Abstract.A new global atmospheric carbon dioxide (CO 2 ) real-time forecast is now available as part of the preoperational Monitoring of Atmospheric Composition and Climate -Interim Implementation (MACC-II) service using the infrastructure of the European Centre for MediumRange Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). One of the strengths of the CO 2 forecasting system is that the land surface, including vegetation CO 2 fluxes, is modelled online within the IFS. Other CO 2 fluxes are prescribed from inventories and from off-line statistical and physical models. The CO 2 forecast also benefits from the transport modelling from a state-of-the-art numerical weather prediction (NWP) system initialized daily with a wealth of meteorological observations. This paper describes the capability of the forecast in modelling the variability of CO 2 on different temporal and spatial scales compared to observations. The modulation of the amplitude of the CO 2 diurnal cycle by near-surface winds and boundary layer height is generally well represented in the forecast. The CO 2 forecast also has high skill in simulating day-today synoptic variability. In the atmospheric boundary layer, this skill is significantly enhanced by modelling the day-today variability of the CO 2 fluxes from vegetation compared to using equivalent monthly mean fluxes with a diurnal cycle. However, biases in the modelled CO 2 fluxes also lead to accumulating errors in the CO 2 forecast. These biases vary with season with an underestimation of the amplitude of the seasonal cycle both for the CO 2 fluxes compared to total optimized fluxes and the atmospheric CO 2 compared to observations. The largest biases in the atmospheric CO 2 forecast are found in spring, corresponding to the onset of the growing season in the Northern Hemisphere. In the future, the forecast will be re-initialized regularly with atmospheric CO 2 analyses based on the assimilation of CO 2 products retrieved from satellite measurements and CO 2 in situ observations, as they become available in near-real time. In this way, the accumulation of errors in the atmospheric CO 2 forecast will be reduced. Improvements in the CO 2 forecast are also expected with the continuous developments in the operational IFS.
Abstract. The seasonal cycle accounts for a dominant mode of total column CO 2 (XCO 2 ) annual variability and is connected to CO 2 uptake and release; it thus represents an important quantity to test the accuracy of the measurements from space. We quantitatively evaluate the XCO 2 seasonal cycle of the Greenhouse Gases Observing Satellite (GOSAT) observations from the Atmospheric CO 2 Observations from Space (ACOS) retrieval system and compare average regional seasonal cycle features to those directly measured by the Total Carbon Column Observing Network (TCCON). We analyse the mean seasonal cycle amplitude, dates of maximum and minimum XCO 2 , as well as the regional growth rates in XCO 2 through the fitted trend over several years. We find that GOSAT/ACOS captures the seasonal cycle amplitude within 1.0 ppm accuracy compared to TCCON, except in Europe, where the difference exceeds 1.0 ppm at two sites, and the amplitude captured by GOSAT/ACOS is generally shallower compared to TCCON. This bias over Europe is not as large for the other GOSAT retrieval algorithms (NIES v02.21, RemoTeC v2.35, UoL v5.1, and NIES PPDF-S v.02.11), although they have significant biases at other sites. We find that the ACOS bias correction partially explains the shallow amplitude over Europe. The impact of the co-location method and aerosol changes in the ACOS algorithm were also tested and found to be few tenths of a ppm and mostly non-systematic. We find generally good agreement in the date of minimum XCO 2 between ACOS and TCCON, but ACOS generally infers a date of maximum XCO 2 2-3 weeks later than TCCON. We further analyse the latitudinal dependence of the seasonal cycle amplitude throughout the Northern Hemisphere and compare the dependence to that predicted by current optimized models that assimilate in situ measurements of CO 2 . In the zonal averages, models are consistent with the GOSAT amplitude to within 1.4 ppm, depending on the model and latitude. We also show that the seasonal cycle of XCO 2 depends on longitude especially at the mid-latitudes: the amplitude of GOSAT XCO 2 doubles from western USA to East Asia at 45-50 • N, which is only partially shown by the models. In general, we find that model-tomodel differences can be larger than GOSAT-to-model difPublished by Copernicus Publications on behalf of the European Geosciences Union. ferences. These results suggest that GOSAT/ACOS retrievals of the XCO 2 seasonal cycle may be sufficiently accurate to evaluate land surface models in regions with significant discrepancies between the models.
Abstract. We have measured spectral albedo, as well as ancillary parameters, of seasonal European Arctic snow at Sodankylä, Finland (67°22' N, 26°39' E). The springtime intensive melt period was observed during the Snow Reflectance Transition Experiment (SNORTEX) in April 2009. The upwelling and downwelling spectral irradiance, measured at 290–550 nm with a double monochromator spectroradiometer, revealed albedo values of ~0.5–0.7 for the ultraviolet and visible range, both under clear sky and variable cloudiness. During the most intensive snowmelt period of four days, albedo decreased from 0.65 to 0.45 at 330 nm, and from 0.72 to 0.53 at 450 nm. In the literature, the UV and VIS albedo for clean snow are ~0.97–0.99, consistent with the extremely small absorption coefficient of ice in this spectral region. Our low albedo values were supported by two independent simultaneous broadband albedo measurements, and simulated albedo data. We explain the low albedo values to be due to (i) large snow grain sizes up to ~3 mm in diameter; (ii) meltwater surrounding the grains and increasing the effective grain size; (iii) absorption caused by impurities in the snow, with concentration of elemental carbon (black carbon) in snow of 87 ppb, and organic carbon 2894 ppb, at the time of albedo measurements. The high concentrations of carbon, detected by the thermal–optical method, were due to air masses originating from the Kola Peninsula, Russia, where mining and refining industries are located.
Abstract. We present 5 years of GOSAT XCH 4 retrieved using the "proxy" approach. The Proxy XCH 4 data are validated against ground-based TCCON observations and are found to be of high quality with a small bias of 4.8 ppb (∼ 0.27 %) and a single-sounding precision of 13.4 ppb (∼ 0.74 %). The station-to-station bias (a measure of the relative accuracy) is found to be 4.2 ppb. For the first time the XCH 4 / XCO 2 ratio component of the Proxy retrieval is validated (bias of 0.014 ppb ppm −1 (∼ 0.30 %), single-sounding precision of 0.033 ppb ppm −1 (∼ 0.72 %)).The uncertainty relating to the model XCO 2 component of the Proxy XCH 4 is assessed through the use of an ensemble of XCO 2 models. While each individual XCO 2 model is found to agree well with the TCCON validation data (r = 0.94-0.97), it is not possible to select one model as the best from our comparisons. The median XCO 2 value of the ensemble has a smaller scatter against TCCON (a standard deviation of 0.92 ppm) than any of the individual models whilst maintaining a small bias (0.15 ppm). This model median XCO 2 is used to calculate the Proxy XCH 4 with the maximum deviation of the ensemble from the median used as an estimate of the uncertainty.We compare this uncertainty to the a posteriori retrieval error (which is assumed to reduce with sqrt(N)) and find typically that the model XCO 2 uncertainty becomes significant during summer months when the a posteriori error is at its lowest due to the increase in signal related to increased summertime reflected sunlight.We assess the significance of these model and retrieval uncertainties on flux inversion by comparing the GOSAT XCH 4 against modelled XCH 4 from TM5-4DVAR constrained by NOAA surface observations (MACC reanalysis scenario S1-NOAA). We find that for the majority of regions the differences are much larger than the estimated uncertainties. Our findings show that useful information will be provided to the inversions for the majority of regions in addition to that already provided by the assimilated surface measurements.
Abstract. This paper presents a first statistical validation of tropospheric ozone products derived from measurements of the IASI satellite instrument. Since the end of 2006, IASI (Infrared Atmospheric Sounding Interferometer) aboard the polar orbiter Metop-A measures infrared spectra of the Earth's atmosphere in nadir geometry. This validation covers the northern mid-latitudes and the period from July 2007 to August 2008. Retrieval results from four different sources are presented: three are from scientific products (LATMOS, LISA, LPMAA) and the fourth one is the pre-operational product distributed by EUMETSAT (version Correspondence to: M. Eremenko (maxim.eremenko@lisa.univ-paris12.fr) 4.2). The different products are derived from different algorithms with different approaches. The difference and their implications for the retrieved products are discussed. In order to evaluate the quality and the performance of each product, comparisons with the vertical ozone concentration profiles measured by balloon sondes are performed and lead to estimates of the systematic and random errors in the IASI ozone products (profiles and partial columns). A first comparison is performed on the given profiles; a second comparison takes into account the altitude dependent sensitivity of the retrievals. Tropospheric columnar amounts are compared to the sonde for a lower tropospheric column (surface to about 6 km) and a "total" tropospheric column (surface to about 11 km). On average both tropospheric columns have small biases for the scientific products, less than 2 DobsonPublished by Copernicus Publications on behalf of the European Geosciences Union. C. Keim et al.: IASI tropospheric ozone validationUnits (DU) for the lower troposphere and less than 1 DU for the total troposphere. The comparison of the still preoperational EUMETSAT columns shows higher mean differences of about 5 DU.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.