Existing infrastructure and the 2°C target

To clarify the link between existing infrastructure legacy and the 2°C target, we extend the work of Davis et al. (Science 329:1330–1333, 2010) by introducing non-CO₂ greenhouse gases and the inertia in transportation-needs drivers. We conclude that climate policies able to maintain climate change below 2°C cannot disregard existing infrastructure.     

Comparison Between GAMIL, and CAM2 on Interannual Variability Simulation

Recently, a new atmospheric general circulation model （GAMIL： Grid-point Atmospheric Model of IAP LASG） has been developed at the Institute of Atmospheric Physics （IAP）, Chinese Academy of Sciences （CAS）, which is based on the Community Atmospheric Model Version 2 （CAM2） of the National Center for Atmospheric Research （NCAR）. Since the two models have the same physical processes but different dynamical cores, the interannual variability simulation performances of the two models are compared. The ensemble approach is used to reduce model internal variability. In general, the simulation performances of the two models are similar. Both models have good per- formance in simulating total space-time variability and the Southern Oscillation Index. GAMIL performs better in the Eastern Asian winter circulation simulation than CAM2, and the model internal variability of GAMIL has a better response to external forcing than that of CAM2. These indicate that the improvement of the dynamic core is very important. It is also verified that there is less predictability in the middle and high latitudes than in the low latitudes.     

Observations of NO_2 and O_3 during Thunderstorm Activity Using Visible Spectroscopy

Simultaneous observations for the total column densities of NO2,O3 and H2O were carried on using the portable Spectrometer (438-450 nm and 400-450 nm) and the visible Spectrometer (544.4-628 nm) during premonsoon thunderstorms and embedded hail storm activity at Pune (18°32'N & 73°51'E),India.These observations confirm the fact that there is an increase in O3 and NO2 column densities during thunderstorms.The increase in O3 was observed following onset of thunderstorm,while the increase in NO2 was observed only after the thunder flashes occur.This implies that the production mechanisms for O3 and NO2 in thunderstorm are different.The observed column density of NO,value (1 to 3×1017molecules cm-2) during thunderstorm activity is 10 to 30 times higher than the value (1×10th molecules cm-2) of a normal day total column density.The spectrometric observations and observations of thunder flashes by electric field meter showed that 6.4×1025molecules/flash of NO2 are produced.The increased to-oil column den     

National GHG emissions reduction pledges and 2°C: comparison of studies

This article provides further detail on expected global GHG emission levels in 2020, based on the Emissions Gap Report (United Nations Environment Programme, December 2010), assuming the emission reduction proposals in the Copenhagen Accord and Cancun Agreements are met. Large differences are found in the results of individual groups owing to uncertainties in current and projected emission estimates and in the interpretation of the reduction proposals. Regardless of these uncertainties, the pledges for 2020 are expected to deliver emission levels above those that are consistent with a 2°C limit. This emissions gap could be narrowed through implementing the more stringent conditional pledges, minimizing the use of ‘lenient’ credits from forests and surplus emission units, avoiding double-counting of offsets and implementing measures beyond current pledges. Conversely, emission reduction gains from countries moving from their low to high ambition pledges could be more than offset by the use of ‘lenient’ land use, land-use change and forestry (LULUCF) credits and surplus emissions units, if these were used to the maximum. Laying the groundwork for faster emission reduction rates after 2020 appears to be crucial in any case.     

Radiative Forcing of SO2 and NOx： A Case Study in Beijing

A case study was performed in Beijing in 2000 to observe concentrations of SO2 and NOx in the atmosphere and to evaluate their radiative impact. It was found that the concentrations of these gases are usually high in the morning due to a temperature inversion in the boundary layer. The average concentrations obtained from the observations are much higher than those used in the McClatchey reference atmosphere. The radiative impacts of these gases are calculated using a line-by-line radiative transfer model. The results show that the radiative forcing at the surface due to SO2 is 0.0576 W m-2 and that due to NOx is 0.0032 W m-2. These figures are almost compatible with that due to CFC11.     

How is CO2 affecting yields and technological progress? A statistical analysis

This paper analyzes the impact of climate, crop production technology, and atmospheric carbon dioxide (CO₂) on current and future crop yields. The analysis of crop yields endeavors to advance the literature by estimating the effect of atmospheric CO₂ on observed crop yields. This is done using an econometric model estimated over pooled historical data for 1950–2009 and data from the free air CO₂ enrichment experiments. The main econometric findings are: 1) Yields of C3 crops (soybeans, cotton, and wheat) directly respond to the elevated CO₂, while yields of C4 crops (corn and sorghum) do not, but they are found to indirectly benefit from elevated CO₂ in times and places of drought stress; 2) The effect of technological progress on mean yields is non-linear; 3) Ignoring atmospheric CO₂ in an econometric model of crop yield likely leads to overestimates of the pure effects of technological progress on crop yields of about 51, 15, 17, 9, and 1 % of observed yield gain for cotton, soybeans, wheat, corn and sorghum, respectively; 4) Average climate conditions and climate variability contribute in a statistically significant way to average crop yields and their variability; and 5) The effect of CO₂ fertilization generally outweighs the effect of climate change on mean crop yields in many regions resulting in an increase of 7–22, 4–47, 5–26, 65–96, and 3–35 % for yields of corn, sorghum, soybeans, cotton, and wheat, respectively.     

Characteristics of the water cycle and land–atmosphere interactions from a comprehensive reforecast and reanalysis data set: CFSv2

The behavior of the water cycle in the Coupled Forecast System version 2 reforecasts and reanalysis is examined. Attention is focused on the evolution of forecast biases as the lead-time changes, and how the lead-time dependent model climatology differs from the reanalysis. Precipitation biases are evident in both reanalysis and reforecasts, while biases in soil moisture grow throughout the duration of the forecasts. Locally, the soil moisture biases may shrink or reverse sign. These biases are reflected in evaporation and runoff. The Noah land surface scheme shows the necessary relationships between evaporation and soil moisture for land-driven climate predictability. There is evidence that the atmospheric model cannot maintain the link between precipitation and antecedent soil moisture as strongly as in the real atmosphere, potentially hampering prediction skill, although there is better precipitation forecast skill over most locations when initial soil moisture anomalies are large. Bias change with lead-time, measured as the variance across ten monthly forecast leads, is often comparable to or larger than the interannual variance. Skill scores when forecast anomalies are calculated relative to reanalysis are seriously reduced over most locations when compared to validation against anomalies based on the forecast model climate at the corresponding lead-time. When all anomalies are calculated relative to the 0-month forecast, some skill is recovered over some regions, but the complex manner in which biases evolve indicates that a complete suite of reforecasts would be necessary whenever a new version of a climate model is implemented. The utility of reforecast programs is evident for operational forecast systems.     

Chemistry–Climate Interactions of Stratospheric and Mesospheric Ozone in EMAC Long-Term Simulations with Different Boundary Conditions for CO2, CH4, N2O,and ODS

Abstract

To evaluate future climate change in the middle atmosphere and the chemistry–climate interaction of stratospheric ozone, we performed a long-term simulation from 1960 to 2050 with boundary conditions from the Intergovernmental Panel on Climate Change A1B greenhouse gas scenario and the World Meteorological Organization Ab halogen scenario using the chemistry–climate model ECHAM5/MESSy Atmospheric Chemistry (EMAC). In addition to this standard simulation we performed five sensitivity simulations from 2000 to 2050 using the rerun files of the simulation mentioned above. For these sensitivity simulations we used the same model setup as in the standard simulation but changed the boundary conditions for carbon dioxide (CO₂), methane (CH₄), nitrous oxide (N₂O), and ozone-depleting substances (ODS). In the first sensitivity simulation we fixed the mixing ratios of CO₂, CH₄, and N₂O in the boundary conditions to the amounts for 2000. In each of the four other sensitivity simulations we fixed the boundary conditions of only one of CO₂, CH₄, N₂O, or ODS to the year 2000.

In our model simulations the future evolution of greenhouse gases leads to significant cooling in the stratosphere and mesosphere. Increasing CO₂ mixing ratios make the largest contributions to this radiative cooling, followed by increasing stratospheric CH₄, which also forms additional H₂O in the upper stratosphere and mesosphere. Increasing N₂O mixing ratios makes the smallest contributions to the cooling. The simulated ozone recovery leads to warming of the middle atmosphere.

In the EMAC model the future development of ozone is influenced by several factors. 1) Cooler temperatures lead to an increase in ozone in the upper stratosphere. The strongest contribution to this ozone production is cooling due to increasing CO₂ mixing ratios, followed by increasing CH₄. 2) Decreasing ODS mixing ratios lead to ozone recovery, but the contribution to the total ozone increase in the upper stratosphere is only slightly higher than the contribution of the cooling by greenhouse gases. In the polar lower stratosphere a decrease in ODS is mainly responsible for ozone recovery. 3) Higher NO_x and HO_x mixing ratios due to increased N₂O and CH₄ lead to intensified ozone destruction, primarily in the middle and upper stratosphere, from additional NO_x; in the mesosphere the intensified ozone destruction is caused by additional HO_x. In comparison to the increase in ozone due to decreasing ODS, ozone destruction caused by increased NO_x is of similar importance in some regions, especially in the middle stratosphere. 4) In the stratosphere the enhancement of the Brewer-Dobson circulation leads to a change in ozone transport. In the polar stratosphere increased downwelling leads to additional ozone in the future, especially at high northern latitudes. The dynamical impact on ozone development is higher at some altitudes in the polar stratosphere than the ozone increase due to cooler temperatures. In the tropical lower stratosphere increased residual vertical upward transport leads to a decrease in ozone.     

Meeting the EU 2°C climate target: global and regional emission implications

Abstract

This article presents a set of multi-gas emission pathways for different CO₂-equivalent concentration stabilization levels, i.e. 400, 450, 500 and 550 ppm CO₂-equivalent, along with an analysis of their global and regional reduction implications and implied probability of achieving the EU climate target of 2°C. For achieving the 2°C target with a probability of more than 60%, greenhouse gas concentrations need to be stabilized at 450 ppm CO₂-equivalent or below, if the 90% uncertainty range for climate sensitivity is believed to be 1.5–4.5°C. A stabilization at 450 ppm CO₂-equivalent or below (400 ppm) requires global emissions to peak around 2015, followed by substantial overall reductions of as much as 25% (45% for 400 ppm) compared to 1990 levels in 2050. In 2020, Annex I emissions need to be approximately 15% (30%) below 1990 levels, and non-Annex I emissions also need to be reduced by 15–20% compared to their baseline emissions. A further delay in peaking of global emissions by 10 years doubles maximum reduction rates to about 5% per year, and very probably leads to high costs. In order to keep the option open of stabilizing at 400 and 450 ppm CO₂-equivalent, the USA and major advanced non-Annex I countries will have to participate in the reductions within the next 10–15 years.     

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.

     

Effect of Increasing CO_2 on the Stratospheric Level of CO and O_3

Production and destruction processes of carbon monoxide (CO) and ozone (O3) are examined in the light of increasing amount of atmospheric carbon dioxide (CO2). It is found that doubling of CO2 will increase the stratospheric concentration of CO and will have positive effect on O3 concentration.     

Research on Early-Warning and Control Techniques for Agricultural and Forest Pests and Diseases and Agrometeorological Disasters

After the open bidding and specialist appraisal, the project is undertaken jointly by the Institute of Plant Protection of the Chinese Academy of Agricultural Science, the Institute of Plant Protection of the Sichuan Province Academy of Agricultural Science, the Chinese Agricultural University, the Research Institute of Forest Protection of the Forestry Academy of China, the Chinese Forest University, the Chinese Academy of Meteorological Sciences, and the Research Institute of Agrometeorology of the Chinese Academy of Agricultural Science. The project (Project No: 2001BA509B, including 15 subprojects), is organized     

Parameterization of the Absorption of the H2O Continuum, CO2, O2, and Other Trace Gases in the Fu-Liou Solar Radiation Program

The absorption properties of the water vapor continuum and a number of weak bands for H2O, O2, CO2, CO, N2O, CH4, and O3 in the solar spectrum are incorporated into the Fu-Liou radiation parameterization program by using the correlated k-distribution method (CKD) for the sorting of absorption lines. The overlap absorption of the H2O lines and the H2O continuum (2500-14500 cm^-1) are treated by taking the two gases as a single-mixture gas in transmittance calculations. Furthermore, in order to optimize the computation efforts, CO2 and CH4 in the spectral region 2850-5250 cm^-1 are taken as a new singlemixture gas as well. For overlap involving other absorption lines in the Fu-Liou spectral bands, the authors adopt the multiplication rule for transmittance computations under which the absorption spectra for two gases are assumed to be uncorrelated. Compared to the line-by-line (LBL) computation, it is shown that the errors in fluxes introduced by these two approaches within the context of the CKD method are small and less than 0.48% for the H20 line and continuum in the 2500-14500 cm^-1 solar spectral region, -1% for H2O (line) H2O (continuum) CO2 CH4 in the spectral region 2850-5250 cm^-1, and -1.5% for H2O (line) H2O (continuum) O2 in the 7700-14500 cm^-1 spectral region. Analysis also demonstrates that the multiplication rule over a spectral interval as wide as 6800 cm^-1 can produce acceptable errors with a maximum percentage value of about 2% in reference to the LBL calculation. Addition of the preceding gases increases the absorption of solar radiation under all sky conditions. For clear sky, the increase in instantaneous solar absorption is about 9%-13% (-12 W m^-2) among which the H20 continuum produces the largest increase, while the contributions from O2 and CO2 rank second and third, respectively. In cloudy sky, the addition of absorption amounts to about 6-9 W m^-2. The new, improved program with the incorporation of the preceding gases produces a smaller solar absorption in clouds due to the reduced solar flux reaching the cloud top.     

The Influence of O3, Relative Humidity, NO and NO2 on the Oxidation of α-Pinene and Δ3-Carene

Upto 13% of -pinene and ³-carene had reacted after 213 s in this dark experimental set-up, where O₃, NO and NO₂ were mixed with terpenes at different relative humidities (RHs). The different experiments were planned according to an experimental design, where O₃, NO₂, NO, RH and reaction time were varied between high and low settings (25 and 75 ppb, 15 and 42%, 44 and 213 s). An increased amount of -pinene and ³-carene reacted in the chamber was observed, when the level of O₃, NO and reaction time was increased and RH was decreased. In the study, it was found that different interactions affected the amount of terpene reacted as well. These interactions were between O₃ and NO, O₃ and reaction time, NO and RH, and between NO and reaction time.     

Parameterization of the Absorption of the H_2O Continuum,CO_2,O_2,and Other Trace Gases in the Fu-Liou Solar Radiation Program

The absorption properties of the water vapor continuum and a number of weak bands for H2O, O2, CO2, CO, N2O, CH4, and O3 in the solar spectrum are incorporated into the Fu-Liou radiation parameterization program by using the correlated k-distribution method (CKD) for the sorting of absorption lines. The overlap absorption of the H2O lines and the H2O continuum (2500-14500 cm-1) are treated by taking the two gases as a single-mixture gas in transmittance calculations. Furthermore, in order to optimize the computation efforts, CO2 and CH4 in the spectral region 2850-5250 cm-1 are taken as a new single-mixture gas as well. For overlap involving other absorption lines in the Fu-Liou spectral bands, the authors adopt the multiplication rule for transmittance computations under which the absorption spectra for two gases are assumed to be uncorrelated. Compared to the line-by-line (LBL) computation, it is shown that the errors in fluxes introduced by these two approaches within the context of the CKD method are small and less than 0.48% for the H2O line and continuum in the 2500-14500 cm-1 solar spectral region, -1% for H2O (line) H2O (continuum) CO2 CH4 in the spectral region 2850-5250 cm-1, and -1.5% for H2O (line) H2O (continuum) O2 in the 7700-14500 cm-1 spectral region. Analysis also demonstrates that the multiplication rule over a spectral interval as wide as 6800 cm-1 can produce acceptable errors with a maximum percentage value of about 2% in reference to the LBL calculation. Addition of the preceding gases increases the absorption of solar radiation under all sky conditions. For clear sky, the increase in instantaneous solar absorption is about 9%-13% (~12 W m~2) among which the H2O continuum produces the largest increase, while the contributions from O2 and CO2 rank second and third, respectively. In cloudy sky, the addition of absorption amounts to about 6-9 W m-2. The new, improved program with the incorporation of the preceding gases produces a smaller solar absorption in clouds due to the reduced solar flux reaching the cloud top.     

Uncertainty in the 2°C warming threshold related to climate sensitivity and climate feedback

Climate sensitivity is an important index that measures the relationship between the increase in greenhouse gases and the magnitude of global warming. Uncertainties in climate change projection and climate modeling are mostly related to the climate sensitivity. The climate sensitivities of coupled climate models determine the magnitudes of the projected global warming. In this paper, the authors thoroughly review the literature on climate sensitivity, and discuss issues related to climate feedback processes and the methods used in estimating the equilibrium climate sensitivity and transient climate response (TCR), including the TCR to cumulative CO₂ emissions. After presenting a summary of the sources that affect the uncertainty of climate sensitivity, the impact of climate sensitivity on climate change projection is discussed by addressing the uncertainties in 2°C warming. Challenges that call for further investigation in the research community, in particular the Chinese community, are discussed.     

Spatial gridding of daily maximum and minimum 2 m temperatures supported by satellite observations

The usefulness of two remotely sensed variables, land surface temperature (LST) and cloud cover (CC), as predictors for the gridding of daily maximum and minimum 2 m temperature (T _min/T _max) was assessed. Four similar gridding methods were compared, each of which applied regression kriging to capture the spatial variation explained by the predictors used; however, both methods differed in the interpolation steps performed and predictor combinations used. The robustness of the gridding methods was tested for daily observations in January and July in the period 2009–2011 and in two different regions: the Central European region (CER) and the Iberian Peninsula (IP). Moreover, the uncertainty estimate provided by each method was evaluated using cross-validation. The regression analyses for both regions demonstrated the high predictive skills of LST for T _min and T _max on daily and monthly timescales (and lower predictive skills of CC). The application of LST as a predictor considerably improved the gridding performance over the IP region in July; however, there was only a slight improvement over the CER region. CC reduced the loss of spatial variability in the interpolated daily T _min/T _max values over the IP region. The interpolation skill was mainly controlled by the station density, but also depended on the complexity of the terrain. LST was shown to be of particular value for very low station densities (1 station per 50,000 km²). Analyses with artificially decreasing station densities showed that even in the case of very low station densities, LST allows the determination of useful regression functions.     

Subseasonal Prediction of Early-summer Northeast Asian Cut-off Lows by BCC-CSM2-HR and GloSea5

Northeast Asian cut-off lows are crucial cyclonic systems that can bring temperature and precipitation extremes over large areas. Skillful subseasonal forecasting of Northeast Asian cut-off lows is of great importance. Using two dynamical forecasting systems, one from the Beijing Climate Center(BCC-CSM2-HR) and the other from the Met Office(GloSea5),this study assesses simulation ability and subseasonal prediction skill for early-summer Northeast Asian cut-off lows. Both models are shown to have...