Abstract. Natural methane (CH 4 ) emissions from wet ecosystems are an important part of today's global CH 4 budget. Climate affects the exchange of CH 4 between ecosystems and the atmosphere by influencing CH 4 production, oxidation, and transport in the soil. The net CH 4 exchange depends on ecosystem hydrology, soil and vegetation characteristics. Here, the LPJ-WHyMe global dynamical vegetation model is used to simulate global net CH 4 emissions for different ecosystems: northern peatlands (45 • -90 • N), naturally inundated wetlands (60 • S-45 • N), rice agriculture and wet mineral soils. Mineral soils are a potential CH 4 sink, but can also be a source with the direction of the net exchange depending on soil moisture content. The geographical and seasonal distributions are evaluated against multi-dimensional atmospheric inversions for 2003-2005, using two independent four-dimensional variational assimilation systems. The atmospheric inversions are constrained by the atmospheric CH 4 observations of the SCIAMACHY satellite instrument and global surface networks. Compared to LPJ-WHyMe the inversions result in a significant reduction in the emissions from northern peatlands and suggest that LPJ-WHyMe maximum annual emissions peak about one month late. TheCorrespondence to: R. Spahni (spahni@climate.unibe.ch) inversions do not put strong constraints on the division of sources between inundated wetlands and wet mineral soils in the tropics. Based on the inversion results we diagnose model parameters in LPJ-WHyMe and simulate the surface exchange of CH 4 over the period [1990][1991][1992][1993][1994][1995][1996][1997][1998][1999][2000][2001][2002][2003][2004][2005][2006][2007][2008]. Over the whole period we infer an increase of global ecosystem CH 4 emissions of +1.11 Tg CH 4 yr −1 , not considering potential additional changes in wetland extent. The increase in simulated CH 4 emissions is attributed to enhanced soil respiration resulting from the observed rise in land temperature and in atmospheric carbon dioxide that were used as input. The longterm decline of the atmospheric CH 4 growth rate from 1990 to 2006 cannot be fully explained with the simulated ecosystem emissions. However, these emissions show an increasing trend of +3.62 Tg CH 4 yr −1 over 2005-2008 which can partly explain the renewed increase in atmospheric CH 4 concentration during recent years.

Natural methane (CH₄) emissions from wet ecosystems are an important part of today's global CH₄ budget. Climate affects the exchange of CH₄ between ecosystems and the atmosphere by influencing CH₄ production, oxidation, and transport in the soil. The net CH₄ exchange depends on ecosystem hydrology, soil and vegetation characteristics. Here, the LPJ-WHyMe global dynamical vegetation model is used to simulate global net CH₄ emissions for different ecosystems: northern peatlands (45°–90° N), naturally inundated wetlands (60° S–45° N), rice agriculture and wet mineral soils. Mineral soils are a potential CH₄ sink, but can also be a source with the direction of the net exchange depending on soil moisture content. The geographical and seasonal distributions are evaluated against multi-dimensional atmospheric inversions for 2003–2005, using two independent four-dimensional variational assimilation systems. The atmospheric inversions are constrained by the atmospheric CH₄ observations of the SCIAMACHY satellite instrument and global surface networks. Compared to LPJ-WHyMe the inversions result in a significant reduction in the emissions from northern peatlands and suggest that LPJ-WHyMe maximum annual emissions peak about one month late. The inversions do not put strong constraints on the division of sources between inundated wetlands and wet mineral soils in the tropics. Based on the inversion results we adapt model parameters in LPJ-WHyMe and simulate the surface exchange of CH₄ over the period 1990–2008. Over the whole period we infer an increase of global ecosystem CH₄ emissions of +1.11 Tg CH₄ yr⁻¹, not considering potential additional changes in wetland extent. The increase in simulated CH₄ emissions is attributed to enhanced soil respiration resulting from the observed rise in land temperature and in atmospheric carbon dioxide that were used as input. The long-term decline of the atmospheric CH₄ growth rate from 1990 to 2006 cannot be fully explained with the simulated ecosystem emissions. However, these emissions show an increasing trend of +3.62 Tg CH₄ yr⁻¹ over 2005–2008 which can partly explain the renewed increase in atmospheric CH₄ concentration during recent years

[1] Historical observations of the 13 C/ 12 C ratio of atmospheric CH 4 are used to constrain the present-day methane budget using optimal estimation. Three methane emission scenarios with basis in the recent literature are evaluated against historical 13 CH 4 observations, considering all uncertainties together. We estimate that present-day methane emissions are composed of 64%-76% biogenic, 19%-30% fossil, and 4%-6% pyrogenic sources. It is found that, barring any changes in the isotopic signatures of sources and sink processes, satisfying the 13 C/ 12 C record requires estimates of present-day anthropogenic fuel-related emissions that are on the high end of the assumed uncertainties, even when a significant geological source is included. Extending present-day results to the time of the Last Glacial Maximum (LGM), emissions from wetlands are implied to be 40%-62% of the present-day value, the higher number being valid only for a scenario with strong (∼30 Tg/a) geological emissions and roughly 20% greater biomass burning emissions at LGM relative to the present-day.Citation: Neef, L., M. van Weele, and P. van Velthoven (2010), Optimal estimation of the present-day global methane budget, Global Biogeochem. Cycles, 24, GB4024,

[1] The recent variability of the tropopause temperature and the tropopause inversion layer (TIL) are investigated with Global Positioning System Radio Occultation data and simulations with the National Center for Atmospheric Research's Whole Atmosphere Community Climate Model (WACCM). Over the past decade (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)) the data show an increase of 0.8 K in the tropopause temperature and a decrease of 0.4 K in the strength of the tropopause inversion layer in the tropics, meaning that the vertical temperature gradient has declined, and therefore that the stability above the tropopause has weakened. WACCM simulations with finer vertical resolution show a more realistic TIL structure and variability. Model simulations show that the increased tropopause temperature and the weaker tropopause inversion layer are related to weakened upwelling in the tropics. Such changes in the thermal structure of the upper troposphere and lower stratosphere may have important implications for climate, such as a possible rise in water vapor in the lower stratosphere.

The problem of spurious excitation of gravity waves in the context of four-dimensional data assimilation is investigated using a simple model of balanced dynamics. The model admits a chaotic vortical mode coupled to a comparatively fast gravity wave mode, and can be initialized such that the model evolves on a so-called slow manifold, where the fast motion is suppressed. Identical twin assimilation experiments are performed, comparing the extended and ensemble Kalman filters (EKF and EnKF, respectively). The EKF uses a tangent linear model (TLM) to estimate the evolution of forecast error statistics in time, whereas the EnKF uses the statistics of an ensemble of nonlinear model integrations. Specifically, the case is examined where the true state is balanced, but observation errors project onto all degrees of freedom, including the fast modes. It is shown that the EKF and EnKF will assimilate observations in a balanced way only if certain assumptions hold, and that, outside of ideal cases (i.e., with very frequent observations), dynamical balance can easily be lost in the assimilation. For the EKF, the repeated adjustment of the covariances by the assimilation of observations can easily unbalance the TLM, and destroy the assumptions on which balanced assimilation rests. It is shown that an important factor is the choice of initial forecast error covariance matrix. A balance-constrained EKF is described and compared to the standard EKF, and shown to offer significant improvement for observation frequencies where balance in the standard EKF is lost. The EnKF is advantageous in that balance in the error covariances relies only on a balanced forecast ensemble, and that the analysis step is an ensemble-mean operation. Numerical experiments show that the EnKF may be preferable to the EKF in terms of balance, though its validity is limited by ensemble size. It is also found that overobserving can lead to a more unbalanced forecast ensemble and thus to an unbalanced analysis.

Data assimilation was recently suggested to smooth out the sharp gradients that characterize the tropopause inversion layer (TIL) in systems that did not assimilate TIL‐resolving observations. We investigate whether this effect is present in the ERA‐Interim reanalysis and the European Centre for Medium‐Range Weather Forecasts (ECMWF) operational forecast system (which assimilate high‐resolution observations) by analyzing the 4D‐Var increments and how the TIL is represented in their data assimilation systems. For comparison, we also diagnose the TIL from high‐resolution GPS radio occultation temperature profiles from the COSMIC satellite mission, degraded to the same vertical resolution as ERA‐Interim and ECMWF operational analyses. Our results show that more recent reanalysis and forecast systems improve the representation of the TIL, updating the earlier hypothesis. However, the TIL in ERA‐Interim and ECMWF operational analyses is still weaker and farther away from the tropopause than GPS radio occultation observations of the same vertical resolution.

The behavior of the ensemble Kalman filter (EnKF) is examined in the context of a model that exhibits a nonlinear chaotic (slow) vortical mode coupled to a linear (fast) gravity wave of a given amplitude and frequency. It is shown that accurate recovery of both modes is enhanced when covariances between fast and slow normal-mode variables (which reflect the slaving relations inherent in balanced dynamics) are modeled correctly. More ensemble members are needed to recover the fast, linear gravity wave than the slow, vortical motion. Although the EnKF tends to diverge in the analysis of the gravity wave, the filter divergence is stable and does not lead to a great loss of accuracy. Consequently, provided the ensemble is large enough and observations are made that reflect both time scales, the EnKF is able to recover both time scales more accurately than optimal interpolation (OI), which uses a static error covariance matrix. For OI it is also found to be problematic to observe the state at a frequency that is a subharmonic of the gravity wave frequency, a problem that is in part overcome by the EnKF. However, error in the modeled gravity wave parameters can be detrimental to the performance of the EnKF and remove its implied advantages, suggesting that a modified algorithm or a method for accounting for model error is needed.

[1] Changes in Earth rotation are strongly related to fluctuations in the angular momentum of the atmosphere, and therefore contain integral information about the atmospheric state. Here we investigate the extent to which observed Earth rotation parameters can be used to evaluate and potentially constrain atmospheric models. This is done by comparing the atmospheric excitation function, computed geophysically from reanalysis data and climate model simulations constrained only by boundary forcings, to the excitation functions inferred from geodetic monitoring data. Model differences are assessed for subseasonal variations, the annual and semiannual cycles, interannual variations, and decadal-scale variations. Observed length-of-day anomalies on the subseasonal timescale are simulated well by the simulations that are constrained by meteorological data only, whereas the annual cycle in length-of-day is simulated well by all models. Interannual length-of-day variations are captured fairly well as long as a model has realistic, time-varying SST boundary conditions and QBO forcing. Observations of polar motion are most clearly relatable to atmospheric dynamics on subseasonal to annual timescales, though angular momentum budget closure is difficult to achieve even for data-constrained atmospheric simulations. Closure of the angular momentum budget on decadal timescales is difficult and strongly dependent on estimates of angular momentum fluctuations due to core-mantle interactions in the solid Earth.