Alkyl nitrate formation from the reaction of a series of branched RO2 radicals with NO as a function of temperature and pressure

Alkyl nitrate yields from the NO _x photooxidations of neopentane, 2-methylbutane and 3-methylpentane have been determined over the temperature and pressure ranges 281–323 K and 54–740 torr, respectively. The formation of the alkyl nitrates is attributed to the reaction pathway (1b) $${\text{RO}}_{\text{2}} + {\text{NO}}^{{\text{ }}\underrightarrow {\text{M}}} {\text{ RONO}}_{\text{2}}$$ and rate constant ratios k _1b/(k _1a+k _1b) are estimated, where (1a) is the reaction pathway (1a) $${\text{RO}}_{\text{2}} + {\text{NO}} \to {\text{RONO}}_{\text{2}} .$$ A method for estimating this rate constant ratio for primary, secondary and tertiary alkyl peroxy radicals is presented.     

Surface influence upon vertical profiles in the nocturnal boundary layer

Near-surface wind profiles in the nocturnal boundary layer, depth h, above relatively flat, tree-covered terrain are described in the context of the analysis of Garratt (1980) for the unstable atmospheric boundary layer. The observations at two sites imply a surface-based transition layer, of depth z _*, within which the observed non-dimensional profiles Φ _M ⁰ are a modified form of the inertial sub-layer relation \(\Phi _M \left( {{z \mathord{\left/ {\vphantom {z L}} \right. \kern-0em} L}} \right) = \left( {{{1 + 5_Z } \mathord{\left/ {\vphantom {{1 + 5_Z } L}} \right. \kern-0em} L}} \right)\) according to $$\Phi _M^{\text{0}} \simeq \left( {{{1 + 5z} \mathord{\left/ {\vphantom {{1 + 5z} L}} \right. \kern-\nulldelimiterspace} L}} \right)\exp \left[ { - 0.7\left( {{{1 - z} \mathord{\left/ {\vphantom {{1 - z} z}} \right. \kern-\nulldelimiterspace} z}_ * } \right)} \right]$$ , where z is height above the zero-plane displacement and L is the Monin-Obukhov length. At both sites the depth z _* is significantly smaller than the appropriate neutral value (z _*N ) found from the previous analysis, as might be expected in the presence of a buoyant sink for turbulent kinetic energy.     

Hourly estimation of soil heat flux density at the soil surface with three models and two field methods

Heat flux density at the soil surface (G ₀) was evaluated hourly on a vegetal cover 0.08 m high, with a leaf area index of 1.07 m² m^?2, during daylight hours, using Choudhury et al. (Agric For Meteorol 39:283–297, 1987) ( $ G_0^{\text{rn}} $ ), Santanello and Friedl (J Appl Meteorol 42:851–862, 2003) ( $ G_0^{\text{s}} $ ), and force-restore ( $ G_0^{\text{fr}} $ ) models and the plate calorimetry methodology ( $ G_0^{\text{pco}} $ ), where the gradient calorimetry methodology (G _0R ) served as a reference for determining G ₀. It was found that the peak of G _0R was at 1 p.m., with values that ranged between 60 and 100 W m^?2 and that the G ₀/Rn relation varied during the day with values close to zero in the early hours of the morning and close to 0.25 in the last hours of daylight. The $ G_0^{\text{s}} $ model presented the best performance, followed by the $ G_0^{\text{rn}} $ and $ G_0^{\text{fr}} $ models. The plate calorimetry methodology showed a similar behavior to that of the gradient calorimetry referential methodology.     

Future surface mass balance of the Antarctic ice sheet and its influence on sea level change, simulated by a regional atmospheric climate model

A regional atmospheric climate model with multi-layer snow module (RACMO2) is forced at the lateral boundaries by global climate model (GCM) data to assess the future climate and surface mass balance (SMB) of the Antarctic ice sheet (AIS). Two different GCMs (ECHAM5 until 2100 and HadCM3 until 2200) and two different emission scenarios (A1B and E1) are used as forcing to capture a realistic range in future climate states. Simulated ice sheet averaged 2 m air temperature (T_2m) increases (1.8–3.0 K in 2100 and 2.4–5.3 K in 2200), simultaneously and with the same magnitude as GCM simulated T_2m. The SMB and its components increase in magnitude, as they are directly influenced by the temperature increase. Changes in atmospheric circulation around Antarctica play a minor role in future SMB changes. During the next two centuries, the projected increase in liquid water flux from rainfall and snowmelt, together 60–200 Gt year^?1, will mostly refreeze in the snow pack, so runoff remains small (10–40 Gt year^?1). Sublimation increases by 25–50 %, but remains an order of magnitude smaller than snowfall. The increase in snowfall mainly determines future changes in SMB on the AIS: 6–16 % in 2100 and 8–25 % in 2200. Without any ice dynamical response, this would result in an eustatic sea level drop of 20–43 mm in 2100 and 73–163 mm in 2200, compared to the twentieth century. Averaged over the AIS, a strong relation between $\Updelta$ SMB and $\Updelta\hbox{T}_{2{\rm m}}$ of 98 ± 5 Gt w.e. year^?1 K^?1 is found.     

The gas-phase reaction of ClONO2 with hydrogen chloride

The gas-phase reaction of ClONO₂ with HCl was investigated using two large-volume environmental chambers with analysis by in situ long pathlength Fourier transform infrared absorption spectroscopy. In these chambers the reaction was observed to proceed, at least in part, by heterogenous routes, and an upper limit to the rate constant for the homogeneous gas-phase reaction of geneous routes, and an upper limit to the rate constant for the homogeneous gas-phase reaction of $$k\left( {{\text{ClONO}}_{\text{2}} + {\text{HCl}}} \right) < 1.5 \times 10^{ - 19} {\text{ cm}}^{\text{3}} {\text{ molecule}}^{{\text{ - 1}}} {\text{ s}}^{{\text{ - 1}}}$$ Was derived at 298±2K. Assuming that this room-temperature upper limit to the rate constant is applicable to stratospheric temperatures, this homogeneous gas-phase reaction can be estimated to be of negligible importance as a ClONO₂ loss process in the stratosphere.     

Climate change mitigation: trade-offs between delay and strength of action required

Climate change mitigation via a reduction in the anthropogenic emissions of carbon dioxide (CO₂) is the principle requirement for reducing global warming, its impacts, and the degree of adaptation required. We present a simple conceptual model of anthropogenic CO₂ emissions to highlight the trade off between delay in commencing mitigation, and the strength of mitigation then required to meet specific atmospheric CO₂ stabilization targets. We calculate the effects of alternative emission profiles on atmospheric CO₂ and global temperature change over a millennial timescale using a simple coupled carbon cycle-climate model. For example, if it takes 50 years to transform the energy sector and the maximum rate at which emissions can be reduced is ?2.5% $\text{year}^{-1}$ , delaying action until 2020 would lead to stabilization at 540 ppm. A further 20 year delay would result in a stabilization level of 730 ppm, and a delay until 2060 would mean stabilising at over 1,000 ppm. If stabilization targets are met through delayed action, combined with strong rates of mitigation, the emissions profiles result in transient peaks of atmospheric CO₂ (and potentially temperature) that exceed the stabilization targets. Stabilization at 450 ppm requires maximum mitigation rates of ?3% to ?5% $\text{year}^{-1}$ , and when delay exceeds 2020, transient peaks in excess of 550 ppm occur. Consequently tipping points for certain Earth system components may be transgressed. Avoiding dangerous climate change is more easily achievable if global mitigation action commences as soon as possible. Starting mitigation earlier is also more effective than acting more aggressively once mitigation has begun.     

The observed relation between the Kolmogorov and von Karman constants in the surface boundary layer

In steady, neutrally-stratified flow over uniform terrain, the Kolmogorov constant for the one-dimensional spectrum in the inertial subrange (α ₁) and the von Karman constant of the logarithmic profile (k) are shown to be related by $$\alpha _1 k^{{4 \mathord{\left/ {\vphantom {4 3}} \right. \kern-\nulldelimiterspace} 3}} = \left[ {\frac{{\sum \phi }}{{0.555}}} \right]\left[ {\frac{{nz}}{{\bar U_z }}} \right]^{{2 \mathord{\left/ {\vphantom {2 3}} \right. \kern-\nulldelimiterspace} 3}} \left[ {\frac{{\ln z_2 /z_1 }}{{\bar U_2 - \bar U_1 }}} \right]^2 \simeq 0.136,$$ , where the numerical value results from field measurements recorded in near-ideal conditions. This experimentally-observed Kolmogorov-von Karman ‘K-von K’ product is close to the value designated by a one-dimensional equivalent of the theoretical relation previously given by Roth (1970). More-over, it is in remarkably close agreement with new values of both constants independently proposed in recent years.     

Improvements in Sensible Heat-Flux Parametrization in the High-Resolution Regional Model (HRM) Through the Modified Treatment of the Roughness Length for Heat

We discuss the impact of the differential treatment of the roughness lengths for momentum and heat ( $z_{0\mathrm{m}}$ and $z_{0\mathrm{h}}$ ) in the flux parametrization scheme of the high-resolution regional model (HRM) for a heterogeneous terrain centred around Thiruvananthapuram, India (8.5°N, 76.9°E). The magnitudes of sensible heat flux (H) obtained from HRM simulations using the original parametrization scheme differed drastically from the concurrent in situ observations. With a view to improving the performance of this parametrization scheme, two distinct modifications are incorporated: (1) In the first method, a constant value of 100 is assigned to the $z_{0\mathrm{m}}/z_{0\mathrm{h}}$ ratio; (2) and in the second approach, this ratio is treated as a function of time. Both these modifications in the HRM model showed significant improvements in the H simulations for Thiruvananthapuram and its adjoining regions. Results obtained from the present study provide a first-ever comparison of H simulations using the modified parametrization scheme in the HRM model with in situ observations for the Indian coastal region, and suggest a differential treatment of $z_{0\mathrm{m}}$ and $z_{0\mathrm{h}}$ in the flux parametrization scheme.     

Numerical simulation of particle trajectories in inhomogeneous turbulence,III: Comparison of predictions with experimental data for the atmospheric surface layer

It is shown that predictions of a numerical trajectory-simulation method agree closely with the Project Prairie Grass observations of the concentrations 100 m downwind of a continuous point source of sulphur dioxide if the height (z) dependence of the Lagrangian length scale Λ _L is chosen as: whereL is the Monin-Obukhov length. The value of 0.5 for Λ _L /z in neutral conditions is consistent with the findings of Reid (1979) for the Porton experiment, and is also shown to be the best choice for simulation of an experiment in which concentration profiles were measured a short distance (< 40 m) downwind of an elevated point source of glass beads (40 μn diameter). $$\begin{gathered} \Lambda _L = 0.5z\left( {1 - 6\frac{z}{L}} \right)^{{1 \mathord{\left/ {\vphantom {1 4}} \right. \kern-\nulldelimiterspace} 4}} L< 0 \hfill \\ \Lambda _L = 0.5z/\left( {1 + 5\frac{z}{L}} \right)L > 0 \hfill \\ \end{gathered} $$      

Absolute OH quantum yields in the laser photolysis of nitrate,nitrite and dissolved H2O2 at 308 and 351 nm in the temperature range 278–353 K

Absolute quantum yields for the formation of OH radicals in the laser photolysis of aqueous solutions of NO₃ ^-, NO₂ ^- and H₂O₂ at 308 and 351 nm and as a function of pH and temperature have been measured. A scavenging technique involving the reaction between OH and SCN^- ions and the time resolved detection by visible absorption of the (SCN)₂ ^- radical ion was used to determine the absolute OH yields. The following results were obtained:

1. NO ₃ ^- -photolysis:% MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGceaqabeaacaaIWa% GaaGioaGGaaiab-bcaGiaab6gacaqGTbGaaeOoaiab-bcaGiabfA6a% gnaaBaaaleaacqWFFoWtcqWFxoasaeqaaOGaaiikaiaaikdacaaI5a% GaaGioaiab-bcaGiab-P5aljaacMcacqGH9aqpcqWFGaaicqWFWaam% cqWFUaGlcqWFWaamcqWFXaqmcqWF3aWncqWFGaaicqGHXcqScqWFGa% aicqWFWaamcqWFUaGlcqWFWaamcqWFWaamcqWFZaWmcqWFGaaicaqG% MbGaae4BaiaabkhacaqGGaGaaeinaiaabccacqGHKjYOcaqGGaGaam% iCaiaabIeacaqGGaGaeyizImQaaeiiaiaabMdaaeaacqWFGaaicqWF% GaaicqWFGaaicqWFGaaicqWFGaaicqWFGaaicqWFGaaicqWFGaaicq% WFGaaicqWFGaaicqWFGaaicqWFGaaicqWFGaaicqWFGaaicqqHMoGr% daWgaaWcbaGae83Nd8Kae83LdGeabeaakiaacIcacaWGubGaaiykai% abg2da9iabfA6agnaaBaaaleaacqWFFoWtcqWFxoasaeqaaOGaaiik% aiaaikdacaaI5aGaaGioaiab-bcaGiab-P5aljaacMcacqWFGaaica% qGLbGaaeiEaiaabchacaqGGaWaamWaaeaacaqGOaGaaeymaiaabIda% caqGWaGaaeimaiaabccacqGHXcqScaaI0aGaaGioaiaaicdacaqGPa% GaaeikamaalaaabaGaaeymaaqaaiaabkdacaqG5aGaaeioaaaacaqG% GaGaeyOeI0IaaeiiamaalaaabaGaaeymaaqaaiaadsfaaaGaaeykaa% Gaay5waiaaw2faaiaac6caaaaa!9673!\[\begin{gathered}08 {\text{nm:}} \Phi _{{\rm O}{\rm H}} (298 {\rm K}) = 0.017 \pm 0.003 {\text{for 4 }} \leqslant {\text{ }}p{\text{H }} \leqslant {\text{ 9}} \hfill \\\Phi _{{\rm O}{\rm H}} (T) = \Phi _{{\rm O}{\rm H}} (298 {\rm K}) {\text{exp }}\left[ {{\text{(1800 }} \pm 480{\text{)(}}\frac{{\text{1}}}{{{\text{298}}}}{\text{ }} - {\text{ }}\frac{{\text{1}}}{T}{\text{)}}} \right]. \hfill \\\end{gathered}\] Selected experiments at 351 nm indicate that these results are essentially unchanged.
2. NO ₂ ^- -photolysis:% MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGceaqabeaacaaIWa% GaaGioaGGaaiab-bcaGiaab6gacaqGTbGaaeOoaiab-bcaGiabfA6a% gnaaBaaaleaacqWFFoWtcqWFxoasaeqaaOGaaiikaiaaikdacaaI5a% GaaGioaiab-bcaGiab-P5aljaacMcacqGH9aqpcqWFGaaicqWFOaak% cqWFWaamcqWFUaGlcqWFWaamcqWFXaqmcqWF3aWncqWFGaaicqGHXc% qScqWFGaaicqWFWaamcqWFUaGlcqWFWaamcqWFWaamcqWFXaqmcqWF% PaqkcqWFGaaicaqGMbGaae4BaiaabkhacaqGGaGaaeinaiaabccacq% GHKjYOcaqGGaGaamiCaiaabIeacaqGGaGaeyizImQaaeiiaiaabMda% caqGSaaabaGae8hiaaIae8hiaaIae8hiaaIae8hiaaIae8hiaaIae8% hiaaIae8hiaaIae8hiaaIae8hiaaIae8hiaaIae8hiaaIae8hiaaIa% e8hiaaIae8hiaaIaeuOPdy0aaSbaaSqaaiab-95apjab-D5aibqaba% GccaGGOaGaamivaiaacMcacqGH9aqpcqqHMoGrdaWgaaWcbaGae83N% d8Kae83LdGeabeaakiaacIcacaaIYaGaaGyoaiaaiIdacqWFGaaicq% WFAoWscaGGPaGae8hiaaIaaeyzaiaabIhacaqGWbGaaeiiamaadmaa% baGaaeikaiaabgdacaqG1aGaaeOnaiaabcdacaqGGaGaeyySaeRaae% iiaiaabodacaqG2aGaaeimaiaabMcacaqGOaWaaSaaaeaacaqGXaaa% baGaaeOmaiaabMdacaqG4aaaaiaabccacqGHsislcaqGGaWaaSaaae% aacaqGXaaabaGaamivaaaacaqGPaaacaGLBbGaayzxaaGaaiilaaqa% aiaaiodacaaI1aGaaGymaiaabccacaqGUbGaaeyBaiaabQdacqWFGa% aicqqHMoGrdaWgaaWcbaGae83Nd8Kae83LdGeabeaakiaacIcacaaI% YaGaaGyoaiaaiIdacqWFGaaicqWFAoWscaGGPaGaeyypa0Jae8hiaa% Iae8hkaGIae8hmaaJae8Nla4Iae8hmaaJae8hnaqJae8NnayJae8hi% aaIaeyySaeRae8hiaaIae8hmaaJae8Nla4Iae8hmaaJae8hmaaJae8% xoaKJae8xkaKIae8hiaaIaaeOzaiaab+gacaqGYbGaaeiiaiaabsda% caqGGaGaeyizImQaaeiiaiaadchacaqGibGaaeiiaiaab2dacaqGGa% GaaeioaiaabYcaaeaacqWFGaaicqWFGaaicqWFGaaicqWFGaaicqWF% GaaicqWFGaaicqWFGaaicqWFGaaicqWFGaaicqWFGaaicqWFGaaicq% WFGaaicqWFGaaicqWFGaaicqqHMoGrdaWgaaWcbaGae83Nd8Kae83L% dGeabeaakiaacIcacaWGubGaaiykaiabg2da9iabfA6agnaaBaaale% aacqWFFoWtcqWFxoasaeqaaOGaaiikaiaaikdacaaI5aGaaGioaiab% -bcaGiab-P5aljaacMcacqWFGaaicaqGLbGaaeiEaiaabchacaqGGa% WaamWaaeaacaqGOaGaaeymaiaabIdacaqGWaGaaeimaiaabccacqGH% XcqScaqGGaGaaeinaiaabcdacaqGWaGaaeykaiaabIcadaWcaaqaai% aabgdaaeaacaqGYaGaaeyoaiaabIdaaaGaaeiiaiabgkHiTiaabcca% daWcaaqaaiaabgdaaeaacaWGubaaaiaabMcaaiaawUfacaGLDbaaca% GGUaaaaaa!FC61!\[\begin{gathered}08 {\text{nm:}} \Phi _{{\rm O}{\rm H}} (298 {\rm K}) = (0.017 \pm 0.001) {\text{for 4 }} \leqslant {\text{ }}p{\text{H }} \leqslant {\text{ 9,}} \hfill \\\Phi _{{\rm O}{\rm H}} (T) = \Phi _{{\rm O}{\rm H}} (298 {\rm K}) {\text{exp }}\left[ {{\text{(1560 }} \pm {\text{ 360)(}}\frac{{\text{1}}}{{{\text{298}}}}{\text{ }} - {\text{ }}\frac{{\text{1}}}{T}{\text{)}}} \right], \hfill \\351{\text{ nm:}} \Phi _{{\rm O}{\rm H}} (298 {\rm K}) = (0.046 \pm 0.009) {\text{for 4 }} \leqslant {\text{ }}p{\text{H = 8,}} \hfill \\\Phi _{{\rm O}{\rm H}} (T) = \Phi _{{\rm O}{\rm H}} (298 {\rm K}) {\text{exp }}\left[ {{\text{(1800 }} \pm {\text{ 400)(}}\frac{{\text{1}}}{{{\text{298}}}}{\text{ }} - {\text{ }}\frac{{\text{1}}}{T}{\text{)}}} \right]. \hfill \\\end{gathered}\]
3. H₂O₂-photolysis:% MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGceaqabeaacaaIWa% GaaGioaGGaaiab-bcaGiaab6gacaqGTbGaaeOoaiab-bcaGiabfA6a% gnaaBaaaleaacqWFFoWtcqWFxoasaeqaaOGaaiikaiaaikdacaaI5a% GaaGioaiab-bcaGiab-P5aljaacMcacqGH9aqpcqWFGaaicqWFOaak% cqWFWaamcqWFUaGlcqWF5aqocqWF4aaocqWFGaaicqGHXcqScqWFGa% aicqWFWaamcqWFUaGlcqWFWaamcqWFZaWmcqWFPaqkcqWFGaaicaqG% MbGaae4BaiaabkhacaqGGaGaamiCaiaabIeacaqGGaGaeyizImQaae% iiaiaabEdacaqGSaaabaGae8hiaaIae8hiaaIae8hiaaIae8hiaaIa% e8hiaaIae8hiaaIae8hiaaIae8hiaaIae8hiaaIae8hiaaIae8hiaa% Iae8hiaaIae8hiaaIae8hiaaIaeuOPdy0aaSbaaSqaaiab-95apjab% -D5aibqabaGccaGGOaGaamivaiaacMcacqGH9aqpcqqHMoGrdaWgaa% WcbaGae83Nd8Kae83LdGeabeaakiaacIcacaaIYaGaaGyoaiaaiIda% cqWFGaaicqWFAoWscaGGPaGae8hiaaIaaeyzaiaabIhacaqGWbGaae% iiamaadmaabaGaaeikaiaabAdacaqG2aGaaeimaiaabccacqGHXcqS% caqGGaGaaeymaiaabMdacaqGWaGaaeykaiaabIcadaWcaaqaaiaabg% daaeaacaqGYaGaaeyoaiaabIdaaaGaaeiiaiabgkHiTiaabccadaWc% aaqaaiaabgdaaeaacaWGubaaaiaabMcaaiaawUfacaGLDbaacaGGSa% aabaGaaG4maiaaiwdacaaIXaGaaeiiaiaab6gacaqGTbGaaeOoaiab% -bcaGiabfA6agnaaBaaaleaacqWFFoWtcqWFxoasaeqaaOGaaiikai% aaikdacaaI5aGaaGioaiab-bcaGiab-P5aljaacMcacqGH9aqpcqWF% GaaicqWFOaakcqWFWaamcqWFUaGlcqWF5aqocqWF2aGncqWFGaaicq% GHXcqScqWFGaaicqWFWaamcqWFUaGlcqWFWaamcqWF0aancqWFPaqk% cqWFGaaicaqGMbGaae4BaiaabkhacaqGGaGaaeinaiaabccacqGHKj% YOcaqGGaGaamiCaiaabIeacaqGGaGaaeypaiaabccacaqG3aGaaeil% aaqaaiab-bcaGiab-bcaGiab-bcaGiab-bcaGiab-bcaGiab-bcaGi% ab-bcaGiab-bcaGiab-bcaGiab-bcaGiab-bcaGiab-bcaGiab-bca% Giab-bcaGiabfA6agnaaBaaaleaacqWFFoWtcqWFxoasaeqaaOGaai% ikaiaadsfacaGGPaGaeyypa0JaeuOPdy0aaSbaaSqaaiab-95apjab% -D5aibqabaGccaGGOaGaaGOmaiaaiMdacaaI4aGae8hiaaIae8NMdS% Kaaiykaiab-bcaGiaabwgacaqG4bGaaeiCaiaabccadaWadaqaaiaa% bIcacaqG1aGaaeioaiaabcdacaqGGaGaeyySaeRaaeiiaiaabgdaca% qG2aGaaeimaiaabMcacaqGOaWaaSaaaeaacaqGXaaabaGaaeOmaiaa% bMdacaqG4aaaaiaabccacqGHsislcaqGGaWaaSaaaeaacaqGXaaaba% GaamivaaaacaqGPaaacaGLBbGaayzxaaGaaiOlaaaaaa!F3D0!\[\begin{gathered}08 {\text{nm:}} \Phi _{{\rm O}{\rm H}} (298 {\rm K}) = (0.98 \pm 0.03) {\text{for }}p{\text{H }} \leqslant {\text{ 7,}} \hfill \\\Phi _{{\rm O}{\rm H}} (T) = \Phi _{{\rm O}{\rm H}} (298 {\rm K}) {\text{exp }}\left[ {{\text{(660 }} \pm {\text{ 190)(}}\frac{{\text{1}}}{{{\text{298}}}}{\text{ }} - {\text{ }}\frac{{\text{1}}}{T}{\text{)}}} \right], \hfill \\351{\text{ nm:}} \Phi _{{\rm O}{\rm H}} (298 {\rm K}) = (0.96 \pm 0.04) {\text{for 4 }} \leqslant {\text{ }}p{\text{H = 7,}} \hfill \\\Phi _{{\rm O}{\rm H}} (T) = \Phi _{{\rm O}{\rm H}} (298 {\rm K}) {\text{exp }}\left[ {{\text{(580 }} \pm {\text{ 160)(}}\frac{{\text{1}}}{{{\text{298}}}}{\text{ }} - {\text{ }}\frac{{\text{1}}}{T}{\text{)}}} \right]. \hfill \\\end{gathered}\] Together with the absorption coefficients and an assumed actinic flux within atmospheric droplets of twice the clear air value, the partial photolytic lifetimes (τ_OH) of these molecules at 298 K are estimated as 10.5 d, 5.4 h and 30.3 h for NO₃ ^-, NO₂ ^- and H₂O₂, respectively. These lifetimes will increase by a factor of two (NO₃ ^-, NO₂ ^-) and by 15% (H₂O₂) at T=278 K. Using average ambient concentrations in tropospheric aqueous droplets, the photolytic OH source strengths from these species are calculated to be 2.8×10^-11, 1.3×10^-11 and 1.4×10^-11 mol 1^-1 s^-1 for NO₃ ^-, NO₂ ^- and H₂O₂ respectively.

     

Time-scale and state dependence of the carbon-cycle feedback to climate

Climate and atmospheric CO₂ concentration are intimately coupled in the Earth system: CO₂ influences climate through the greenhouse effect, but climate also affects CO₂ through its impact on the amount of carbon stored on land and in the ocean. The change in atmospheric CO₂ as a response to a change in temperature ( $\varDelta CO_{2}/\varDelta T$ ) is a useful measure to quantify the feedback between the carbon cycle and climate. Using an ensemble of experiments with an Earth system model of intermediate complexity we show a pronounced time-scale dependence of $\varDelta CO_{2}/\varDelta T$ . A maximum is found on centennial scales with $\varDelta CO_{2}/\varDelta T$ values for the model ensemble in the range 5–12 ppm °C^?1, while lower values are found on shorter and longer time scales. These results are consistent with estimates derived from past observations. Up to centennial scales, the land carbon response to climate dominates the CO₂ signal in the atmosphere, while on longer time scales the ocean becomes important and eventually dominates on multi-millennial scales. In addition to the time-scale dependence, modeled $\varDelta CO_{2}/\varDelta T$ show a distinct dependence on the initial state of the system. In particular, on centennial time-scales, high $\varDelta CO_{2}/\varDelta T$ values are correlated with high initial land carbon content. A similar relation holds also for the CMIP5 models, although for $\varDelta CO_{2}/\varDelta T$ computed from a very different experimental setup. The emergence of common patterns like this could prove to usefully constrain the climate–carbon cycle feedback.     

Vertical Distribution of Radiation and Energy Balance Partitioning Within and Above a Lodgepole Pine Stand Recovering from a Recent Insect Attack

The current outbreak of mountain pine beetle (MPB) that started in the late 1990s in British Columbia, Canada, is the largest ever recorded in the north American native habitat of the beetle. The killing of trees is expected to change the vertical distribution of net radiation ( $Q^*$ Q ? ) and the partitioning of latent ( $Q_\mathrm{E}$ Q E ) and sensible ( $Q_\mathrm{H}$ Q H ) heat fluxes in the different layers of an attacked forest canopy. During an intensive observation period in the summer of 2010, eddy-covariance flux and radiation measurements were made at seven heights from ground level up to 1.34 times the canopy height in an MPB-attacked open-canopy forest stand $(\hbox {leaf area index} = 0.55~\mathrm{{m}}^{2}\ \mathrm{{m}}^{-2})$ ( leaf area index = 0.55 m 2 m - 2 ) in the interior of British Columbia, Canada. The lodgepole pine dominated stand with a rich secondary structure (trees and understorey not killed by the beetle) was first attacked by the MPB in 2003 and received no management. In this study, the vertical distribution of the energy balance components and their sources and sinks were analyzed and energy balance closure (EBC) was determined for various levels within the canopy. The low stand density resulted in approximately 60 % of the shortwave irradiance and 50 % of the daily total $Q^*$ Q ? reaching the ground. Flux divergence calculations indicated relatively strong sources of latent heat at the ground and where the secondary structure was located. Only very weak sources of latent heat were found in the upper part of the canopy, which was mainly occupied by dead lodgepole pine trees. $Q_\mathrm{H}$ Q H was the dominant term throughout the canopy, and the Bowen ratio ( $Q_\mathrm{H}/Q_\mathrm{E}$ Q H / Q E ) increased with height in the canopy. Soil heat flux ( $Q_\mathrm{G}$ Q G ) accounted for approximately 4 % of $Q^*$ Q ? . Sensible heat storage in the air ( $\Delta Q_\mathrm{S,H}$ Δ Q S , H ) was the largest of the energy balance storage components in the upper canopy during daytime, while in the lower canopy sensible heat storage in the boles ( $\Delta Q_\mathrm{S,B}$ Δ Q S , B ) and biochemical energy storage ( $\Delta Q_\mathrm{S,C}$ Δ Q S , C ) were the largest terms. $\Delta Q_\mathrm{S,H}$ Δ Q S , H was almost constant from the bottom to above the canopy. $\Delta Q_\mathrm{S,C}$ Δ Q S , C , $\Delta Q_\mathrm{S,B}$ Δ Q S , B and latent heat storage in the air ( $\Delta Q_\mathrm{S,E}$ Δ Q S , E ) varied more than $\Delta Q_\mathrm{S,H}$ Δ Q S , H throughout the canopy. During daytime, energy balance closure was high in and above the upper canopy, and in the lowest canopy level. However, where the secondary structure was most abundant, ${\textit{EBC}} \le 66\,\%$ EBC ≤ 66 % . During nighttime, the storage terms together with $Q_\mathrm{G}$ Q G made up the largest part of the energy balance, while $Q_\mathrm{H}$ Q H and $Q_\mathrm{E}$ Q E were relatively small. These radiation and energy balance measurements in an insect-attacked forest highlight the role of secondary structure in the recovery of attacked stands.     

An Approach to Minimizing Artifacts Caused by Cross-Sensitivity in the Determination of Air–Sea \text{ CO }_{2} Flux Using the Eddy-Covariance Technique

The air–sea $\text{ CO }_{2}$ flux was measured from a research vessel in the North Yellow Sea in October 2007 using an open-path eddy-covariance technique. In 11 out of 64 samples, the normalized spectra of scalars ( $\text{ CO }_{2}$ , water vapour, and temperature) showed similarities. However, in the remaining samples, the normalized $\text{ CO }_{2}$ spectra were observed to be greater than those of water vapour and temperature at low frequencies. In this paper, the noise due to cross-sensitivity was identified through a combination of intercomparisons among the normalized spectra of three scalars and additional analyses. Upon examination, the cross-sensitivity noise appeared to be mainly present at frequencies ${<}0.8\,\text{ Hz }$ . Our analysis also suggested that the high-frequency fluctuations of $\text{ CO }_{2}$ concentration (frequency ${>}0.8\,\text{ Hz }$ ) was probably less affected by the cross-sensitivity. To circumvent the cross-sensitivity issue, the cospectrum in the high-frequency range 0.8–1.5 Hz, instead of the whole range, was used to estimate the $\text{ CO }_{2}$ flux by taking the contribution of the high frequency to the $\text{ CO }_{2}$ flux to be the same as the contribution to the water vapour flux. The estimated air–sea $\text{ CO }_{2}$ flux in the North Yellow Sea was $-0.039\,\pm \,0.048\,\text{ mg } \text{ m }^{-2}\,\text{ s }^{-1},$ a value comparable to the estimates using the inertial dissipation method and Edson’s method (Edson et al., J Geophys Res 116:C00F10, 2011).     

Turbulent Schmidt Numbers Above a Wheat Crop

Measurements of vertical fluxes and concentration differences above a spring wheat crop (height $h=0.9$ – $0.95$  m, row spacing 0.25 m, displacement height $d=0.5$ – $0.6$  m) were analyzed to determine the Schmidt numbers for water vapour ( $S^\mathrm{v}$ ) and carbon dioxide ( $S^\mathrm{c}$ ) based on concentration differences between intakes 2.55 and 3.54 m above the ground. During nearly-neutral stratification $S^\mathrm{v}(0) = 0.68 \pm 0.1$ while $S^\mathrm{c} = 0.78 \pm 0.2$ , implying that the roughness sublayer extended above $2.5 h$ .     

Parametrization of orographical effects in the planetary boundary layer

Studies of the influence of orography on the dynamics of atmospheric processes usually assume the following relation as a boundary condition at the surface of the Earth, or at the top of the planetary layer: $$w = u\frac{{\delta z_0 }}{{\delta x}} + v\frac{{\delta z_0 }}{{\delta y}}$$ where u, v and w are the components of wind velocity along the x, y and z axes, respectively, and z ₀ = z₀(x, y) is the equation of the Earth's orography. We see that w, and consequently the influence of orography on the dynamics of atmospheric processes, depend on the wind (u, v) and on the slope of the obstacle (δz ₀/δx, δz₀/δy). In the present work, it is shown that the above relation for w is insufficient to describe the influence of orography on the dynamics of the atmosphere. It is also shown that the relation is a particular case of the expression: $$\begin{gathered} w_h = \left| {v_g } \right|\left[ {a_1 (Ro,s)\frac{{\delta z_0 }}{{\delta x}} + a_2 (Ro,s)\frac{{\delta z_0 }}{{\delta y}}} \right] + \hfill \\ + \frac{{\left| {v_g } \right|^2 }}{f}\left[ {b_1 (Ro,s)\frac{{\delta ^2 z_0 }}{{\delta x^2 }} + b_2 (Ro,s)\frac{{\delta ^2 z_0 }}{{\delta y^2 }} + b_3 (Ro,s)\frac{{\delta ^2 z_0 }}{{\delta x\delta y}}} \right] \hfill \\ \end{gathered} $$ where ¦vv _g¦ is the strength of the geostrophic wind, a ₁, a₂, b₁, b₂, b₃ are functions of Rossby number Ro and of the external stability parameter s. The above relation is obtained with the help of similarity theory, with a parametrization of the planetary boundary layer. Finally, the authors show that a close connection exists between the effects described by the above expression and cyclo- and anticyclogenesis.     

A Scale-Adaptive Approach for Spatially-Varying Urban Morphology Characterization in Boundary Layer Parametrization Using Multi-Resolution Analysis

Urban morphology characterization is crucial for the parametrization of boundary-layer development over urban areas. One complexity in such a characterization is the three-dimensional variation of the urban canopies and textures, which are customarily reduced to and represented by one-dimensional varying parametrization such as the aerodynamic roughness length $z_{0}$ and zero-plane displacement $d$ . The scope of the paper is to provide novel means for a scale-adaptive spatially-varying parametrization of the boundary layer by addressing this 3-D variation. Specifically, the 3-D variation of urban geometries often poses questions in the multi-scale modelling of air pollution dispersion and other climate or weather-related modelling applications that have not been addressed yet, such as: (a) how we represent urban attributes (parameters) appropriately for the multi-scale nature and multi-resolution basis of weather numerical models, (b) how we quantify the uniqueness of an urban database in the context of modelling urban effects in large-scale weather numerical models, and (c) how we derive the impact and influence of a particular building in pre-specified sub-domain areas of the urban database. We illustrate how multi-resolution analysis (MRA) addresses and answers the afore-mentioned questions by taking as an example the Central Business District of Oklahoma City. The selection of MRA is motivated by its capacity for multi-scale sampling; in the MRA the “urban” signal depicting a city is decomposed into an approximation, a representation at a higher scale, and a detail, the part removed at lower scales to yield the approximation. Different levels of approximations were deduced for the building height $\bar{{H}}$ and planar packing density $\lambda _\mathrm{p}$ . A spatially-varying characterization with a scale-adaptive capacity is obtained for the boundary-layer parameters (aerodynamic roughness length $z_{0}$ and zero-plane displacement $d$ ) using the MRA-deduced results for the building height and the planar packing density with a morphometric model; an attribute that is shown to be of great advantage to multi-scale and multi-resolution numerical weather prediction models.     

Transfer Coefficients of Momentum, Heat and Water Vapour in the Atmospheric Surface Layer of a Large Freshwater Lake

In studies of lake–atmosphere interactions, the fluxes of momentum, water vapour and sensible heat are often parametrized as being proportional to the differences in wind, humidity and air temperature between the water surface and a reference height above the surface. Here, the proportionality via transfer coefficients in these relationships was investigated with the eddy-covariance method at three sites within an eddy-covariance mesonet across Lake Taihu, China. The results indicate that the transfer coefficients decreased with increasing wind speed for weak winds and approached constant values for strong winds. The presence of submerged macrophytes reduced the momentum transfer (drag) coefficient significantly. At the two sites free of submerged macrophytes, the 10-m drag coefficients under neutral stability were 1.8 $(\pm \,0.4) \times \,10^{-3}$ ( ± 0.4 ) × 10 ? 3 and $1.7\,(\pm \,0.3) \times \,10^{-3 }$ 1.7 ( ± 0.3 ) × 10 ? 3 at the wind speed of $9\,\text{ m } \text{ s }^{-1}$ 9 m s ? 1 , which are 38 and 34 % greater than the prediction by the Garratt model for the marine environment.     

Cross-Validation of Open-Path and Closed-Path Eddy-Covariance Techniques for Observing Methane Fluxes

Methane ( ${\mathrm {CH}}_{4}$ ) fluxes observed with the eddy-covariance technique using an open-path ${\mathrm {CH}}_{4}$ analyzer and a closed-path ${\mathrm {CH}}_{4}$ analyzer in a rice paddy field were evaluated with an emphasis on the flux correction methodology. A comparison of the fluxes obtained by the analyzers revealed that both the open-path and closed-path techniques were reliable, provided that appropriate corrections were applied. For the open-path approach, the influence of fluctuations in air density and the line shape variation in laser absorption spectroscopy (hereafter, spectroscopic effect) was significant, and the relative importance of these corrections would increase when observing small ${\mathrm {CH}}_{4}$ fluxes. A new procedure proposed by Li-Cor Inc. enabled us to accurately adjust for these effects. The high-frequency loss of the open-path ${\mathrm {CH}}_{4}$ analyzer was relatively large (11 % of the uncorrected covariance) at an observation height of 2.5 m above the canopy owing to its longer physical path length, and this correction should be carefully applied before correcting for the influence of fluctuations in air density and the spectroscopic effect. Uncorrected ${\mathrm {CH}}_{4}$ fluxes observed with the closed-path analyzer were substantially underestimated (37 %) due to high-frequency loss because an undersized pump was used in the observation. Both the bandpass and transfer function approaches successfully corrected this flux loss. Careful determination of the bandpass frequency range or the transfer function and the cospectral model is required for the accurate calculation of ${\mathrm {CH}}_{4}$ fluxes with the closed-path technique.     

A simplified calibrated model for estimating daily global solar radiation in Madinah, Saudi Arabia

Solar radiation is the most important parameter in defining the energy budget at the surface thereby influencing the hydroclimate. Several empirical models based on air temperature are developed and used in several decision-making needs such as agriculture and energy sector. However, a calibration against direct observations is a priori for implementing such models. A calibrated model is developed for Saudi Arabia (Madinah) based on observations during 2007–2011. The model $ \left( {\mathrm{Rs}=A+B\cdot \mathrm{R}{{\mathrm{s}}_0}{{{\left( {{T_{\max }}-{T_{\min }}} \right)}}^C}} \right) $ is used to estimate daily solar radiation and results show a correlation coefficient of 0.94. The calibrated model outperforms the uncalibrated model available for this location. To increase the confidence, the calibrated model is also compared with a simple artificial neural network.     

Some Statistics of the Temperature Structure Parameter in the Convective Boundary Layer Observed by Sodar

The characteristics of the temporal and height variations of the temperature structure parameter $C_\mathrm{T}^{2}$ in strongly convective situations derived from the sodar echo-signal intensity measurements were analyzed for the first 100 m. It was corroborated that the probability density function (pdf) of the logarithm of $C_\mathrm{T}^{2}$ in the lower convective boundary layer is markedly non-Gaussian, whereas turbulence theory predicts it to be normal. It was also corroborated that the sum of two weighted Gaussians, which characterize the statistics of $C_\mathrm{T}^{2}$ within convective plumes and in their environment and the probability of plume occurrence, well approximates the observed pdfs. It was shown that the height behaviour of the arithmetic mean of $ C_\mathrm{T}^{2}$ (both total and within plumes) follows well a power law $C_\mathrm{T}^{2} (z) \sim z^{-q}$ with the exponent $q$ close to the theoretically predicted value of 4/3. But for the geometrical means of $C_\mathrm{T}^{2}$ (both total and within the plumes), $q$ is close to 1. The difference between arithmetically and geometrically averaged $C_\mathrm{T}^{2}$ profiles was analyzed. The vertical profiles of the standard deviation, skewness and kurtosis of $\hbox {ln}C_\mathrm{T}^{2}$ pdfs were analyzed to show their steady behaviour with height. The standard deviations of the logarithm of $C_\mathrm{T}^{2}$ within the plumes and between them are similar and are 1.5 times less than the total standard deviation. The estimate of the variability index $F_\mathrm{T}$ and its height behaviour were obtained, which can be useful to validate some theoretical and modelling predictions. The vertical profiles of the skewness and kurtosis show the negative asymmetry of pdfs and their flatness, respectively. The spectra of variations in $\hbox {ln}C_\mathrm{T}^{2}$ are shown to be satisfactorily fitted by the power law $f^{-\gamma } $ in the frequency range 0.02 and 0.2 Hz, with the average exponent $\approx $ 1.27  $\pm $  0.22.