ALBERT

Description: Volatilization of ammonia (NH3) from fertilizers and livestock wastes forms a significant pathway of nitrogen losses in agricultural ecosystems and constitutes the largest source of atmospheric emissions of NH3. This paper describes a major update to the process model FAN (Flow of Agricultural Nitrogen), which evaluates NH3 emissions interactively within an Earth system model; in this work, the Community Earth System Model (CESM) is used. The updated version (FANv2) includes a more detailed treatment of both physical and agricultural processes, which allows the model to differentiate between the volatilization losses from animal housings, manure storage, grazed pastures, and the application of manure and different types of mineral fertilizers. The modeled ammonia emissions are first evaluated at a local scale against experimental data for various types of fertilizers and manure, and they are subsequently run globally to evaluate NH3 emissions for 2010–2015 based on gridded datasets of fertilizer use and livestock populations. Comparison of regional emissions shows that FANv2 agrees with previous inventories for North America and Europe and is within the range of previous inventories for China. However, due to higher NH3 emissions in Africa, India, and Latin America, the global emissions simulated by FANv2 (48 Tg N) are 30 %–40 % higher than in the existing inventories.

Description: Abundance-based model evaluations with observations provide critical tests for the simulated mean state in models of intercontinental pollution transport, and under certain conditions may also offer constraints on model responses to emission changes. We compile multiyear measurements of peroxy acetyl nitrate (PAN) available from five mountaintop sites and apply them in a proof-of-concept approach that exploits an ensemble of global chemical transport models (HTAP1) to identify an observational “emergent constraint”. In April, when the signal from anthropogenic emissions on PAN is strongest, simulated PAN at northern midlatitude mountaintops correlates strongly with PAN source–receptor relationships (the response to 20 % reductions in precursor emissions within northern midlatitude continents; hereafter, SRRs). This finding implies that PAN measurements can provide constraints on PAN SRRs by limiting the SRR range to that spanned by the subset of models simulating PAN within the observed range. In some cases, regional anthropogenic volatile organic compound (AVOC) emissions, tracers of transport from different source regions, and SRRs for ozone also correlate with PAN SRRs. Given the large observed interannual variability in the limited available datasets, establishing strong constraints will require matching meteorology in the models to the PAN measurements. Application of this evaluation approach to the chemistry–climate models used to project changes in atmospheric composition will require routine, long-term mountaintop PAN measurements to discern both the climatological SRR signal and its interannual variability.

Description: The co-occurrence of heat waves and pollution events and the resulting high mortality rates emphasize the importance of the co-occurrence of pollution and temperature extremes. Through the use of extreme value theory and other statistical methods, tropospheric surface ozone and temperature extremes and their joint occurrence are analyzed over the United States during the summer months (JJA) using measurements and simulations of the present and future climate and chemistry. Five simulations from the Chemistry-Climate Model Initiative (CCMI) reference experiment using specified dynamics (REFC1SD) were analyzed: the CESM1 CAM4-chem, CHASER, CMAM, MOCAGE and MRI-ESM1r1 simulations. In addition, a 25-year present-day simulation branched off the CCMI REFC2 simulation in the year 2000 and a 25-year future simulation branched off the CCMI REFC2 simulation in 2100 were analyzed using CESM1 CAM4-chem. The last two simulations differed in their concentration of carbon dioxide (representative of the years 2000 and 2100) but were otherwise identical. In general, regions with relatively high ozone extremes over the US do not occur in regions of relatively high temperature extremes. A new metric, the spectral density, is developed to measure the joint extremal dependence of ozone and temperature by evaluating the spectral dependence of their extremes. While in many areas of the country ozone and temperature are highly correlated overall, the correlation is significantly reduced when examined on the higher end of the distributions. Measures of spectral density are less than about 0.35 everywhere, suggesting that at most only about a third of the time do extreme temperatures coincide with extreme ozone. Two regions of the US have the strongest measured extreme dependence of ozone and temperature: the northeast and the southeast. The simulated future increase in temperature and ozone is primarily due to a shift in their distributions, not to an increase in their extremes. The locations where the right-hand side of the temperature distribution does increase (by up to 30 %) are consistent with locations where soil–moisture feedback may be expected. Future changes in the right-hand side of the ozone distribution range regionally between +20 % and −10 %. The location of future increases in the high-end tail of the ozone distribution are weakly related to those of temperature with a correlation of 0.3. However, the regions where the temperature extremes increase are not located where the extremes in ozone are large, suggesting a muted ozone response.

Description: Soil respiration is a key ecosystem function whereby shifts in respiration rates can shift systems from carbon sinks to sources. Soil respiration in permafrost-associated systems is particularly important given climate change driven permafrost thaw that leads to significant uncertainty in resulting ecosystem carbon dynamics. Here we characterize the spatial structure and environmental drivers of soil respiration across a permafrost transition zone. We find that soil respiration is characterized by a non-linear threshold that occurs at active-layer depths greater than 140 cm. We also find that within each season, tree basal area is a dominant driver of soil respiration regardless of spatial scale, but only in spatial domains with significant spatial variability in basal area. Our analyses further show that spatial variation (the coefficient of variation) and mean-variance power-law scaling of soil respiration in our boreal system are consistent with previous work in other ecosystems (e.g., tropical forests) and in population ecology, respectively. Comparing our results to those in other ecosystems suggests that temporally stable features such as tree-stand structure are often primary drivers of spatial variation in soil respiration. If so, this provides an opportunity to better estimate the magnitude and spatial variation in soil respiration through remote sensing. Combining such an approach with broader knowledge of thresholding behavior – here related to active layer depth – would provide empirical constraints on models aimed at predicting ecosystem responses to ongoing permafrost thaw.

Description: Boreal spring climate is uniquely susceptible to solar warming mechanisms because it has expansive snow cover and receives relatively strong insolation. Carbonaceous particles can influence snow coverage by warming the atmosphere, reducing surface-incident solar energy (dimming), and reducing snow reflectance after deposition (darkening). We apply a range of models and observations to explore impacts of these processes on springtime climate, drawing several conclusions: 1) Nearly all atmospheric particles (those with visible-band single-scatter albedo less than 0.999), including all mixtures of black carbon (BC) and organic matter (OM), increase net solar heating of the atmosphere-snow column. 2) Darkening caused by small concentrations of particles within snow exceeds the loss of absorbed energy from concurrent dimming, thus increasing solar heating of snowpack as well (positive net surface forcing). Over global snow, we estimate 6-fold greater surface forcing from darkening than dimming, caused by BC+OM. 3) Equilibrium climate experiments suggest that fossil fuel and biofuel emissions of BC+OM induce 95% as much springtime snow cover loss over Eurasia as anthropogenic carbon dioxide, a consequence of strong snow-albedo feedback and large BC+OM emissions from Asia. 4) Of 22 climate models contributing to the IPCC Fourth Assessment Report, 21 underpredict the rapid warming (0.64°C decade−1) observed over springtime Eurasia since 1979. Darkening from natural and anthropogenic sources of BC and mineral dust exerts 3-fold greater forcing on springtime snow over Eurasia (3.9 W m−2) than North America (1.2 W m−2). Inclusion of this forcing significantly improves simulated continental warming trends, but does not reconcile the low bias in rate of Eurasian spring snow cover decline exhibited by all models.

Description: We examine the response of Arctic gas and aerosol concentrations to perturbations in pollutant emissions from Europe, East and South Asia, and North America using results from a coordinated model intercomparison. These sensitivities to regional emissions (mixing ratio change per unit emission) vary widely across models and species. Intermodel differences are systematic, however, so that the relative importance of different regions is robust. North America contributes the most to Arctic ozone pollution. For aerosols and CO, European emissions dominate at the Arctic surface but Asian emissions become progressively more important with altitude, and are dominant in the upper troposphere. Sensitivities show strong seasonality: surface sensitivities typically maximize during boreal winter for European and during spring for East Asian and North American emissions. Mid-tropospheric sensitivities, however, nearly always maximize during spring or summer for all regions. Deposition of black carbon (BC) onto Greenland is most sensitive to North American emissions. North America and Europe each contribute ~40% of total BC deposition to Greenland, with ~20% from East Asia. Elsewhere in the Arctic, both sensitivity and total BC deposition are dominated by European emissions. Model diversity for aerosols is especially large, resulting primarily from differences in aerosol physics and removal. Comparison of aerosols with observations indicates problems in either the models or interpretation of the measurements. For gas phase pollutants such as CO and O3, which are relatively well-simulated, the processes contributing most to uncertainties depend on the source region. Uncertainties in the Arctic surface CO response to emissions perturbations are dominated by emissions for East Asian sources, while uncertainties in transport, emissions, and oxidation are comparable for European and North American sources. At higher levels, model-to-model variations in transport and oxidation are most important. Differences in photochemistry appear to play the largest role in the intermodel variations in Arctic ozone sensitivity.

Description: A method is presented to calculate UV irradiances on inclined surfaces that additionally takes into account the influence of sky obstructions caused by obstacles such as mountains, houses, trees, or umbrellas. Thus the method allows calculating the impact of UV radiation on biological systems, such as for instance the human skin or eye, in any natural or artificial environment. The method, a combination of radiation models, is explained and the correctness of its results is demonstrated. The effect of a natural skyline is shown for an Alpine ski area, where the UV irradiance even on a horizontal surface may increase due to reflection at snow by more than 10%. In contrast in a street canyon the irradiance on a horizontal surface is reduced down to 30% in shadow and to about 75% for a position in the sun.

Description: We present a global chemical data assimilation system using a global atmosphere model, the Community Atmosphere Model (CAM3) with simplified chemistry and the Data Assimilation Research Testbed (DART) assimilation package. DART is a community software facility for assimilation studies using the ensemble Kalman filter approach. Here, we apply the assimilation system to constrain global tropospheric carbon monoxide (CO) by assimilating meteorological observations of temperature and horizontal wind velocity and satellite CO retrievals from the Measurement of Pollution in the Troposphere (MOPITT) satellite instrument. We verify the system performance using independent CO observations taken on board the NSF/NCAR C-130 and NASA DC-8 aircrafts during the April 2006 part of the Intercontinental Chemical Transport Experiment (INTEX-B). Our evaluations show that MOPITT data assimilation provides significant improvements in terms of capturing the observed CO variability relative to no MOPITT assimilation (i.e. the correlation improves from 0.62 to 0.71, significant at 99% confidence). The assimilation provides evidence of median CO loading of about 150 ppbv at 700 hPa over the NE Pacific during April 2006. This is marginally higher than the modeled CO with no MOPITT assimilation (~140 ppbv). Our ensemble-based estimates of model uncertainty also show model overprediction over the source region (i.e. China) and underprediction over the NE Pacific, suggesting model errors that cannot be readily explained by emissions alone. These results have important implications for improving regional chemical forecasts and for inverse modeling of CO sources and further demonstrates the utility of the assimilation system in comparing non-coincident measurements, e.g. comparing satellite retrievals of CO with in-situ aircraft measurements.

Description: Two 40-year meteorological datasets are used to drive the Model of Ozone and Related Tracers chemical transport model, version 2 (MOZART2) in hindcast simulations. One dataset is from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis, the second dataset uses meteorology from the Community Atmosphere Model (CAM3) forced with observed interannually varying sea surface temperatures. All emissions, except those from lightning are annually constant. Analysis of these simulations is from 1979–1999, due to meteorological discontinuities in the NCEP reanalysis during the 1970s. The meteorology using CAM3 captures observed trends in temperature, water vapor, precipitation and cloudiness; the simulation using NCEP meteorology does not. This paper examines the regional and global interannual variability of various chemical and meteorological fields: CO, OH, O3 and HNO3, the surface photolysis rate of NO2 (as a proxy for overhead cloudiness), lightning NO emissions, water vapor, planetary boundary layer height, and temperature. The variability due to changes in emissions is not considered in this analysis. In both the NCEP and CAM3 simulations the relative variability of CO, OH, O3 and HNO3 are qualitatively similar, with variability maxima both in the tropics and the high latitudes. Locally, relative variability generally ranges between 3 and 10%; globally the tropospheric variability generally ranges from half to one percent, but can be higher. For most fields the leading Empirical Orthogonal Function explains approximately 10% of the variability and correlates significantly with El Niño. In both simulations the first principal component of a multiple tracer, globally averaged analysis shows a strong coupling between surface temperature, measures of the hydrological cycle, CO and OH, but is not correlated with El Niño. In both simulations we examine the global response of the selected variables to changes in global surface temperature, and compare with a climate simulation over the 21st century.

Description: Two 40-year meteorological datasets are used to drive the Model of Ozone and Related Tracers chemical transport model, version 2 (MOZART2) in hindcast simulations. One dataset is from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis, the second dataset uses meteorology from the Community Atmosphere Model (CAM3) forced with observed interannually varying sea surface temperatures. All emissions, except those from lightning are annually constant. Analysis of these simulations focuses on the period between 1979–1999, due to meteorological discontinuities in the NCEP reanalysis during the 1970s. The meteorology using CAM3 captures observed trends in temperature and water vapor; the simulation using NCEP meteorology does not. This paper examines the regional and global interannual variability of various chemical and meteorological fields: CO, OH, O3 and HNO3, the surface photolysis rate of NO2 (as a proxy for overhead cloudiness), lightning NO emissions, water vapor, planetary boundary layer height, and temperature. The variability due to changes in emissions is not considered in this analysis. In both the NCEP and CAM3 simulations the relative variability of CO, OH, O3 and HNO3 are qualitatively similar, with variability maxima both in the tropics and the high latitudes. Locally, relative variability generally ranges between 3 and 10%; globally the tropospheric variability generally ranges from half to one percent, but can be higher. For most fields the leading global Empirical Orthogonal Function explains approximately 10% of the variability and correlates significantly with El Niño. In both simulations the first principal component of a multiple tracer, globally averaged analysis shows a strong coupling between surface temperature, measures of the hydrological cycle, CO and OH, but is not correlated with El Niño. In both simulations we examine the global response of the selected variables to changes in global surface temperature, and compare with a climate simulation over the 21st century.