Jan Fokke Meirink

Inverse modeling of global and regional CH4 emissions using SCIAMACHY satellite retrievals

Methane retrievals from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) instrument onboard ENVISAT provide important information on atmospheric CH_4 sources, particularly in tropical regions which are poorly monitored by in situ surface observations. Recently, Frankenberg et al. (2008a, 2008b) reported a major revision of SCIAMACHY retrievals due to an update of spectroscopic parameters of water vapor and CH_4. Here, we analyze the impact of this revision on global and regional CH_4 emissions estimates in 2004, using the TM5-4DVAR inverse modeling system. Inversions based on the revised SCIAMACHY retrievals yield ∼20% lower tropical emissions compared to the previous retrievals. The new retrievals improve significantly the consistency between observed and assimilated column average mixing ratios and the agreement with independent validation data. Furthermore, the considerable latitudinal and seasonal bias correction of the previous SCIAMACHY retrievals, derived in the TM5-4DVAR system by simultaneously assimilating high-accuracy surface measurements, is reduced by a factor of ∼3. The inversions result in significant changes in the spatial patterns of emissions and their seasonality compared to the bottom-up inventories. Sensitivity tests were done to analyze the robustness of retrieved emissions, revealing some dependence on the applied a priori emission inventories and OH fields. Furthermore, we performed a detailed validation of simulated CH_4 mixing ratios using NOAA ship and aircraft profile samples, as well as stratospheric balloon samples, showing overall good agreement. We use the new SCIAMACHY retrievals for a regional analysis of CH_4 emissions from South America, Africa, and Asia, exploiting the zooming capability of the TM5 model. This allows a more detailed analysis of spatial emission patterns and better comparison with aircraft profiles and independent regional emission estimates available for South America. Large CH_4 emissions are attributed to various wetland regions in tropical South America and Africa, seasonally varying and opposite in phase with CH_4 emissions from biomass burning. India, China and South East Asia are characterized by pronounced emissions from rice paddies peaking in the third quarter of the year, in addition to further anthropogenic emissions throughout the year.

Satellite chartography of atmospheric methane from SCIAMACHY on board ENVISAT: 2. Evaluation based on inverse model simulations

We extend the analysis of a global CH_4 data set retrieved from SCIAMACHY (Frankenberg et al., 2006) by making a detailed comparison with inverse TM5 model simulations for 2003 that are optimized versus high accuracy CH_4 surface measurements from the NOAA ESRL network. The comparison of column averaged mixing ratios over remote continental and oceanic regions shows that major features of the atmospheric CH_4 distribution are consistent between SCIAMACHY observations and model simulations. However, the analysis suggests that SCIAMACHY CH_4 retrievals may have some bias that depends on latitude and season (up to ∼30 ppb). Large enhancements of column averaged CH_4 mixing ratios (∼50–100 ppb) are observed and modeled over India, Southeast Asia, and the tropical regions of South America, and Africa. We present a detailed comparison of observed spatial patterns and their seasonal evolution with TM5 1° × 1° zoom simulations over these regions. Application of a new wetland inventory leads to a significant improvement in the agreement between SCIAMACHY retrievals and model simulations over the Amazon basin during the first half of the year. Furthermore, we present an initial coupled inversion that simultaneously uses the surface and satellite observations and that allows the inverse system to compensate for the potential systematic bias. The results suggest significantly greater tropical emissions compared to either the a priori estimates or the inversion based on the surface measurements only. Emissions from rice paddies in India and Southeast Asia are relatively well constrained by the SCIAMACHY data and are slightly reduced by the inversion.

Tropical methane emissions: A revised view from SCIAMACHY onboard ENVISAT

Methane retrievals from near-infrared spectra recorded by the SCIAMACHY instrument onboard ENVISAT hitherto suggested unexpectedly large tropical emissions. Even though recent studies confirm substantial tropical emissions, there were indications for an unresolved error in the satellite retrievals. Here we identify a retrieval error related to inaccuracies in water vapor spectroscopic parameters, causing a substantial overestimation of methane correlated with high water vapor abundances. We report on the overall implications of an update in water spectroscopy on methane retrievals with special focus on the tropics where the impact is largest. The new retrievals are applied in a four-dimensional variational (4D-VAR) data assimilation system to derive a first estimate of the impact on tropical CH_4 sources. Compared to inversions based on previous SCIAMACHY retrievals, annual tropical emission estimates are reduced from 260 to about 201 Tg CH_4 but still remain higher than previously anticipated.

Inverse modeling of European CH4 emissions 2001-2006

European CH4 emissions are estimated for the period 2001-2006 using a four-dimensional variational (4DVAR) inverse modeling system, based on the atmospheric zoom model TM5. Continuous observations are used from various European monitoring stations, complemented by European and global flask samples from the NOAA/ESRL network. The available observations mainly provide information on the emissions from northwest Europe (NWE), including the UK, Ireland, the BENELUX countries, France and Germany. The inverse modeling estimates for the total anthropogenic emissions from NWE are 21% higher compared to the EDGARv4.0 emission inventory and 40% higher than values reported to U.N. Framework Convention on Climate Change. Assuming overall uncertainties on the order of 30% for both bottom-up and top-down estimates, all three estimates can be still considered to be consistent with each other. However, the uncertainties in the uncertainty estimates prevent us from verifying (or falsifying) the bottom-up inventories in a strict sense. Sensitivity studies show some dependence of the derived spatial emission patterns on the set of atmospheric monitoring stations used, but the total emissions for the NWE countries appear to be relatively robust. While the standard inversions include a priori information on the spatial and temporal emission patterns from bottom-up inventories, a further sensitivity inversion without this a priori information results in very similar NWE country totals, demonstrating that the available observations provide significant constraints on the emissions from the NWE countries independent from bottom-up inventories.

Four-dimensional variational data assimilation for inverse modeling of atmospheric methane emissions: Analysis of SCIAMACHY observations

Recent observations from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) instrument aboard ENVISAT have brought new insights in the global distribution of atmospheric methane. In particular, the observations showed higher methane concentrations in the tropics than previously assumed. Here, we analyze the SCIAMACHY observations and their implications for emission estimates in detail using a four-dimensional variational (4D-Var) data assimilation system. We focus on the period September to November 2003 and on the South American continent, for which the satellite observations showed the largest deviations from model simulations. In this set-up the advantages of the 4D-Var approach and the zooming capability of the underlying TM5 atmospheric transport model are fully exploited. After application of a latitude-dependent bias correction to the SCIAMACHY observations, the assimilation system is able to accurately fit those observations, while retaining consistency with a network of surface methane measurements. The main emission increments resulting from the inversion are an increase in the tropics, a decrease in South Asia, and a decrease at northern hemispheric high latitudes. The SCIAMACHY observations yield considerable additional emission uncertainty reduction, particularly in the (sub-)tropical regions, which are poorly constrained by the surface network. For tropical South America, the inversion suggests more than a doubling of emissions compared to the a priori during the 3 months considered. Extensive sensitivity experiments, in which key assumptions of the inversion set-up are varied, show that this finding is robust. Independent airborne observations in the Amazon basin support the presence of considerable local methane sources. However, these observations also indicate that emissions from eastern South America may be smaller than estimated from SCIAMACHY observations. In this respect it must be realized that the bias correction applied to the satellite observations does not take into account potential regional systematic errors, which - if identified in the future - will lead to shifts in the overall distribution of emission estimates.

Evidence for long-range transport of Carbon Monoxide in the Southern Hemisphere from SCIAMACHY observations

This paper gives an overview of the results published by [1],[2], and [3]. The precision of the SCIAMACHY carbon monoxide (CO) total columns depends on the random instrument-noise error and is generally within 10% for monthly means. SCIAMACHY CO total columns agree well with chemistry-transport model simulations using the GFEDv2 biomass-burning emission data base. Enhanced CO columns are seen with SCIAMACHY over Australia during its biomass-burning season in local Spring. It is shown that the enhancements over Australian biomass-burning areas contain a large contribution of CO from South American biomass-burning regions. The results indicate that SCIAMACHY can be used to study both longe-range transport and emission sources of CO.

Quantitative analysis of SCIAMACHY carbon monoxide total column measurements

Global tropospheric ozone distributions, budgets, and radiative forcings from an ensemble of 26 state-of-the-art atmospheric chemistry models have been intercompared and synthesized as part of a wider study into both the air quality and climate roles of ozone. Results from three 2030 emissions scenarios, broadly representing “optimistic,” “likely,” and “pessimistic” options, are compared to a base year 2000 simulation. This base case realistically represents the current global distribution of tropospheric ozone. A further set of simulations considers the influence of climate change over the same time period by forcing the central emissions scenario with a surface warming of around 0.7K. The use of a large multimodel ensemble allows us to identify key areas of uncertainty and improves the robustness of the results. Ensemble mean changes in tropospheric ozone burden between 2000 and 2030 for the 3 scenarios range from a 5% decrease, through a 6% increase, to a 15% increase. The intermodel uncertainty (±1 standard deviation) associated with these values is about ±25%. Model outliers have no significant influence on the ensemble mean results. Combining ozone and methane changes, the three scenarios produce radiative forcings of -50, 180, and 300 mW m-2, compared to a CO2 forcing over the same time period of 800–1100 mW m-2. These values indicate the importance of air pollution emissions in short- to medium-term climate forcing and the potential for stringent/lax control measures to improve/worsen future climate forcing. The model sensitivity of ozone to imposed climate change varies between models but modulates zonal mean mixing ratios by ±5 ppbv via a variety of feedback mechanisms, in particular those involving water vapor and stratosphere-troposphere exchange. This level of climate change also reduces the methane lifetime by around 4%. The ensemble mean year 2000 tropospheric ozone budget indicates chemical production, chemical destruction, dry deposition and stratospheric input fluxes of 5100, 4650, 1000, and 550 Tg(O3) yr-1, respectively. These values are significantly different to the mean budget documented by the Intergovernmental Panel on Climate Change (IPCC) Third Assessment Report (TAR). The mean ozone burden (340 Tg(O3)) is 10% larger than the IPCC TAR estimate, while the mean ozone lifetime (22 days) is 10% shorter. Results from individual models show a correlation between ozone burden and lifetime, and each models ozone burden and lifetime respond in similar ways across the emissions scenarios. The response to climate change is much less consistent. Models show more variability in the tropics compared to midlatitudes. Some of the most uncertain areas of the models include treatments of deep tropical convection, including lightning NO x production; isoprene emissions from vegetation and isoprenes degradation chemistry; stratosphere-troposphere exchange; biomass burning; and water vapor concentrations.

Scanning imaging absorption spectrometer for atmospheric chartography carbon monoxide total columns: statistical evaluation and comparison with chemistry transport model results

This paper presents a detailed statistical analysis of one year (September 2003 to August 2004) of global Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) carbon monoxide (CO) total column retrievals from the Iterative Maximum Likelihood Method (IMLM) algorithm, version 6.3. SCIAMACHY provides the first solar reflectance measurements of CO and is uniquely sensitive down to the boundary layer. SCIAMACHY measurements and chemistry transport model (CTM) results are compared and jointly evaluated. Significant improvements in agreement occur, especially close to biomass burning emission regions, when the new Global Fire Emissions Database version 2 (GFEDv2) is used with the CTM. Globally, the seasonal variation of the model is very similar to that of the SCIAMACHY measurements. For certain locations, significant differences were found, which are likely related to modeling errors due to CO emission uncertainties. Statistical analysis shows that differences between single SCIAMACHY CO total column measurements and corresponding model results are primarily explained by random instrument noise errors. This strongly suggests that the random instrument noise errors are a good diagnostic for the precision of the measurements. The analysis also indicates that noise in single SCIAMACHY CO measurements is generally greater than actual variations in total columns. It is thus required to average SCIAMACHY data over larger temporal and spatial scales to obtain valuable information. Analyses of monthly averaged SCIAMACHY measurements over 3° × 2° geographical regions indicates that they are of sufficient accuracy to reveal valuable information about spatial and temporal variations in CO columns and provide an important tool for model validation. A large spatial and temporal variation in instrument noise errors exists which shows a close correspondence with the spatial distribution of surface albedo and cloud cover. This large spatial variability is important for the use of monthly and annual mean SCIAMACHY CO total column measurements. The smallest instrument noise errors of monthly mean 3° × 2° SCIAMACHY CO total columns measurements are 0.01 × 1018 molecules/cm2 for high surface albedo areas over the Sahara. Errors in SCIAMACHY CO total column retrievals due to errors other than instrument noise, like cloud cover, calibration, retrieval uncertainties and averaging kernels are estimated to be about 0.05–0.1 × 1018 molecules/cm2 in total. The bias found between model and observations is around 0.05–0.1 1018 molecules/cm2 (or about 5%) which also includes model errors. This thus provides a best estimate of the currently achievable measurement accuracy for SCIAMACHY CO monthly mean averages.

Modelling low-Reynolds-number effects in the turbulent air flow over water waves

In studies of the turbulent air flow over water waves it is usually assumed that the effect of viscosity near the water surface is negligible, i.e. the Reynolds number, Re = u ∗λ/ v , is considered to be high. However, for short waves or low wind speeds this assumption is not valid. Therefore, a second-order turbulence closure that takes into account viscous effects is used to simulate the air flow. The model shows reasonable agreement with laboratory measurements of wave-induced velocity profiles. Next, the dependence of the dimensionless energy flux from wind to waves, or growth rate, on Re is investigated. The growth rate of waves that are slow compared to the wind is found to increase strongly when Re decreases below 10 4 , with a maximum around Re = 800. The numerical model predictions are in good agreement with analytical theories and laboratory observations. Results of the study are useful in field conditions for the short waves in the spectrum, which are particularly important for remote sensing applications.

Rainwater path in warm clouds derived from combined visible/near‐infrared and microwave satellite observations

[1] The effects of warm rain on optical properties of clouds in the visible/near‐infrared (VNIR) and passive microwave (PMW) are studied using a simple conceptual cloud model. It is shown that the combined use of PMW and VNIR observations allows for the detection of precipitation and the derivation of rainwater path utilizing the different physical information content of the two observation types. Various potential error sources are studied and one month of combined geostationary visible/near infrared and Advanced Microwave Scanning Radiometer‐EOS (AMSR‐E) passive microwave observations off the coast of South Africa are evaluated using the proposed approach. Comparisons with CloudSat radar reflectivities are used for an independent assessment. A gradual increase in retrieved rainwater path with column maximum radar reflectivity is found for reflectivity values larger than −10 dBz. For monthly mean values at 1 × 1 degree resolution, rainwater path is correlated with in‐cloud liquid water path (R 2 = 0.50). The strongest correlation (R 2 = 0.69) exists between rainwater path and the inverse of cloud droplet number concentration (N). This finding is consistent with other studies supporting a 1/N dependency of precipitation intensity on cloud droplet number concentration in warm clouds.