The role of the basic state in the ENSO-monsoon relationship and implications for predictability

The impact of systematic model errors on a coupled simulation of the Asian Summer monsoon and its interannual variability is studied. Although the mean monsoon climate is reasonably well captured, systematic errors in the equatorial Pacific mean that the monsoon-ENSO teleconnection is rather poorly represented in the GCM. A system of ocean-surface heat flux adjustments is implemented in the tropical Pacific and Indian Oceans in order to reduce the systematic biases. In this version of the GCM, the monsoon-ENSO teleconnection is better simulated, particularly the lag-lead relationships in which weak monsoons precede the peak of El Nino. In part this is related to changes in the characteristics of El Nino, which has a more realistic evolution in its developing phase. A stronger ENSO amplitude in the new model version also feeds back to further strengthen the teleconnection. These results have important implications for the use of coupled models for seasonal prediction of systems such as the monsoon, and suggest that some form of flux correction may have significant benefits where model systematic error compromises important teleconnections and modes of interannual variability

Shipborne measurements of XCO2, XCH4, and XCO above the Pacific Ocean and comparison to CAMS atmospheric analyses and S5P/TROPOMI

Measurements of atmospheric column-averaged dry-air mole fractions of carbon dioxide (XCO2), methane (XCH4), and carbon monoxide (XCO) have been collected across the Pacific Ocean during the Measuring Ocean REferences 2 (MORE-2) campaign in June 2019.We deployed a shipborne variant of the EM27/SUN Fourier transform spectrometer (FTS) on board the German R/V Sonne which, during MORE-2, crossed the Pacific Ocean from Vancouver, Canada, to Singapore. Equipped with a specially manufactured fast solar tracker, the FTS operated in direct-sun viewing geometry during the ship cruise reliably delivering solar absorption spectra in the shortwave infrared spectral range (4000 to 11000 cm-1). After filtering and bias correcting the dataset, we report on XCO2, XCH4, and XCO measurements for 22 d along a trajectory that largely aligns with 30° N of latitude between 140°W and 120° E of longitude. The dataset has been scaled to the Total Carbon Column Observing Network (TCCON) station in Karlsruhe, Germany, before and after the MORE-2 campaign through side-by-side measurements. The 1σ repeatability of hourly means of XCO2, XCH4, and XCO is found to be 0.24 ppm, 1.1 ppb, and 0.75 ppb, respectively. The Copernicus Atmosphere Monitoring Service (CAMS) models gridded concentration fields of the atmospheric composition using assimilated satellite observations, which show excellent agreement of 0:52-0:31 ppm for XCO2, 0:9±4:1 ppb for XCH4, and 3:2-3:4 ppb for XCO (mean difference ± SD, standard deviation, of differences for entire record) with our observations. Likewise, we find excellent agreement to within 2:2±6:6 ppb with the XCO observations of the TROPOspheric MOnitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite (S5P). The shipborne measurements are accessible at https://doi.org/10.1594/PANGAEA.917240 (Knapp et al., 2020). © Author(s) 2021

Forecasts and assimilation experiments of the Antarctic ozone hole 2008

The 2008 Antarctic ozone hole was one of the largest and most long-lived in recent years. Predictions of the ozone hole were made in near-real time (NRT) and hindcast mode with the Integrated Forecast System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The forecasts were carried out both with and without assimilation of satellite observations from multiple instruments to provide more realistic initial conditions. Three different chemistry schemes were applied for the description of stratospheric ozone chemistry: (i) a linearization of the ozone chemistry, (ii) the stratospheric chemical mechanism of the Model of Ozone and Related Chemical Tracers, version 3, (MOZART-3) and (iii) the relaxation to climatology as implemented in the Transport Model, version 5, (TM5). The IFS uses the latter two schemes by means of a two-way coupled system. Without assimilation, the forecasts showed model-specific shortcomings in predicting start time, extent and duration of the ozone hole. The assimilation of satellite observations from the Microwave Limb Sounder (MLS), the Ozone Monitoring Instrument (OMI), the Solar Backscattering Ultraviolet radiometer (SBUV-2) and the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) led to a significant improvement of the forecasts when compared with total columns and vertical profiles from ozone sondes. The combined assimilation of observations from multiple instruments helped to overcome limitations of the ultraviolet (UV) sensors at low solar elevation over Antarctica. The assimilation of data from MLS was crucial to obtain a good agreement with the observed ozone profiles both in the polar stratosphere and troposphere. The ozone analyses by the three model configurations were very similar despite the different underlying chemistry schemes. Using ozone analyses as initial conditions had a very beneficial but variable effect on the predictability of the ozone hole over 15 days. The initialized forecasts with the MOZART-3 chemistry produced the best predictions of the increasing ozone hole whereas the linear scheme showed the best results during the ozonehole closure

Comprehensive evaluation of the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis against independent observations: Reactive gases

The Copernicus Atmosphere Monitoring Service (CAMS) is operationally providing forecast and reanalysis products of air quality and atmospheric composition. In this article, we present an extended evaluation of the CAMS global reanalysis data set of four reactive gases, namely, ozone (O-3), carbon monoxide (CO), nitrogen dioxide (NO2), and formaldehyde (HCHO), using multiple independent observations. Our results show that the CAMS model system mostly provides a stable and accurate representation of the global distribution of reactive gases over time. Our findings highlight the crucial impact of satellite data assimilation and emissions, investigated through comparison with a model run without assimilated data. Stratospheric and tropospheric O-3 are mostly well constrained by the data assimilation, except over Antarctica after 2012/2013 due to changes in the assimilated data. Challenges remain for O-3 in the Tropics and high-latitude regions during winter and spring. At the surface and for short-lived species (NO2), data assimilation is less effective. Total column CO in the CAMS reanalysis is well constrained by the assimilated satellite data. The control run, however, shows large overestimations of total column CO in the Southern Hemisphere and larger year-to-year variability in all regions. Concerning the long-term stability of the CAMS model, we note drifts in the time series of biases for surface O-3 and CO in the Northern midlatitudes and Tropics and for NO2 over East Asia, which point to biased emissions. Compared to the previous Monitoring Atmospheric Composition and Climate reanalysis, changes in the CAMS chemistry module and assimilation system helped to reduce biases and enhance the long-term temporal consistency of model results for the CAMS reanalysis

Global model simulations of air pollution during the 2003 European heat wave

Three global Chemistry Transport Models - MOZART, MOCAGE, and TM5 - as well as MOZART coupled to the IFS meteorological model including assimilation of ozone (O-3) and carbon monoxide (CO) satellite column retrievals, have been compared to surface measurements and MOZAIC vertical profiles in the troposphere over Western/Central Europe for summer 2003. The models reproduce the meteorological features and enhancement of pollution during the period 2-14 August, but not fully the ozone and CO mixing ratios measured during that episode. Modified normalised mean biases are around -25% (except similar to 5% for MOCAGE) in the case of ozone and from -80% to -30% for CO in the boundary layer above Frankfurt. The coupling and assimilation of CO columns from MOPITT overcomes some of the deficiencies in the treatment of transport, chemistry and emissions in MOZART, reducing the negative biases to around 20%. The high reactivity and small dry deposition velocities in MOCAGE seem to be responsible for the overestimation of O-3 in this model. Results from sensitivity simulations indicate that an increase of the horizontal resolution to around 1 degrees x1 degrees and potential uncertainties in European anthropogenic emissions or in long-range transport of pollution cannot completely account for the underestimation of CO and O-3 found for most models. A process-oriented TM5 sensitivity simulation where soil wetness was reduced results in a decrease in dry deposition fluxes and a subsequent ozone increase larger than the ozone changes due to the previous sensitivity runs. However this latest simulation still underestimates ozone during the heat wave and overestimates it outside that period. Most probably, a combination of the mentioned factors together with underrepresented biogenic emissions in the models, uncertainties in the modelling of vertical/horizontal transport processes in the proximity of the boundary layer as well as limitations of the chemistry schemes are responsible for the underestimation of ozone (overestimation in the case of MOCAGE) and CO found in the models during this extreme pollution event

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

Abstract. Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM

Global model simulations of air pollution during the 2003 European heat wave

Three global Chemistry Transport Models – MOZART, MOCAGE, and TM5 – as well as MOZART coupled to the IFS meteorological model including assimilation of ozone (O<sub>3</sub>) and carbon monoxide (CO) satellite column retrievals, have been compared to surface measurements and MOZAIC vertical profiles in the troposphere over Western/Central Europe for summer 2003. The models reproduce the meteorological features and enhancement of pollution during the period 2–14 August, but not fully the ozone and CO mixing ratios measured during that episode. Modified normalised mean biases are around −25% (except ~5% for MOCAGE) in the case of ozone and from −80% to −30% for CO in the boundary layer above Frankfurt. The coupling and assimilation of CO columns from MOPITT overcomes some of the deficiencies in the treatment of transport, chemistry and emissions in MOZART, reducing the negative biases to around 20%. The high reactivity and small dry deposition velocities in MOCAGE seem to be responsible for the overestimation of O<sub>3</sub> in this model. Results from sensitivity simulations indicate that an increase of the horizontal resolution to around 1°×1° and potential uncertainties in European anthropogenic emissions or in long-range transport of pollution cannot completely account for the underestimation of CO and O<sub>3</sub> found for most models. A process-oriented TM5 sensitivity simulation where soil wetness was reduced results in a decrease in dry deposition fluxes and a subsequent ozone increase larger than the ozone changes due to the previous sensitivity runs. However this latest simulation still underestimates ozone during the heat wave and overestimates it outside that period. Most probably, a combination of the mentioned factors together with underrepresented biogenic emissions in the models, uncertainties in the modelling of vertical/horizontal transport processes in the proximity of the boundary layer as well as limitations of the chemistry schemes are responsible for the underestimation of ozone (overestimation in the case of MOCAGE) and CO found in the models during this extreme pollution event

A deep stratosphere-to-troposphere ozone transport event over Europe simulated in CAMS global and regional forecast systems: analysis and evaluation

Stratosphere-to-troposphere transport (STT) is an important natural source of tropospheric ozone, which can occasionally influence ground-level ozone concentrations relevant for air quality. Here, we analyse and evaluate the Copernicus Atmosphere Monitoring Service (CAMS) global and regional forecast systems during a deep STT event over Europe for the time period from 4 to 9 January 2017. The predominant synoptic condition is described by a deep upper level trough over eastern and central Europe, favouring the formation of tropopause folding events along the jet stream axis and therefore the intrusion of stratospheric ozone into the troposphere. Both global and regional CAMS forecast products reproduce the hook-shaped streamer of ozone-rich and dry air in the middle troposphere depicted from the observed satellite images of water vapour. The CAMS global model successfully reproduces the folding of the tropopause at various European sites, such as Trapani (Italy), where a deep folding down to 550&thinsp;hPa is seen. The stratospheric ozone intrusions into the troposphere observed by WOUDC ozonesonde and IAGOS aircraft measurements are satisfactorily forecasted up to 3 days in advance by the CAMS global model in terms of both temporal and vertical features of ozone. The fractional gross error (FGE) of CAMS ozone day 1 forecast between 300 and 500&thinsp;hPa is 0.13 over Prague, while over Frankfurt it is 0.04 and 0.19, highlighting the contribution of data assimilation, which in most cases improves the model performance. Finally, the meteorological and chemical forcing of CAMS global forecast system in the CAMS regional forecast systems is found to be beneficial for predicting the enhanced ozone concentrations in the middle troposphere during a deep STT event.</p

Monitoring and assimilation tests with TROPOMI data in the CAMS system: near-real-time total column ozone

The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor (S5P) satellite launched in October 2017 yields a wealth of atmospheric composition data, including retrievals of total column ozone (TCO3) that are provided in near-real-time (NRT) and off-line. The NRT TCO3 retrievals (v1.0.0–v1.1.2) have been included in the data assimilation system of the Copernicus Atmosphere Monitoring Service (CAMS), and tests to monitor the data and to carry out first assimilation experiments with them have been performed for the period 26 November 2017 to 30 November 2018. The TROPOMI TCO3 data agree to within 2&thinsp;% with the CAMS analysis over large parts of the globe between 60∘&thinsp;N and 60∘&thinsp;S and also with TCO3 retrievals from the Ozone Monitoring Instrument (OMI) and the Global Ozone Monitoring Experiment-2 (GOME-2) that are routinely assimilated by CAMS. However, the TCO3 NRT data from TROPOMI show some retrieval anomalies at high latitudes, at low solar elevations and over snow/ice (e.g. Antarctica and snow-covered land areas in the Northern Hemisphere), where the differences with the CAMS analysis and the other data sets are larger. These differences are particularly pronounced over land in the NH during winter and spring (when they can reach up to 40&thinsp;DU) and come mainly from the surface albedo climatology that is used in the NRT TROPOMI TCO3 retrieval. This climatology has a coarser horizontal resolution than the TROPOMI TCO3 data, which leads to problems in areas where there are large changes in reflectivity from pixel to pixel, e.g. pixels covered by snow/ice or not. The differences between TROPOMI and the CAMS analysis also show some dependency on scan position. The assimilation of TROPOMI TCO3 has been tested in the CAMS system for data between 60∘&thinsp;N and 60∘&thinsp;S and for solar elevations greater than 10∘ and is found to have a small positive impact on the ozone analysis compared to Brewer TCO3 data and an improved fit to ozone sondes in the tropical troposphere and to IAGOS aircraft profiles at West African airports. The impact of the TROPOMI data is relatively small because the CAMS analysis is already well constrained by several other ozone retrievals that are routinely assimilated. When averaged over the periods February–April and September–October 2018, differences between experiments with and without assimilation of TROPOMI data are less than 2&thinsp;% for TCO3 and less than 3&thinsp;% in the vertical for seasonal mean zonal mean O3 mixing ratios, with the largest relative differences found in the troposphere.</p