was not associated with improved photosynthesis as temperatures increased during the growing season. Furthermore, urban trees experienced relatively higher isoprene emission at high CO2 concentrations, while isoprene emission was suppressed at the other sites. These results suggest that isoprene emission may be less beneficial in urban, and potentially future, environmental conditions, particularly if higher temperatures override the suppressive effects of high CO2 on isoprene emission. These are important considerations for modeling future biosphere-atmosphere interactions and for understanding tree physiological responses to climate change.
Precipitation is a primary input for hydrologic, agricultural, and engineering models, so making accurate estimates of it across the landscape is critically important. While the distribution of in-situ measurements of precipitation can lead to challenges in spatial interpolation, gridded precipitation information is designed to produce a full coverage product. In this study, we compare daily precipitation accumulations from the ERA5 Global Reanalysis (hereafter ERA5) and the US Global Historical Climate Network (hereafter GHCN) across the northeastern United States. We find that both the distance from the Atlantic Coast and elevation difference between ERA5 estimates and GHCN observations affect precipitation relationships between the two datasets. ERA5 has less precipitation along the coast than GHCN observations but more precipitation inland. Elevation differences between ERA5 and GHCN observations are positively correlated with precipitation differences. Isolated GHCN stations on mountain peaks, with elevations well above the ERA5 model grid elevation, have much higher precipitation. Summer months (June, July, and August) have slightly less precipitation in ERA5 than GHCN observations, perhaps due to the ERA5 convective parameterization scheme. The heavy precipitation accumulation above the 90th, 95th, and 99th percentile thresholds are very similar for ERA5 and the GHCN. We find that daily precipitation in the ERA5 dataset is comparable to GHCN observations in the northeastern United States and its gridded spatial continuity has advantages over in-situ point precipitation measurements for regional modeling applications.
Global model reanalyses of temperature and radiation are used for many purposes because of their spatial and temporal homogeneity. However, they use sub-models for lakes that are smaller than the model grid. This paper compares the simplified small-lake model, known as FLake, used in the European Centre global reanalysis known as ERA5, with observations made in and near Lake Champlain in northern Vermont. Lake Champlain is a challenging test for the ERA5 FLake model. The lake, which extends over several grid cells, is the lowest region at 30 m above sea level within complex mountain topography. The smoothing of the adjacent mountain topography means that the ERA5 grid cells containing the lake have higher mean elevations then 30 m, and this contributes to a small cool bias in FLake mid-summer temperatures. The seasonal cycle of FLake temperatures has a sharper peak than the observed lake temperatures. In winter, lake temperatures are close to 3°C, while the 30 m deep FLake mixed layer (ML) is near freezing. In May and June, FLake maintains a deep ML, while lake profiles are generally strongly stratified with peak temperatures near the surface several degrees above the model ML. One possible contributing reason is that inflowing river temperatures that are not considered by FLake are as much as 5°C above the lake surface temperature from April to June. The lake does develop a ML structure as it cools from the temperature peak in August, but the FLake ML cools faster and grows deeper in fall. We conclude that the vertical mixing in the FLake ML is stronger than the vertical mixing in Lake Champlain.
Equatorward-moving cold surges occur along the lee of high terrain during the cold season. Even though the east coast of Africa features high terrain, little research exists on cold surges along the African highlands despite the fact that these surges could have potentially large agricultural and societal effects. This paper examines a 5-yr climatology of the most extreme African-highlands cold surges spanning the 2008–12 period. During these years, 186 cold surges occurred to the lee of the African highlands, with 84 events extending between 30° and 35°S (type 1), 27 extending between 25° and 30°S (type 2), and 75 extending equatorward of 25°S (type 3) based on the 1000–850-hPa thickness pattern. This climatology reveals that extreme African-highlands cold surges have a climatological maximum in September. Cold surges of type 1 and type 2 tend to occur throughout the Southern Hemisphere winter and spring, whereas surges of type 3 are generally confined to the winter months. These cold surges can last from 2 to 8 days, with the highest frequency of events spanning a 3-day period. A typical cold-surge event features maximum 925-hPa meridional flow of 30.0–39.9 kt (1 kt = 0.51 m s−1) that most frequently advects cold Antarctic air to between 15.0° and 24.9°S and at times as far as the equator.
Much of the previous research on total and heavy precipitation trends across the Northeastern US (hereafter Northeast) used daily precipitation totals over relatively short periods of record, which do not capture the full range of climate variability and change. Less well understood are the characteristics of long-term changes and synoptic patterns in longer-duration heavy precipitation events across the Northeast. A multi-duration (1, 2, 3, 7, 14, and 30 days), multi-return interval (2, 5, 10, and 50 years) precipitation dataset was used to diagnose changes in various types of precipitation events across the Northeast from 1895 to 2017. Increasing trends were found in all duration and return-interval event combinations with the rarest, longest duration events increasing at faster rates than more frequent, shorter duration ones. Daily 850-hPa geopotential height patterns associated with precipitation events were extracted from Rotated Principal Component Analysis and k-means clustering analysis, which allowed for the main synoptic types present, as well as their structure and evolution to be analyzed. The daily synoptic patterns thus identified were found to be similar across all durations and return-intervals and included: coastal low (Nor’easters, tropical cyclones, and predecessor rain events), deep trough, east coast trough, zonal, and high pressure patterns.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.