Impact of land surface initialization approach on subseasonal forecast skill: A regional analysis in the southern hemisphere

The authors use a sophisticated coupled land–atmosphere modeling system for a Southern Hemisphere subdomain centered over southeastern Australia to evaluate differences in simulation skill from two different land surface initialization approaches. The first approach uses equilibrated land surface states obtained from offline simulations of the land surface model, and the second uses land surface states obtained from reanalyses. The authors find that land surface initialization using prior offline simulations contribute to relative gains in subseasonal forecast skill. In particular, relative gains in forecast skill for temperature of 10%–20% within the first 30 days of the forecast can be attributed to the land surface initialization method using offline states. For precipitation there is no distinct preference for the land surface initialization method, with limited gains in forecast skill irrespective of the lead time. The authors evaluated the asymmetry between maximum and minimum temperatures and found that maximum temperatures had the largest gains in relative forecast skill, exceeding 20% in some regions. These results were statistically significant at the 98% confidence level at up to 60 days into the forecast period. For minimum temperature, using reanalyses to initialize the land surface contributed to relative gains in forecast skill, reaching 40% in parts of the domain that were statistically significant at the 98% confidence level. The contrasting impact of the land surface initialization method between maximum and minimum temperature was associated with different soil moisture coupling mechanisms. Therefore, land surface initialization from prior offline simulations does improve predictability for temperature, particularly maximum temperature, but with less obvious improvements for precipitation and minimum temperature over southeastern Australia

Analysis of CO in the tropical troposphere using Aura satellite data and the GEOS-Chem model: insights into transport characteristics of the GEOS meteorological products

We use the GEOS-Chem chemistry-transport model (CTM) to interpret the spatial and temporal variations of tropical tropospheric CO observed by the Microwave Limb Sounder (MLS) and the Tropospheric Emission Spectrometer (TES). In so doing, we diagnose and evaluate transport in the GEOS-4 and GEOS-5 assimilated meteorological fields that drive the model, with a particular focus on vertical mixing at the end of the dry season when convection moves over the source regions. The results indicate that over South America, deep convection in both GEOS-4 and GEOS-5 decays at too low an altitude early in the wet season, and the source of CO from isoprene in the model (MEGAN v2.1) is too large, causing a lag in the model's seasonal maximum of CO compared to MLS CO in the upper troposphere (UT). TES and MLS data reveal problems with excessive transport of CO to the eastern equatorial Pacific and lofting in the ITCZ in August and September, particularly in GEOS-4. Over southern Africa, GEOS-4 and GEOS-5 simulations match the phase of the observed CO variation from the lower troposphere (LT) to the UT fairly well, although the magnitude of the seasonal maximum is underestimated considerably due to low emissions in the model. A sensitivity run with increased emissions leads to improved agreement with observed CO in the LT and middle troposphere (MT), but the amplitude of the seasonal variation is too high in the UT in GEOS-4. Difficulty in matching CO in the LT and UT implies there may be overly vigorous vertical mixing in GEOS-4 early in the wet season. Both simulations and observations show a time lag between the peak in fire emissions (July and August) and in CO (September and October). We argue that it is caused by the prevailing subsidence in the LT until convection moves south in September, as well as the low sensitivity of TES data in the LT over the African Plateau. The MLS data suggest that too much CO has been transported from fires in northern Africa to the UT in the model during the burning season, as does MOZAIC aircraft data, perhaps as a result of the combined influence of too strong Harmattan winds in the LT and too strong vertical mixing over the Gulf of Guinea in the model

Implementation of a soil albedo scheme in the CABLEv1.4b land surface model and evaluation against MODIS estimates over Australia

Land surface albedo, the fraction of incoming solar radiation reflected by the land surface, is a key component of the Earth system. This study evaluates snow-free surface albedo simulations by the Community Atmosphere Biosphere Land Exchange (CABLEv1.4b) model with the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Satellite Pour L'Observation de la Terre (SPOT) albedo. We compare results from offline simulations over the Australian continent. The control simulation has prescribed background snow-free and vegetation-free soil albedo derived from MODIS whilst the experiments use a simple parameterisation based on soil moisture and colour, originally from the Biosphere Atmosphere Transfer Scheme (BATS), and adopted in the Common Land Model (CLM). The control simulation, with prescribed soil albedo, shows that CABLE simulates overall albedo over Australia reasonably well, with differences compared to MODIS and SPOT albedos within ±0.1. Application of the original BATS scheme, which uses an eight-class soil classification, resulted in large differences of up to −0.25 for the near-infrared (NIR) albedo over large parts of the desert regions of central Australia. The use of a recalibrated 20-class soil colour classification from the CLM, which includes a higher range for saturated and VIS (visible) and NIR soil albedos, reduced the underestimation of the NIR albedo. However, this soil colour mapping is tuned to CLM soil moisture, a quantity which is not necessarily transferrable between land surface models. We therefore recalibrated the soil color map using CABLE's climatological soil moisture, which further reduced the underestimation of the NIR albedo to within ±0.15 over most of the continent as compared to MODIS and SPOT albedos. Small areas of larger differences of up to −0.25 remained within the central arid parts of the continent during summer; however, the spatial extent of these large differences is substantially reduced as compared to the simulation using the default eight-class uncalibrated soil colour map. It is now possible to use CABLE coupled to atmospheric models to investigate soil-moisture–albedo feedbacks, an important enhancement of the model

Influence of leaf area index prescriptions on simulations of heat, moisture, and carbon fluxes

Leaf area index (LAI), the total one-sided surface area of leaf per ground surface area, is a key component of land surface models. The authors investigate the influence of differing, plausible LAI prescriptions on heat, moisture, and carbon fluxes simulated by the Community Atmosphere Biosphere Land Exchange version 1.4b (CABLEv1.4b) model over the Australian continent. A 15-member ensemble monthly LAI dataset is generated using the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product and gridded observations of temperature and precipitation. Offline simulations lasting 29 years (1980–2008) are carried out at 25-km resolution with the composite monthly means from the MODIS LAI product (control simulation) and compared with simulations using each of the 15-member ensemble monthly varying LAI datasets generated. The imposed changes in LAI did not strongly influence the sensible and latent fluxes, but the carbon fluxes were more strongly affected. Croplands showed the largest sensitivity in gross primary production with differences ranging from −90% to 60%. Plant function types (PFTs) with high absolute LAI and low interannual variability, such as evergreen broadleaf trees, showed the least response to the different LAI prescriptions, while those with lower absolute LAI and higher interannual variability, such as croplands, were more sensitive. The authors show that reliance on a single LAI prescription may not accurately reflect the uncertainty in the simulation of terrestrial carbon fluxes, especially for PFTs with high interannual variability. The study highlights that accurate representation of LAI in land surface models is key to the simulation of the terrestrial carbon cycle. Hence, this will become critical in quantifying the uncertainty in future changes in primary production

Nitrogen deposition to the United States: distribution, sources, and processes

We simulate nitrogen deposition over the US in 2006–2008 by using the GEOS-Chem global chemical transport model at 1/2°×2/3° horizontal resolution over North America and adjacent oceans. US emissions of NO<sub>x</sub> and NH<sub>3</sub> in the model are 6.7 and 2.9 Tg N a<sup>−1</sup> respectively, including a 20% natural contribution for each. Ammonia emissions are a factor of 3 lower in winter than summer, providing a good match to US network observations of NH<sub>x</sub> (≡NH<sub>3</sub> gas + ammonium aerosol) and ammonium wet deposition fluxes. Model comparisons to observed deposition fluxes and surface air concentrations of oxidized nitrogen species (NO<sub>y</sub>) show overall good agreement but excessive wintertime HNO<sub>3</sub> production over the US Midwest and Northeast. This suggests a model overestimate N<sub>2</sub>O<sub>5</sub> hydrolysis in aerosols, and a possible factor is inhibition by aerosol nitrate. Model results indicate a total nitrogen deposition flux of 6.5 Tg N a<sup>−1</sup> over the contiguous US, including 4.2 as NO<sub>y</sub> and 2.3 as NH<sub>x</sub>. Domestic anthropogenic, foreign anthropogenic, and natural sources contribute respectively 78%, 6%, and 16% of total nitrogen deposition over the contiguous US in the model. The domestic anthropogenic contribution generally exceeds 70% in the east and in populated areas of the west, and is typically 50–70% in remote areas of the west. Total nitrogen deposition in the model exceeds 10 kg N ha<sup>−1</sup> a<sup>−1</sup> over 35% of the contiguous US

Chemistry of hydrogen oxide radicals (HO_x) in the Arctic troposphere in spring

We use observations from the April 2008 NASA ARCTAS aircraft campaign to the North American Arctic, interpreted with a global 3-D chemical transport model (GEOS-Chem), to better understand the sources and cycling of hydrogen oxide radicals (HO_x≡H+OH+peroxy radicals) and their reservoirs (HO_y≡HO_x+peroxides) in the springtime Arctic atmosphere. We find that a standard gas-phase chemical mechanism overestimates the observed HO_2 and H_2O_2 concentrations. Computation of HO_x and HO_y gas-phase chemical budgets on the basis of the aircraft observations also indicates a large missing sink for both. We hypothesize that this could reflect HO_2 uptake by aerosols, favored by low temperatures and relatively high aerosol loadings, through a mechanism that does not produce H_2O_2. We implemented such an uptake of HO_2 by aerosol in the model using a standard reactive uptake coefficient parameterization with γ(HO_2) values ranging from 0.02 at 275 K to 0.5 at 220 K. This successfully reproduces the concentrations and vertical distributions of the different HO_x species and HO_y reservoirs. HO_2 uptake by aerosol is then a major HO_x and HO_y sink, decreasing mean OH and HO_2 concentrations in the Arctic troposphere by 32% and 31% respectively. Better rate and product data for HO_2 uptake by aerosol are needed to understand this role of aerosols in limiting the oxidizing power of the Arctic atmosphere

Source Attribution and Interannual Variability of Arctic Pollution in Spring Constrained by Aircraft (ARCTAS, ARCPAC) and Satellite (AIRS) Observations of Carbon Monoxide

We use aircraft observations of carbon monoxide (CO) from the NASA ARCTAS and NOAA ARCPAC campaigns in April 2008 together with multiyear (2003-2008) CO satellite data from the AIRS instrument and a global chemical transport model (GEOS-Chem) to better understand the sources, transport, and interannual variability of pollution in the Arctic in spring. Model simulation of the aircraft data gives best estimates of CO emissions in April 2008 of 26 Tg month-1 for Asian anthropogenic, 9.1 for European anthropogenic, 4.2 for North American anthropogenic, 9.3 for Russian biomass burning (anomalously large that year), and 21 for Southeast Asian biomass burning. We find that Asian anthropogenic emissions are the dominant source of Arctic CO pollution everywhere except in surface air where European anthropogenic emissions are of similar importance. Synoptic pollution influences in the Arctic free troposphere include contributions of comparable magnitude from Russian biomass burning and from North American, European, and Asian anthropogenic sources. European pollution dominates synoptic variability near the surface. Analysis of two pollution events sampled by the aircraft demonstrates that AIRS is capable of observing pollution transport to the Arctic in the mid-troposphere. The 2003-2008 record of CO from AIRS shows that interannual variability averaged over the Arctic cap is very small. AIRS CO columns over Alaska are highly correlated with the Ocean Nino Index, suggesting a link between El Nino and northward pollution transport. AIRS shows lower-than-average CO columns over Alaska during April 2008, despite the Russian fires, due to a weakened Aleutian Low hindering transport from Asia and associated with the moderate 2007-2008 La Nina. This suggests that Asian pollution influence over the Arctic may be particularly large under strong El Nino conditions

Source Attribution and Interannual Variability of Arctic Pollution in Spring Constrained by Aircraft (ARCTAS, ARCPAC) and Satellite (AIRS) Observations of Carbon Monoxide

We use aircraft observations of carbon monoxide (CO) from the NASA ARCTAS and NOAA ARCPAC campaigns in April 2008 together with multiyear (2003–2008) CO satellite data from the AIRS instrument and a global chemical transport model (GEOS-Chem) to better understand the sources, transport, and interannual variability of pollution in the Arctic in spring. Model simulation of the aircraft data gives best estimates of CO emissions in April 2008 of $26 Tg month^{−1}$ for Asian anthropogenic, 9.4 for European anthropogenic, 4.1 for North American anthropogenic, 15 for Russian biomass burning (anomalously large that year), and 23 for Southeast Asian biomass burning. We find that Asian anthropogenic emissions are the dominant source of Arctic CO pollution everywhere except in surface air where European anthropogenic emissions are of similar importance. Russian biomass burning makes little contribution to mean CO (reflecting the long CO lifetime) but makes a large contribution to CO variability in the form of combustion plumes. Analysis of two pollution events sampled by the aircraft demonstrates that AIRS can successfully observe pollution transport to the Arctic in the mid-troposphere. The 2003–2008 record of CO from AIRS shows that interannual variability averaged over the Arctic cap is very small. AIRS CO columns over Alaska are highly correlated with the Ocean Niño Index, suggesting a link between El Niño and Asian pollution transport to the Arctic. AIRS shows lower-than-average CO columns over Alaska during April 2008, despite the Russian fires, due to a weakened Aleutian Low hindering transport from Asia and associated with the moderate 2007–2008 La Niña. This suggests that Asian pollution influence over the Arctic may be particularly large under strong El Niño conditions.Engineering and Applied Science

Corrigendum to "Source attribution of the changes in atmospheric methane for 2006–2008" published in Atmos. Chem. Phys., 11, 3689–3700, 2011

No abstract available