Spring enhancement and summer reduction in carbon uptake during the 2018 drought in northwestern Europe
Smith, Naomi E. ; Kooijmans, Linda M.J. ; Koren, Gerbrand ; Schaik, Erik van; Woude, Auke M. van der; Wanders, Niko ; Ramonet, Michel ; Xueref-Remy, Irène ; Siebicke, Lukas ; Manca, Giovanni ; Brümmer, Christian ; Baker, Ian T. ; Haynes, Katherine D. ; Luijkx, Ingrid T. ; Peters, Wouter - \ 2020
Philosophical Transactions of the Royal Society B. Biological sciences 375 (2020)1810. - ISSN 0962-8436 - 1 p.
CO2 - data assimilation - drought - European carbon balance - remote sensing
We analysed gross primary productivity (GPP), total ecosystem respiration (TER) and the resulting net ecosystem exchange (NEE) of carbon dioxide (CO2) by the terrestrial biosphere during the summer of 2018 through observed changes across the Integrated Carbon Observation System (ICOS) network, through biosphere and inverse modelling, and through remote sensing. Highly correlated yet independently-derived reductions in productivity from sun-induced fluorescence, vegetative near-infrared reflectance, and GPP simulated by the Simple Biosphere model version 4 (SiB4) suggest a 130-340 TgC GPP reduction in July-August-September (JAS) of 2018. This occurs over an area of 1.6 × 106 km2 with anomalously low precipitation in northwestern and central Europe. In this drought-affected area, reduced GPP, TER, NEE and soil moisture at ICOS ecosystem sites are reproduced satisfactorily by the SiB4 model. We found that, in contrast to the preceding 5 years, low soil moisture is the main stress factor across the affected area. SiB4's NEE reduction by 57 TgC for JAS coincides with anomalously high atmospheric CO2 observations in 2018, and this is closely matched by the NEE anomaly derived by CarbonTracker Europe (52 to 83 TgC). Increased NEE during the spring (May-June) of 2018 (SiB4 -52 TgC; CTE -46 to -55 TgC) largely offset this loss, as ecosystems took advantage of favourable growth conditions. This article is part of the theme issue 'Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale'.
Joint Assimilation of Leaf Area Index and Soil Moisture from Sentinel-1 and Sentinel-2 Data into the WOFOST Model for Winter Wheat Yield Estimation
Pan, Haizhu ; Chen, Zhongxin ; Wit, Allard de; Ren, Jianqiang - \ 2019
Sensors 19 (2019)14. - ISSN 1424-8220
data assimilation - EnKF - LAI - Sentinel-1 and Sentinel-2 - SM - winter wheat yield - WOFOST
It is well known that timely crop growth monitoring and accurate crop yield estimation at a fine scale is of vital importance for agricultural monitoring and crop management. Crop growth models have been widely used for crop growth process description and yield prediction. In particular, the accurate simulation of important state variables, such as leaf area index (LAI) and root zone soil moisture (SM), is of great importance for yield estimation. Data assimilation is a useful tool that combines a crop model and external observations (often derived from remote sensing data) to improve the simulated crop state variables and consequently model outputs like crop total biomass, water use and grain yield. In spite of its effectiveness, applying data assimilation for monitoring crop growth at the regional scale in China remains challenging, due to the lack of high spatiotemporal resolution satellite data that can match the small field sizes which are typical for agriculture in China. With the accessibility of freely available images acquired by Sentinel satellites, it becomes possible to acquire data at high spatiotemporal resolution (10-30 m, 5-6 days), which offers attractive opportunities to characterize crop growth. In this study, we assimilated remotely sensed LAI and SM into the Word Food Studies (WOFOST) model to estimate winter wheat yield using an ensemble Kalman filter (EnKF) algorithm. The LAI was calculated from Sentinel-2 using a lookup table method, and the SM was calculated from Sentinel-1 and Sentinel-2 based on a change detection approach. Through validation with field data, the inverse error was 10% and 35% for LAI and SM, respectively. The open-loop wheat yield estimation, independent assimilations of LAI and SM, and a joint assimilation of LAI + SM were tested and validated using field measurement observation in the city of Hengshui, China, during the 2016-2017 winter wheat growing season. The results indicated that the accuracy of wheat yield simulated by WOFOST was significantly improved after joint assimilation at the field scale. Compared to the open-loop estimation, the yield root mean square error (RMSE) with field observations was decreased by 69 kg/ha for the LAI assimilation, 39 kg/ha for the SM assimilation and 167 kg/ha for the joint LAI + SM assimilation. Yield coefficients of determination (R2) of 0.41, 0.65, 0.50, and 0.76 and mean relative errors (MRE) of 4.87%, 4.32%, 4.45% and 3.17% were obtained for open-loop, LAI assimilation alone, SM assimilation alone and joint LAI + SM assimilation, respectively. The results suggest that LAI was the first-choice variable for crop data assimilation over SM, and when both LAI and SM satellite data are available, the joint data assimilation has a better performance because LAI and SM have interacting effects. Hence, joint assimilation of LAI and SM from Sentinel-1 and Sentinel-2 at a 20 m resolution into the WOFOST provides a robust method to improve crop yield estimations. However, there is still bias between the key soil moisture in the root zone and the Sentinel-1 C band retrieved SM, especially when the vegetation cover is high. By active and passive microwave data fusion, it may be possible to offer a higher accuracy SM for crop yield prediction.
Methane budget estimates in Finland from the CarbonTracker Europe-CH4 data assimilation system
Tsuruta, Aki ; Aalto, Tuula ; Backman, Leif ; Krol, Maarten C. ; Peters, Wouter ; Lienert, Sebastian ; Joos, Fortunat ; Miller, Paul A. ; Zhang, Wenxin ; Laurila, Tuomas ; Hatakka, Juha ; Leskinen, Ari ; Lehtinen, Kari E.J. ; Peltola, Olli ; Vesala, Timo ; Levula, Janne ; Dlugokencky, Ed ; Heimann, Martin ; Kozlova, Elena ; Aurela, Mika ; Lohila, Annalea ; Kauhaniemi, Mari ; Gomez-Pelaez, Angel J. - \ 2019
Tellus Series B: Chemical and Physical Meteorology 71 (2019)1. - ISSN 0280-6509 - p. 1 - 20.
atmospheric CH - CH flux - data assimilation - Finland - flux estimation
We estimated the CH4 budget in Finland for 2004–2014 using the CTE-CH4 data assimilation system with an extended atmospheric CH4 observation network of seven sites from Finland to surrounding regions (Hyytiälä, Kjølnes, Kumpula, Pallas, Puijo, Sodankylä, and Utö). The estimated average annual total emission for Finland is 0.6 ± 0.5 Tg CH4 yr−1. Sensitivity experiments show that the posterior biospheric emission estimates for Finland are between 0.3 and 0.9 Tg CH4 yr−1, which lies between the LPX-Bern-DYPTOP (0.2 Tg CH4 yr−1) and LPJG-WHyMe (2.2 Tg CH4 yr−1) process-based model estimates. For anthropogenic emissions, we found that the EDGAR v4.2 FT2010 inventory (0.4 Tg CH4 yr−1) is likely to overestimate emissions in southernmost Finland, but the extent of overestimation and possible relocation of emissions are difficult to derive from the current observation network. The posterior emission estimates were especially reliant on prior information in central Finland. However, based on analysis of posterior atmospheric CH4, we found that the anthropogenic emission distribution based on a national inventory is more reliable than the one based on EDGAR v4.2 FT2010. The contribution of total emissions in Finland to global total emissions is only about 0.13%, and the derived total emissions in Finland showed no trend during 2004–2014. The model using optimized emissions was able to reproduce observed atmospheric CH4 at the sites in Finland and surrounding regions fairly well (correlation > 0.75, bias < ± ppb), supporting adequacy of the observations to be used in atmospheric inversion studies. In addition to global budget estimates, we found that CTE-CH4 is also applicable for regional budget estimates, where small scale (1x1 in this case) optimization is possible with a dense observation network.
Balance of Emission and Dynamical Controls on Ozone During the Korea-United States Air Quality Campaign From Multiconstituent Satellite Data Assimilation
Miyazaki, K. ; Sekiya, T. ; Fu, D. ; Bowman, K.W. ; Kulawik, S.S. ; Sudo, K. ; Walker, T. ; Kanaya, Y. ; Takigawa, M. ; Ogochi, K. ; Eskes, H. ; Boersma, K.F. ; Thompson, A.M. ; Gaubert, B. ; Barre, J. ; Emmons, L.K. - \ 2019
Journal of Geophysical Research: Atmospheres 124 (2019)1. - ISSN 2169-897X - p. 387 - 413.
air quality - Asia - data assimilation - emission - ozone - satellite
Global multiconstituent concentration and emission fields obtained from the assimilation of the satellite retrievals of ozone, CO, NO2, HNO3, and SO2 from the Ozone Monitoring Instrument (OMI), Global Ozone Monitoring Experiment 2, Measurements of Pollution in the Troposphere, Microwave Limb Sounder, and Atmospheric Infrared Sounder (AIRS)/OMI are used to understand the processes controlling air pollution during the Korea-United States Air Quality (KORUS-AQ) campaign. Estimated emissions in South Korea were 0.42 Tg N for NOx and 1.1 Tg CO for CO, which were 40% and 83% higher, respectively, than the a priori bottom-up inventories, and increased mean ozone concentration by up to 7.5 ± 1.6 ppbv. The observed boundary layer ozone exceeded 90 ppbv over Seoul under stagnant phases, whereas it was approximately 60 ppbv during dynamical conditions given equivalent emissions. Chemical reanalysis showed that mean ozone concentration was persistently higher over Seoul (75.10 ± 7.6 ppbv) than the broader KORUS-AQ domain (70.5 ± 9.2 ppbv) at 700 hPa. Large bias reductions (>75%) in the free tropospheric OH show that multiple-species assimilation is critical for balanced tropospheric chemistry analysis and emissions. The assimilation performance was dependent on the particular phase. While the evaluation of data assimilation fields shows an improved agreement with aircraft measurements in ozone (to less than 5 ppbv biases), CO, NO2, SO2, PAN, and OH profiles, lower tropospheric ozone analysis error was largest at stagnant conditions, whereas the model errors were mostly removed by data assimilation under dynamic weather conditions. Assimilation of new AIRS/OMI ozone profiles allowed for additional error reductions, especially under dynamic weather conditions. Our results show the important balance of dynamics and emissions both on pollution and the chemical assimilation system performance.
Two perspectives on the coupled carbon, water and energy exchange in the planetary boundary layer
Combe, M. ; Vilà-Guerau De Arellano, J. ; Ouwersloot, H.G. ; Jacobs, C.M.J. ; Peters, W. - \ 2015
Biogeosciences 12 (2015). - ISSN 1726-4170 - p. 103 - 123.
ensemble kalman filter - land-surface model - leaf-area index - soil-moisture - use efficiency - climate-change - crop model - stomatal conductance - data assimilation - plant geography
Understanding the interactions between the land surface and the atmosphere is key to modelling boundary-layer meteorology and cloud formation, as well as carbon cycling and crop yield. In this study we explore these interactions in the exchange of water, heat and CO2 in a cropland–atmosphere system at the diurnal and local scale. To that end, we couple an atmospheric mixed-layer model (MXL) to two land-surface schemes developed from two different perspectives: while one land-surface scheme (A-gs) simulates vegetation from an atmospheric point of view, the other (GECROS) simulates vegetation from a carbon-storage point of view. We calculate surface fluxes of heat, moisture and carbon, as well as the resulting atmospheric state and boundary-layer dynamics, over a maize field in the Netherlands, on a day for which we have a rich set of observations available. Particular emphasis is placed on understanding the role of upper-atmosphere conditions like subsidence in comparison to the role of surface forcings like soil moisture. We show that the atmospheric-oriented model (MXL-A-gs) outperforms the carbon storage-oriented model (MXL-GECROS) on this diurnal scale. We find this performance is partly due to the difference of scales at which the models were made to run. Most importantly, this performance strongly depends on the sensitivity of the modelled stomatal conductance to water stress, which is implemented differently in each model. This sensitivity also influences the magnitude of the surface fluxes of CO2, water and heat (surface control) and subsequently impacts the boundary-layer growth and entrainment fluxes (upper atmosphere control), which alter the atmospheric state. These findings suggest that observed CO2 mole fractions in the boundary layer can reflect strong influences of both the surface and upper-atmosphere conditions, and the interpretation of CO2 mole fraction variations depends on the assumed land-surface coupling. We illustrate this with a sensitivity analysis where high subsidence and soil moisture depletion, typical for periods of drought, have competing and opposite effects on the boundary-layer height h. The resulting net decrease in h induces a change of 12 ppm in the late-afternoon CO2 mole fraction. Also, the effect of such high subsidence and soil moisture depletion on the surface Bowen ratio are of the same magnitude. Thus, correctly including such two-way land-surface interactions on the diurnal scale can potentially improve our understanding and interpretation of observed variations in atmospheric CO2, as well as improve crop yield forecasts by better describing the water loss and carbon gain.
Contribution of Dynamic Vegetation Phenology to Decadal Climate Predictability
Weiss, M. ; Miller, P.A. ; Hurk, B.J.J.M. van den; Noije, T. van; Stefanescu, S. ; Haarsma, R. ; Ulft, L.H. van; Hazeleger, W. ; Sager, P. Le; Smith, B. ; Schurgers, G. - \ 2014
Journal of Climate 27 (2014)22. - ISSN 0894-8755 - p. 8563 - 8577.
leaf-area index - ensemble forecasts - data assimilation - soil-moisture - model - prediction - system - impact - skill - oscillation
In this study, the impact of coupling and initializing the leaf area index from the dynamic vegetation model Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) is analyzed on skill of decadal predictions in the fully coupled atmosphere-land-ocean-sea ice model, the European Consortium Earth System Model (EC-Earth). Similar to the impact of initializing the model with the observed oceanic state, initializing the leaf area index (LAI) fields obtained from an offline LPJ-GUESS simulation forced by the observed atmospheric state leads to a systematic drift. A different treatment of the water and soil moisture budget in LPJ-GUESS is a likely cause of this drift. The coupled system reduces the cold bias of the reference model over land by reducing LAI (and the associated evaporative cooling), particularly outside the growing season. The coupling with the interactive vegetation module implies more degrees of freedom in the coupled model, which generates more noise that can mask a portion of the extra signal that is generated. The forecast reliability improves marginally, particularly early in the forecast. Ranked probability skill scores are also improved slightly in most areas analyzed, but the signal is not fully coherent over the forecast interval because of the relatively low number of ensemble members. Methods to remove the LAI drift and allow coupling of other variables probably need to be implemented before significant forecast skill can be expected.
A multi-year methane inversion using SCIAMACHY, accounting for systematic errors using TCCON measurements
Houweling, S. ; Krol, M.C. ; Bergamaschi, P. ; Frankenberg, C. ; Dlugokencky, E.J. ; Morino, I. - \ 2014
Atmospheric Chemistry and Physics 14 (2014). - ISSN 1680-7316 - p. 3991 - 4012.
column observing network - atmospheric methane - carbon-dioxide - tropospheric methane - lower stratosphere - data assimilation - transport model - emissions - ch4 - gosat
This study investigates the use of total column CH4 (XCH4) retrievals from the SCIAMACHY satellite instrument for quantifying large-scale emissions of methane. A unique data set from SCIAMACHY is available spanning almost a decade of measurements, covering a period when the global CH4 growth rate showed a marked transition from stable to increasing mixing ratios. The TM5 4DVAR inverse modelling system has been used to infer CH4 emissions from a combination of satellite and surface measurements for the period 2003–2010. In contrast to earlier inverse modelling studies, the SCIAMACHY retrievals have been corrected for systematic errors using the TCCON network of ground-based Fourier transform spectrometers. The aim is to further investigate the role of bias correction of satellite data in inversions. Methods for bias correction are discussed, and the sensitivity of the optimized emissions to alternative bias correction functions is quantified. It is found that the use of SCIAMACHY retrievals in TM5 4DVAR increases the estimated inter-annual variability of large-scale fluxes by 22% compared with the use of only surface observations. The difference in global methane emissions between 2-year periods before and after July 2006 is estimated at 27–35 Tg yr-1. The use of SCIAMACHY retrievals causes a shift in the emissions from the extra-tropics to the tropics of 50 ± 25 Tg yr-1. The large uncertainty in this value arises from the uncertainty in the bias correction functions. Using measurements from the HIPPO and BARCA aircraft campaigns, we show that systematic errors in the SCIAMACHY measurements are a main factor limiting the performance of the inversions. To further constrain tropical emissions of methane using current and future satellite missions, extended validation capabilities in the tropics are of critical importance.
Improving operational flood forecasting using data assimilation
Rakovec, O. - \ 2014
Wageningen University. Promotor(en): Remko Uijlenhoet, co-promotor(en): Albrecht Weerts. - Wageningen : Wageningen University - ISBN 9789461738493
overstromingen - hoogwaterbeheersing - voorspellen - data-assimilatie - gegevensanalyse - hydrologie - modellen - floods - flood control - forecasting - data assimilation - data analysis - hydrology - models
Hoogwatervoorspellingssystemen die betrouwbaar en nauwkeurig overstromingen kunnen voorspellen zijn erg belangrijk, omdat dit het aantal slachtoffers en de economische schade van overstromingen kan beperken. Het begrijpen van het gedrag van extreme hydrologische gebeurtenissen en het vermogen van de modelleur om betere en nauwkeurigere prognoses te krijgen, zijn uitdagingen binnen de toegepaste hydrologie. Omdat modellen slechts een versimpelde weergave van de complexe werkelijkheid geven, kleven er aan hydrologische voorspellingen veel onzekerheden. Dit proefschrift draagt bij aan een verbeterd begrip en kwantificatie van hydrologische modelonzekerheid, vooral gekoppeld aan de initi¨ele condities van het model, en in mindere mate aan de modelstructuur en de parameters.
Predicting multiyear North Atlantic Ocean variability
Hazeleger, W. ; Wouters, B. ; Oldenborgh, G.J. van; Corti, S. ; Palmer, T. ; Lloyd Smith, D. ; Dunstone, N. ; Kroger, J. ; Pohlmann, H. ; Storch, J.S. von - \ 2013
Journal of Geophysical Research: Oceans 118 (2013)3. - ISSN 2169-9275 - p. 1087 - 1098.
meridional overturning circulation - coupled climate models - surface-temperature - physical parametrizations - multidecadal variability - decadal variability - data assimilation - labrador sea - ecmwf model - ec-earth
We assess the skill of retrospective multiyear forecasts of North Atlantic ocean characteristics obtained with ocean-atmosphere-sea ice models that are initialized with estimates from the observed ocean state. We show that these multimodel forecasts can skilfully predict surface and subsurface ocean variability with lead times of 2 to 9 years. We focus on assessment of forecasts of major well-observed oceanic phenomena that are thought to be related to the Atlantic meridional overturning circulation (AMOC). Variability in the North Atlantic subpolar gyre, in particular that associated with the Atlantic Multidecadal Oscillation, is skilfully predicted 2-9 years ahead. The fresh water content and heat content in major convection areas such as the Labrador Sea are predictable as well, although individual events are not captured. The skill of these predictions is higher than that of uninitialized coupled model simulations and damped persistence. However, except for heat content in the subpolar gyre, differences between damped persistence and the initialized predictions are not significant. Since atmospheric variability is not predictable on multiyear time scales, initialization of the ocean and oceanic processes likely provide skill. Assessment of relationships of patterns of variability and ocean heat content and fresh water content shows differences among models indicating that model improvement can lead to further improvements of the predictions. The results imply there is scope for skilful predictions of the AMOC.
Reassessing the variability in atmospheric H2 using the two-way nested TM5 model
Pieterse, G. ; Krol, M.C. ; Batenburg, A.M. ; Brenninkmeijer, C.A. ; Popa, M.E. ; O'Doherty, S. ; Grant, A. ; Steele, L.P. ; Krummel, P.B. ; Langenfelds, R.L. ; Wang, H.J. ; Vermeulen, A.T. ; Schmidt, M. ; Yver, C. ; Jordan, A. ; Engel, A. ; Fisher, R.E. ; Lowry, D. ; Nisbet, E.G. ; Reimann, S. ; Vollmer, M.K. ; Steinbacher, M. ; Hammer, S. ; Forster, G. ; Sturges, W.T. ; Rockmann, T. - \ 2013
Journal of Geophysical Research: Atmospheres 118 (2013)9. - ISSN 2169-897X - p. 3764 - 3780.
dry deposition parameterization - stable isotopic composition - general-circulation model - global hydrogen economy - molecular-hydrogen - trace gases - environmental-impact - dissolved hydrogen - seasonal-variation - data assimilation
This work reassesses the global atmospheric budget of H2 with the TM5 model. The recent adjustment of the calibration scale for H2 translates into a change in the tropospheric burden. Furthermore, the ECMWF Reanalysis-Interim (ERA-Interim) data from the European Centre for Medium-Range Weather Forecasts (ECMWF) used in this study show slower vertical transport than the operational data used before. Consequently, more H2 is removed by deposition. The deposition parametrization is updated because significant deposition fluxes for snow, water, and vegetation surfaces were calculated in our previous study. Timescales of 1-2h are asserted for the transport of H2 through the canopies of densely vegetated regions. The global scale variability of H2 and [DH2] is well represented by the updated model. H2 is slightly overestimated in the Southern Hemisphere because too little H2 is removed by dry deposition to rainforests and savannahs. The variability in H2 over Europe is further investigated using a high-resolution model subdomain. It is shown that discrepancies between the model and the observations are mainly caused by the finite model resolution. The tropospheric burden is estimated at 165 +/- 8TgH2. The removal rates of H2 by deposition and photochemical oxidation are estimated at 53 +/- 4 and 23 +/- 2TgH2/yr, resulting in a tropospheric lifetime of 2.2 +/- 0.2year.
Real-time multi-model decadal climate predictions
Smith, D.M. ; Scaife, A.A. ; Boer, G.J. ; Caian, M. ; Doblas-Reyes, F.J. ; Guemas, V. ; Hawkins, E. ; Hazeleger, W. ; Hermanson, L. ; Ho, C.K. ; Ishii, M. ; Kharin, V. ; Kimoto, M. ; Kirtman, B. ; Lean, J. ; Matei, D. ; Merryfield, W.J. ; Muller, W.A. ; Pohlmann, H. ; Rosati, A. ; Wouters, B. ; Wyser, K. - \ 2013
Climate Dynamics 41 (2013)11-12. - ISSN 0930-7575 - p. 2875 - 2888.
surface-temperature - data assimilation - atlantic hurricanes - north-american - ensemble - model - design
We present the first climate prediction of the coming decade made with multiple models, initialized with prior observations. This prediction accrues from an international activity to exchange decadal predictions in near real-time, in order to assess differences and similarities, provide a consensus view to prevent over-confidence in forecasts from any single model, and establish current collective capability. We stress that the forecast is experimental, since the skill of the multi-model system is as yet unknown. Nevertheless, the forecast systems used here are based on models that have undergone rigorous evaluation and individually have been evaluated for forecast skill. Moreover, it is important to publish forecasts to enable open evaluation, and to provide a focus on climate change in the coming decade. Initialized forecasts of the year 2011 agree well with observations, with a pattern correlation of 0.62 compared to 0.31 for uninitialized projections. In particular, the forecast correctly predicted La Nia in the Pacific, and warm conditions in the north Atlantic and USA. A similar pattern is predicted for 2012 but with a weaker La Nia. Indices of Atlantic multi-decadal variability and Pacific decadal variability show no signal beyond climatology after 2015, while temperature in the Nio3 region is predicted to warm slightly by about 0.5 A degrees C over the coming decade. However, uncertainties are large for individual years and initialization has little impact beyond the first 4 years in most regions. Relative to uninitialized forecasts, initialized forecasts are significantly warmer in the north Atlantic sub-polar gyre and cooler in the north Pacific throughout the decade. They are also significantly cooler in the global average and over most land and ocean regions out to several years ahead. However, in the absence of volcanic eruptions, global temperature is predicted to continue to rise, with each year from 2013 onwards having a 50 % chance of exceeding the current observed record. Verification of these forecasts will provide an important opportunity to test the performance of models and our understanding and knowledge of the drivers of climate change.
Impact of transport model errors on the global and regional methane emissions estimated by inverse modelling
Locatelli, R. ; Bousquet, P. ; Chevallier, F. ; Fortems-Cheney, A. ; Szopa, S. ; Saunois, M. ; Agusti-Panareda, A. ; Bergmann, D. ; Bian, H. ; Cameron-Smith, P. ; Chipperfield, M.P. ; Gloor, E. ; Houweling, S. ; Kawa, S.R. ; Krol, M.C. ; Patra, P.K. ; Prinn, R.G. ; Rigby, M. ; Saito, R. ; Wilson, C. - \ 2013
Atmospheric Chemistry and Physics 13 (2013)19. - ISSN 1680-7316 - p. 9917 - 9937.
general-circulation model - atmospheric transport - tracer transport - co2 inversions - boundary-layer - vertical profiles - data assimilation - climate-change - growth-rate - part i
A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Meteorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System) inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure) is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr(-1) at the global scale, representing 5% of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr(-1) in North America to 7 Tg yr(-1) in Boreal Eurasia (from 23 to 48 %, respectively). At the model grid-scale, the spread of inverse estimates can reach 150% of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly question the consistency of transport model errors in current inverse systems. Future inversions should include more accurately prescribed observation covariances matrices in order to limit the impact of transport model errors on estimated methane fluxes.
Atmospheric CH4 in the first decade of the 21st century: Inverse modeling analysis using SCIAMACHY satellite retrievals and NOAA surface measurements
Bergamaschi, P. ; Houweling, S. ; Segers, A. ; Krol, M.C. ; Frankenberg, C. ; Scheepmaker, R.A. ; Dlugokencky, E. ; Wofsy, S.C. ; Kort, E.A. ; Sweeney, C. ; Schuck, T. ; Brenninkmeijer, C. ; Chen, H. ; Beck, V. ; Gerbig, C. - \ 2013
Journal of Geophysical Research: Atmospheres 118 (2013)13. - ISSN 2169-897X - p. 7350 - 7369.
growth-rate - methane emissions - carbon-dioxide - northern-hemisphere - data assimilation - transport model - variability - chemistry - climate - troposphere
The causes of renewed growth in the atmospheric CH4 burden since 2007 are still poorly understood and subject of intensive scientific discussion. We present a reanalysis of global CH4 emissions during the 2000s, based on the TM5-4DVAR inverse modeling system. The model is optimized using high-accuracy surface observations from NOAA ESRL's global air sampling network for 2000-2010 combined with retrievals of column-averaged CH4 mole fractions from SCIAMACHY onboard ENVISAT (starting 2003). Using climatological OH fields, derived global total emissions for 2007-2010 are 16-20 Tg CH4/yr higher compared to 2003-2005. Most of the inferred emission increase was located in the tropics (9-14 Tg CH4/yr) and mid- latitudes of the northern hemisphere (6-8 Tg CH4/yr), while no significant trend was derived for Arctic latitudes. The atmospheric increase can be attributed mainly to increased anthropogenic emissions, but the derived trend is significantly smaller than estimated in the EDGARv4.2 emission inventory. Superimposed on the increasing trend in anthropogenic CH4 emissions are significant inter-annual variations (IAV) of emissions from wetlands (up to +/- 10 Tg CH4/yr), and biomass burning (up to +/- 7 Tg CH4/yr). Sensitivity experiments, which investigated the impact of the SCIAMACHY observations (versus inversions using only surface observations), of the OH fields used, and of a priori emission inventories, resulted in differences in the detailed latitudinal attribution of CH4 emissions, but the IAV and trends aggregated over larger latitude bands were reasonably robust. All sensitivity experiments show similar performance against independent shipboard and airborne observations used for validation, except over Amazonia where satellite retrievals improved agreement with observations in the free troposphere.
State updating of a distributed hydrological model with Ensemble Kalman Filtering: effects of updating frequency and observation network density on forecast accuracy
Rakovec, O. ; Weerts, A.H. ; Hazenberg, P. ; Torfs, P.J.J.F. ; Uijlenhoet, R. - \ 2012
Hydrology and Earth System Sciences 16 (2012)9. - ISSN 1027-5606 - p. 3435 - 3449.
rainfall-runoff model - sensed soil-moisture - data assimilation - discharge predictions - ourthe catchment - improvement - streamflow - system - region - scale
This paper presents a study on the optimal setup for discharge assimilation within a spatially distributed hydrological model. The Ensemble Kalman filter (EnKF) is employed to update the grid-based distributed states of such an hourly spatially distributed version of the HBV-96 model. By using a physically based model for the routing, the time delay and attenuation are modelled more realistically. The discharge and states at a given time step are assumed to be dependent on the previous time step only (Markov property). Synthetic and real world experiments are carried out for the Upper Ourthe (1600 km(2)), a relatively quickly responding catchment in the Belgian Ardennes. We assess the impact on the forecasted discharge of (1) various sets of the spatially distributed discharge gauges and (2) the filtering frequency. The results show that the hydrological forecast at the catchment outlet is improved by assimilating interior gauges. This augmentation of the observation vector improves the forecast more than increasing the updating frequency. In terms of the model states, the EnKF procedure is found to mainly change the pdfs of the two routing model storages, even when the uncertainty in the discharge simulations is smaller than the defined observation uncertainty.
Precipitation measurement at CESAR, the Netherlands
Leijnse, H. ; Uijlenhoet, R. ; Beek, C.Z. van de; Overeem, A. ; Otto, T. ; Unal, C.M.H. ; Dufournet, Y. ; Russchenberg, H.W. ; Figueras i Ventura, J. ; Klein Baltink, H. ; Holleman, I. - \ 2010
Journal of Hydrometeorology 11 (2010)6. - ISSN 1525-755X - p. 1322 - 1329.
neerslag - meettechnieken - weersvoorspelling - data-assimilatie - nederland - precipitation - measurement techniques - weather forecasting - data assimilation - netherlands - polarization spectral-analysis - dual-frequency retrieval - x-band frequencies - radar measurements - rainfall measurements - part i - reflectivity - resolution - rates - doppler
The Cabauw Experimental Site for Atmospheric Research (CESAR) observatory hosts a unique collection of instruments related to precipitation measurement. The data collected by these instruments are stored in a database that is freely accessible through a Web interface. The instruments present at the CESAR site include three disdrometers (two on the ground and one at 200 mabove ground level), a dense network of rain gauges, three profiling radars (1.3, 3.3, and 35 GHz), and an X-band Doppler polarimetric scanning radar. In addition to these instruments, operational weather radar data from the nearby (;25 km) De Bilt C-band Doppler radar are also available. The richness of the datasets available is illustrated for a rainfall event, where the synergy of the different instruments provides insight into precipitation at multiple spatial and temporal scales. These datasets, which are freely available to the scientific community, can contribute greatly to our understanding of precipitation-related atmospheric and hydrologic processes.
Parameter Sensitivity in LSMs: An Analysis Using Stochastic Soil Moisture Models and ELDAS Soil Parameters
Teuling, A.J. ; Uijlenhoet, R. ; Hurk, B. van den; Seneviratne, S.I. - \ 2009
Journal of Hydrometeorology 10 (2009)3. - ISSN 1525-755X - p. 751 - 765.
land-surface scheme - data assimilation - hydraulic-properties - level parameters - water-balance - climate - energy - simulations - vegetation - atmosphere
Integration of simulated and observed states through data assimilation as well as model evaluation requires a realistic representation of soil moisture in land surface models (LSMs). However, soil moisture in LSMs is sensitive to a range of uncertain input parameters, and intermodel differences in parameter values are often large. Here, the effect of soil parameters on soil moisture and evapotranspiration are investigated by using parameters from three different LSMs participating in the European Land Data Assimilation System (ELDAS) project. To prevent compensating effects from other than soil parameters, the effects are evaluated within a common framework of parsimonious stochastic soil moisture models. First, soil parameters are shown to affect soil moisture more strongly than the average evapotranspiration. In arid climates, the effect of soil parameters is on the variance rather than the mean, and the intermodel flux differences are smallest. Soil parameters from the ELDAS LSMs differ strongly, most notably in the available moisture content between the wilting point and the critical moisture content, which differ by a factor of 3. The ELDAS parameters can lead to differences in mean volumetric soil moisture as high as 0.10 and an average evapotranspiration of 10%–20% for the investigated parameter range. The parsimonious framework presented here can be used to investigate first-order parameter sensitivities under a range of climate conditions without using full LSM simulations. The results are consistent with many other studies using different LSMs under a more limited range of possible forcing conditions
The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data
Fox, A. ; Williams, M. ; Richardson, A.D. ; Cameron, D. ; Gove, J.H. ; Quaife, T. ; Ricciuto, D. ; Reichstein, M. ; Tomelleri, E. ; Trudinger, C.M. ; Wijk, M.T. van - \ 2009
Agricultural and Forest Meteorology 149 (2009)10. - ISSN 0168-1923 - p. 1597 - 1615.
parameter-estimation - data assimilation - carbon-dioxide - uncertainty - climate - forest - productivity - variability - simulation - feedbacks
We describe a model-data fusion (MDF) inter-comparison project (REFLEX), which compared various algorithms for estimating carbon (C) model parameters consistent with both measured carbon fluxes and states and a simple C model. Participants were provided with the model and with both synthetic net ecosystem exchange (NEE) of CO2 and leaf area index (LAI) data, generated from the model with added noise, and observed NEE and LAI data from two eddy covariance sites. Participants endeavoured to estimate model parameters and states consistent with the model for all cases over the two years for which data were provided, and generate predictions for one additional year without observations. Nine participants contributed results using Metropolis algorithms, Kalman filters and a genetic algorithm. For the synthetic data case, parameter estimates compared well with the true values. The results of the analyses indicated that parameters linked directly to gross primary production (GPP) and ecosystem respiration, such as those related to foliage allocation and turnover, or temperature sensitivity of heterotrophic respiration, were best constrained and characterised. Poorly estimated parameters were those related to the allocation to and turnover of fine root/wood pools. Estimates of confidence intervals varied among algorithms, but several algorithms successfully located the true values of annual fluxes from synthetic experiments within relatively narrow 90% confidence intervals, achieving >80% success rate and mean NEE confidence intervals
Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation
Vrugt, J.A. ; Braak, C.J.F. ter; Clark, M.P. ; Hyman, J.M. ; Robinson, B.A. - \ 2008
Water Resources Research 44 (2008). - ISSN 0043-1397 - p. W00B09 - W00B09.
ensemble kalman filter - rainfall-runoff models - streamflow simulation - metropolis algorithm - parameter-estimation - bayesian-estimation - data assimilation - optimization - prediction - calibration
There is increasing consensus in the hydrologic literature that an appropriate framework for streamflow forecasting and simulation should include explicit recognition of forcing and parameter and model structural error. This paper presents a novel Markov chain Monte Carlo (MCMC) sampler, entitled differential evolution adaptive Metropolis (DREAM), that is especially designed to efficiently estimate the posterior probability density function of hydrologic model parameters in complex, high-dimensional sampling problems. This MCMC scheme adaptively updates the scale and orientation of the proposal distribution during sampling and maintains detailed balance and ergodicity. It is then demonstrated how DREAM can be used to analyze forcing data error during watershed model calibration using a five-parameter rainfall-runoff model with streamflow data from two different catchments. Explicit treatment of precipitation error during hydrologic model calibration not only results in prediction uncertainty bounds that are more appropriate but also significantly alters the posterior distribution of the watershed model parameters. This has significant implications for regionalization studies. The approach also provides important new ways to estimate areal average watershed precipitation, information that is of utmost importance for testing hydrologic theory, diagnosing structural errors in models, and appropriately benchmarking rainfall measurement devices.
Carbon flux bias estimation employing Maximum Likelihood Ensemble Filter (MLEF)
Zupanski, D. ; Denning, A.S. ; Uliasz, M. ; Zupanski, M. ; Schuh, A.E. ; Rayner, P.J. ; Peters, W. ; Corbin, K.D. - \ 2007
Journal of Geophysical Research: Atmospheres 112 (2007). - ISSN 2169-897X - 18 p.
data assimilation - kalman filter - variational analysis - theoretical aspects - atmospheric co2 - part i - model - transport
We evaluate the capability of an ensemble based data assimilation approach, referred to as Maximum Likelihood Ensemble Filter (MLEF), to estimate biases in the CO2 photosynthesis and respiration fluxes. We employ an off-line Lagrangian Particle Dispersion Model (LPDM), which is driven by the carbon fluxes, obtained from the Simple Biosphere - Regional Atmospheric Modeling System (SiB-RAMS). The SiB-RAMS carbon fluxes are assumed to have errors in the form of multiplicative biases. Our goal is to estimate and reduce these biases and also to assign reliable posterior uncertainties to the estimated biases. Experiments of this study are performed using simulated CO2 observations, which resemble real CO2 concentrations from the Ring of Towers in northern Wisconsin. We evaluate the MLEF results with respect to the 'truth' and the Kalman Filter (KF) solution. The KF solution is considered theoretically optimal for the problem of this study, which is a linear data assimilation problem involving Gaussian errors. We also evaluate the impact of forecast error covariance localization based on a new 'distance' defined in the space of information measures. Experimental results are encouraging, indicating that the MLEF can successfully estimate carbon flux biases and their uncertainties. As expected, the estimated biases are closer to the 'true' biases in the experiments with more ensemble members and more observations. The data assimilation algorithm has a stable performance and converges smoothly to the KF solution when the ensemble size approaches the size of the model state vector (i.e., the control variable of the data assimilation problem)
Spectrodirectional Remote Sensing: From Pixels to Processes
Schaepman, M.E. - \ 2007
International Journal of applied Earth Observation and Geoinformation 9 (2007)2. - ISSN 0303-2434 - p. 204 - 223.
radiative-transfer models - net primary productivity - imaging spectrometer aviris - comparing global-models - leaf-area index - terrestrial ecosystem - multiangular measurements - spectral reflectance - forest ecosystems - data assimilation
This paper discusses the historical evolution of imaging spectroscopy in Earth observation as well as directional (or multiangular) research leading to current achievements in spectrodirectional remote sensing. It elaborates on the evolution from two separate research areas into a common approach to quantify the interaction of light with the Earth surface. The contribution of spectrodirectional remote sensing towards an improved understanding of the Earth System is given by discussing the benefits of converging from individual pixel analysis to process models in the land-biosphere domain. The paper concludes with an outlook of research focus and upcoming areas of interest emphasizing towards multidisciplinary approaches using integrated system solutions based on remote and in situ sensing, data assimilation, and state space estimation algorithms.