ALBERT

Description: We report on the appearance of a 2 km wide, 70 m deep circular depression located 50 km inland of the southwestern margin of the Greenland Ice Sheet that provides the first direct evidence for concentrated, long-term storage, and sudden release, of meltwater at the bed. Drainage of the lake may have been triggered by the recent increase in meltwater runoff. The abundance of such lakes and their potential importance to the ice sheet's hydrologic system and flow regime remain unknown.

Description: A unique stratigraphic sequence of fossil leaves of Eotrigonobalanus furcinervis (extinct trees of the beech family, Fagaceae) from central Germany has been used to derive an atmospheric pCO2 record with multiple data points spanning the late middle to late Eocene, two sampling levels which may be earliest Oligocene, and two samples from later in the Oligocene. Using the inverse relationship between the density of stomata and pCO2, we show that pCO2 decreased continuously from the late middle to late Eocene, reaching a relatively stable low value before the end of the Eocene. Based on the subsequent records, pCO2 in parts of the Oligocene was similar to latest Eocene values. These results show that a decrease in pCO2 preceded the large shift in marine oxygen isotope records that characterizes the Eocene–Oliogocene transition. This may be related to the "hysteresis effect" previously proposed – where a certain threshold of pCO2 change was crossed before the cumulative effects of this and other factors resulted in rapid temperature decline, ice build up on Antarctica and hence a change of climate mode.

Description: Air pollution variability is strongly dependent on meteorology. However, quantifying the impacts of changes in regional climatology on pollution extremes can be difficult due to the many non-linear and competing meteorological influences on the production, transport, and removal of pollutant species. Furthermore, observed pollutant levels at many sites show sensitivities at the extremes that differ from those of the overall mean, indicating relationships that would be poorly characterized by simple linear regressions. To address this challenge, we apply quantile regression to observed daily ozone (O3) and fine particulate matter (PM2.5) levels and reanalysis meteorological fields in the United States over the past decade to specifically identify the meteorological sensitivities of higher pollutant levels. From an initial set of over 1700 possible meteorological indicators (including 28 meteorological variables with 63 different temporal options) we generate reduced sets of O3 and PM2.5 indicators for both summer and winter months, analyzing pollutant sensitivities to each for response quantiles ranging from 2–98%. Primary drivers of high-quantile O3 levels include temperature and relative humidity in the summer, while winter O3 levels are most commonly associated with incoming radiation flux. Drivers of summer PM2.5 include temperature, wind speed, and tropospheric stability at many locations, while stability, humidity, and planetary boundary layer height are the key drivers most frequently associated with winter PM2.5. We find key differences in driver sensitivities across regions and quantiles. For example, we find nationally averaged sensitivities of 95th percentile summer O3 to changes in maximum daily temperature of approximately 0.9 ppb °C−1, while the sensitivity of 50th percentile summer O3 (the annual median) is only 0.6 ppb °C−1. This gap points to differing sensitivities within various percentiles of the pollutant distribution, highlighting the need for statistical tools capable of identifying meteorological impacts across the entire response spectrum.

Description: Air pollution variability is strongly dependent on meteorology. However, quantifying the impacts of changes in regional climatology on pollution extremes can be difficult due to the many non-linear and competing meteorological influences on the production, transport, and removal of pollutant species. Furthermore, observed pollutant levels at many sites show sensitivities at the extremes that differ from those of the overall mean, indicating relationships that would be poorly characterized by simple linear regressions. To address this challenge, we apply quantile regression to observed daily ozone (O3) and fine particulate matter (PM2.5) levels and reanalysis meteorological fields in the USA over the past decade to specifically identify the meteorological sensitivities of higher pollutant levels. From an initial set of over 1700 possible meteorological indicators (including 28 meteorological variables with 63 different temporal options), we generate reduced sets of O3 and PM2.5 indicators for both summer and winter months, analyzing pollutant sensitivities to each for response quantiles ranging from 2 to 98 %. Primary covariates connected to high-quantile O3 levels include temperature and relative humidity in the summer, while winter O3 levels are most commonly associated with incoming radiation flux. Covariates associated with summer PM2.5 include temperature, wind speed, and tropospheric stability at many locations, while stability, humidity, and planetary boundary layer height are the key covariates most frequently associated with winter PM2.5. We find key differences in covariate sensitivities across regions and quantiles. For example, we find nationally averaged sensitivities of 95th percentile summer O3 to changes in maximum daily temperature of approximately 0.9 ppb °C−1, while the sensitivity of 50th percentile summer O3 (the annual median) is only 0.6 ppb °C−1. This gap points to differing sensitivities within various percentiles of the pollutant distribution, highlighting the need for statistical tools capable of identifying meteorological impacts across the entire response spectrum.