Showing papers by "Gianpaolo Balsamo published in 2020"

Energy, environment and sustainable development of the belt and road initiative: The Chinese scenario and Western contributions

Abstract: The Belt and Road Initiative has tremendously increased the interaction of China with the countries involved, pushing forward the integration and comparative phase, based on the main factors affecting the energy, environmental and development scenarios. The indicators of this process are strictly related to the environmental sustainability of projects and infrastructural initiatives which entail aspects regarding climate change, environmental impact, transport management, urbanization and effective utilization of energy. Through this osmotic program, which the Chinese government said in 2013 would be executed from east to west, the contributions Western countries may make to maximize the common efforts are foreseeably very important for the success of the whole BRI and, indirectly, for the harmonization of the very rapid Chinese growth. China held the first position in 2014 for electricity generation (5388 billion kwh/h) and coal production (4.27 billion short tons/year), as well as the second position for petroleum consumption. On the other hand, carbon dioxide emissions were 1.8 times those of the USA in 2015, and transport, urbanization and energy intensity still struggle to attain optimal levels. The quality of productive sectors, research and universities is still low in the world ranking, despite the huge efforts of the Chinese, due to a still slow and cumbersome internationalization process. This article aims to integrate the numerous excellent studies published in the last few years on sustainable development and energy effectiveness within the BRI (Table 1 provides some specific references), by carrying out a systematic analysis of the Chinese energy, environmental and sustainable landscape from a Western perspective, breaking the main areas of the Chinese scenario down into its components and identifying those where the contributions of Western countries can support the gaps coverage. In this respect, the change of viewpoint provided by this study may be beneficial to properly balance the BRI perspective along the east-west axis.

Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces

Abstract: . This study demonstrates that LDAS-Monde, a global and offline Land Data Assimilation System (LDAS), that integrates satellite Earth observations into the ISBA (Interaction between Soil Biosphere and Atmosphere) Land Surface Model (LSM), is able to detect, monitor and forecast the impact of extreme weather on land surface states. LDAS-Monde jointly assimilates satellite derived Earth observations of surface soil moisture (SSM) and Leaf Area Index (LAI). It is run at global scale forced by ERA5 (LDAS_ERA5), the latest atmospheric reanalysis from the European Centre for Medium Range Weather Forecast (ECMWF) over 2010–2018 leading to a 9-yr, ~ 0.25° × 0.25° spatial resolution reanalysis of Land Surface Variables (LSVs). This reanalysis is then used to compute anomalies of land surface states, in order to (i) detect regions exposed to extreme weather such as droughts and heatwave events and (ii) address specific monitoring and forecasting requirements of LSVs for those regions. In this study, LDAS_ERA5 analysis is first successfully evaluated worldwide using several satellite-based datasets (SSM, LAI, Evapotranspiration, Gross Primary Production and Sun Induced Fluorescence), as well as in situ measurements (SSM, evapotranspiration and river discharge). The added value of assimilating the soil moisture and LAI is demonstrated with respect to a model simulation (openloop, with no assimilation). Since the global LDAS_ERA5 has relatively coarse resolution, two higher spatial resolution experiments over two areas particularly affected by heatwaves and/or droughts in 2018 were run: North Western Europe and the Murray-Darling basin in South Eastern Australia. These experiments were forced with ECMWF Integrated Forecasting System (IFS) high resolution operational analysis (LDAS_HRES, ~ 0.10° × 0.10° spatial resolution) over 2017–2018, and both openloop and analysis experiments compared once again. Since the IFS is a forecast system, it also allows LDAS-Monde to be used in forecast mode, and we demonstrate the added value of initializing 4- and 8-day LDAS-HRES forecasts of the LSVs, from the LDAS-HRES assimilation run, compared to the openloop experiments. This is particularly true for LAI that evolves on longer time space than SSM and is more sensitive to initial conditions than to atmospheric forcing, even at an 8-day lead time. This confirms that slowly evolving land initial conditions are paramount for forecasting LSVs and that LDAS-systems should jointly analyse both soil moisture and vegetation states. Finally evaluation of the modelled snowpack is presented and the perspectives for snow data assimilation in LDAS-Monde are discussed.

Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments

Abstract: . Snow models are usually evaluated at sites providing high-quality meteorological data, so that the uncertainty in the meteorological input data can be neglected when assessing model performances. However, high-quality input data are rarely available in mountain areas and, in practical applications, the meteorological forcing used to drive snow models is typically derived from spatial interpolation of the available in situ data or from reanalyses, whose accuracy can be considerably lower. In order to fully characterize the performances of a snow model, the model sensitivity to errors in the input data should be quantified. In this study we test the ability of six snow models to reproduce snow water equivalent, snow density and snow depth when they are forced by meteorological input data with gradually lower accuracy. The SNOWPACK, GEOTOP, HTESSEL, UTOPIA, SMASH and S3M snow models are forced, first, with high-quality measurements performed at the experimental site of Torgnon, located at 2160 m a.s.l. in the Italian Alps (control run). Then, the models are forced by data at gradually lower temporal and/or spatial resolution, obtained by (i) sampling the original Torgnon 30 min time series at 3, 6, and 12 h, (ii) spatially interpolating neighbouring in situ station measurements and (iii) extracting information from GLDAS, ERA5 and ERA-Interim reanalyses at the grid point closest to the Torgnon site. Since the selected models are characterized by different degrees of complexity, from highly sophisticated multi-layer snow models to simple, empirical, single-layer snow schemes, we also discuss the results of these experiments in relation to the model complexity. The results show that, when forced by accurate 30 min resolution weather station data, the single-layer, intermediate-complexity snow models HTESSEL and UTOPIA provide similar skills to the more sophisticated multi-layer model SNOWPACK, and these three models show better agreement with observations and more robust performances over different seasons compared to the lower-complexity models SMASH and S3M. All models forced by 3-hourly data provide similar skills to the control run, while the use of 6- and 12-hourly temporal resolution forcings may lead to a reduction in model performances if the incoming shortwave radiation is not properly represented. The SMASH model generally shows low sensitivity to the temporal degradation of the input data. Spatially interpolated data from neighbouring stations and reanalyses are found to be adequate forcings, provided that temperature and precipitation variables are not affected by large biases over the considered period. However, a simple bias-adjustment technique applied to ERA-Interim temperatures allowed all models to achieve similar performances to the control run. Regardless of their complexity, all models show weaknesses in the representation of the snow density.

Representing model uncertainty for global atmospheric CO 2 flux inversions using ECMWF-IFS-46R1

Abstract: . Atmospheric flux inversions use observations of atmospheric CO2 to provide anthropogenic and biogenic CO2 flux estimates at a range of spatio-temporal scales. Inversions require prior flux, a forward model and observation errors to estimate posterior fluxes and uncertainties. Here, we investigate the forward transport error and the associated biogenic feedback in an Earth system model (ESM) context. These errors can occur from uncertainty in the initial meteorology, the analysis fields used, or the advection schemes and physical parameterisation of the model. We also explore the spatio-temporal variability and flow-dependent error covariances. We then compare the error with the atmospheric response to uncertainty in the prior anthropogenic emissions. Although transport errors are variable, average total-column CO2 ( XCO2 ) transport errors over anthropogenic emission hotspots (0.1–0.8 ppm) are comparable to, and often exceed, prior monthly anthropogenic flux uncertainties projected onto the same space (0.1–1.4 ppm). Average near-surface transport errors at three sites (Paris, Caltech and Tsukuba) range from 1.7 to 7.2 ppm. The global average XCO2 transport error standard deviation plateaus at ∼0.1 ppm after 2–3 d, after which atmospheric mixing significantly dampens the concentration gradients. Error correlations are found to be highly flow dependent, with XCO2 spatio-temporal correlation length scales ranging from 0 to 700 km and 0 to 260 min. Globally, the average model error caused by the biogenic response to atmospheric meteorological uncertainties is small ( ppm); however, this increases over high flux regions and is seasonally dependent (e.g. the Amazon; January and July: 0.24±0.18 ppm and 0.13±0.07 ppm). In general, flux hotspots are well-correlated with model transport errors. Our model error estimates, combined with the atmospheric response to anthropogenic flux uncertainty, are validated against three Total Carbon Observing Network (TCCON) XCO2 sites. Results indicate that our model and flux uncertainty account for 21 %–65 % of the total uncertainty. The remaining uncertainty originates from additional sources, such as observation, numerical and representation errors, as well as structural errors in the biogenic model. An underrepresentation of transport and flux uncertainties could also contribute to the remaining uncertainty. Our quantification of CO2 transport error can be used to help derive accurate posterior fluxes and error reductions in future inversion systems. The model uncertainty diagnosed here can be used with varying degrees of complexity and with different modelling techniques by the inversion community.

Varying snow and vegetation signatures of surface-albedo feedback on the Northern Hemisphere land warming

Abstract: Changes in snow and vegetation cover associated with global warming can modify surface albedo (the reflected amount of radiative energy from the sun), therefore modulating the rise of surface temperature that is primarily caused by anthropogenic greenhouse-gases emission. This introduces a series of potential feedbacks to regional warming with positive (negative) feedbacks enhancing (reducing) temperature increase by augmenting (decreasing) the absorption of short-wave radiation. So far our knowledge on the importance and magnitude of these feedbacks has been hampered by the limited availability of relatively long records of continuous satellite observations. Here we exploit a 31 year (1982–2012) high-frequency observational record of land data to quantify the strength of the surface-albedo feedback on land warming modulated by snow and vegetation during the recent historical period. To distinguish snow and vegetation contributions to this feedback, we examine temporal composites of satellite data in three different Northern Hemisphere domains. The analysis reveals and quantifies markedly different signatures of the surface-albedo feedback. A large positive surface-albedo feedback of +0.87 (CI 95%: 0.68, 1.05) W(m2⋅K)−1 absorbed solar radiation per degree of temperature increase is estimated in the domain where snow dominates. On the other hand the surface-albedo feedback becomes predominantly negative where vegetation dominates: it is largely negative (−0.91 (−0.81, −1.03) W(m2⋅K)−1 ) in the domain with vegetation dominating, while it is moderately negative (−0.57 (−0.40, −0.72) W(m2⋅K)−1 ) where both vegetation and snow are significantly present. Snow cover reduction consistently provides a positive feedback on warming. In contrast, vegetation expansion can produce either positive or negative feedbacks in different regions and seasons, depending on whether the underlying surface being replaced has higher (e.g. snow) or lower (e.g. dark soils) albedo than vegetation. This work provides fundamental knowledge to model and predict how the surface-albedo feedback will evolve and affect the rate of regional temperature rise in the future.

Measuring the Impact of a New Snow Model Using Surface Energy Budget Process Relationships

Abstract: Energy exchange at the snow-atmosphere interface in winter is important for the evolution of temperature at the surface and within the snow, preconditioning the snowpack for melt during spring. Thi...

Sensitivity of surface fluxes in the ECMWF land surface model to the remotely sensed leaf area index and root distribution: Evaluation with tower flux data

Abstract: The surface-atmosphere turbulent exchanges couple the water, energy and carbon budgets in the Earth system. The biosphere plays an important role in the evaporation process, and vegetation related parameters such as the leaf area index (LAI), vertical root distribution and stomatal resistance are poorly constrained due to sparse observations at the spatio-temporal scales at which land surface models (LSMs) operate. In this study, we use the Carbon Hydrology Tiled European Center for Medium-Range Weather Forecasts (ECMWF) Scheme for Surface Exchanges over Land (CHTESSEL) model and investigate the sensitivity of the simulated turbulent fluxes to these vegetation related parameters. Observed data from 17 FLUXNET towers were used to force and evaluate model simulations with different vegetation parameter configurations. The replacement of the current LAI climatology used by CHTESSEL, by a new high-resolution climatology, representative of the station’s location, has a small impact on the simulated fluxes. Instead, a revision of the root profile considering a uniform root distribution reduces the underestimation of evaporation during water stress conditions. Despite the limitations of using only one model and a limited number of stations, our results highlight the relevance of root distribution in controlling soil moisture stress, which is likely to be applicable to other LSMs.

Systematic detection of local CH 4 emissions anomalies combining satellite measurements and high-resolution forecasts

Abstract: . In this study we present a novel monitoring methodology to detect local CH4 concentration anomalies worldwide that are related to rapidly changing anthropogenic emissions that significantly contribute to the CH4 atmospheric budget. The method uses high resolution (7 km × 7 km) retrievals of total column CH4 from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel 5 Precursor satellite. Observations are combined with high resolution CH4 forecasts (~ 9 km) produced by the Copernicus Atmosphere Monitoring Service (CAMS) to provide departures (observations minus forecasts) close to the native satellite resolution at appropriate time. Investigating the departures is an effective way to link satellite measurements and emission inventory data in a quantitative manner. We perform filtering on the departures to remove the large-scale biases on both forecasts and satellite observations. We then use a simple classification on the filtered departures to detect anomalies and plumes coming from CAMS emissions that are missing (e.g. pipeline or facility leaks), under-reported or over-reported (e.g. depleted drilling fields). Additionally, the classification helps to detect local satellite retrieval errors due to land surface albedo issues.

Anthropogenic CO2 emission uncertainties

Abstract: The CO2 Human Emissions (CHE) project has been tasked by the European Commission to prepare the development of a European capacity to monitor anthropogenic CO2 emissions. The monitoring of fossil fuel CO2 emissions has to come with a sufficiently low uncertainty in order to be useful for policymakers. In this context, the main approaches to estimate fossil fuel emissions, apart from bottom-up inventories, are based on inverse transport modeling either on its own or within a coupled carbon cycle fossil fuel data assimilation system. Both approaches make use of atmospheric CO2 and other tracers (e.g., CO and NOx) and rely on the availability of prior fossil fuel CO2 emission estimates and uncertainties (as well as biogenic fluxes for the transport inverse modeling). For a robust estimate of the uncertainty, information from different sources needs to be brought together. A methodology to calculate yearly and monthly anthropogenic CO2 emission uncertainties based on IPCC guidelines (2006 IPCC Guidelines for National Greenhouse Gas Inventories + its 2019 Refinements) has been developed. Emission uncertainties are calculated for all world countries, under the assumption of two categories of world countries, depending on whether the country’s statistical infrastructure is well or less developed. For well-developed statistical infrastructure, emission uncertainties are lower, while less developed statistical infrastructure countries have higher emission uncertainties. A sensitivity analysis is investigating the impact of the well or less developed infrastructure assumption for several countries on the global emission uncertainty. Sensitivity experiments with different anthropogenic CO2 sources distributions, as well as the first results on using these prior anthropogenic CO2 uncertainties in ensemble perturbation runs will be presented.

Improvement of the simulation of the water and energy cycle using Multiscale Parameter Regionalization (MPR)

Abstract: The representation of the water and energy cycle in environmental models is closely linked to the parameter values used in the process parametrizations. The dimension of the parameter space in spatially distributed environmental models corresponds to the number of grid cells multiplied by the number of parameters per grid cell. For large-scale simulations on national and continental scales, the dimensionality of the parameter space is too high for efficient parameter estimation using inverse estimation methods. A regularization of the parameter space is necessary to reduce its dimensionality. The Multiscale Parameter Regionalization (MPR) is one approach to achieve this.