ESA Climate Change Initiative Research Articles

Ensemble Downscaling of Soil Moisture Data Using BMA and ATPRK

Soil moisture is essential information for meteorological and hydrological analyses. To date, many efforts have been made to achieve the two goals for soil moisture data, i.e., the improvement of accuracy and resolution, which is very challenging. We presented an ensemble downscaling method for quality improvement of gridded soil moisture data in terms of the accuracy and the spatial resolution by the integration of BMA (Bayesian model averaging) and ATPRK (area-to-point regression kriging). In the experiments, the BMA ensemble showed a 22% better accuracy than the data sets from ESA CCI (European Space Agency-Climate Change Initiative), ERA5 (ECMWF Reanalysis 5), and GLDAS (Global Land Data Assimilation System) in terms of RMSE (root mean square error). Also, the ATPRK downscaling could enhance the spatial resolution from 0.25o to 0.05o while preserving the improved accuracy and the spatial pattern of the BMA ensemble, without under- or over-estimation. The quality-improved data sets can contribute to a variety of local and regional applications related to soil moisture, such as agriculture, forest, hydrology, and meteorology. Because the ensemble downscaling method can be applied to the other land surface variables such as temperature, humidity, precipitation, and evapotranspiration, it can be a viable option to complement the accuracy and the spatial resolution of satellite images and numerical models.

The Sea State CCI dataset v1: towards a sea state climate data record based on satellite observations

Abstract. Sea state data are of major importance for climate studies, marine engineering, safety at sea and coastal management. However, long-term sea state datasets are sparse and not always consistent, and sea state data users still mostly rely on numerical wave models for research and engineering applications. Facing the urgent need for a sea state climate data record, the Global Climate Observing System has listed “Sea State” as an Essential Climate Variable (ECV), fostering the launch in 2018 of the Sea State Climate Change Initiative (CCI). The CCI is a programme of the European Space Agency, whose objective is to realise the full potential of global Earth observation archives established by ESA and its member states in order to contribute to the ECV database. This paper presents the implementation of the first release of the Sea State CCI dataset, the implementation and benefits of a high-level denoising method, its validation against in situ measurements and numerical model outputs, and the future developments considered within the Sea State CCI project. The Sea State CCI dataset v1 is freely available on the ESA CCI website (http://cci.esa.int/data, last access: 25 August 2020) at ftp://anon-ftp.ceda.ac.uk/neodc/esacci/sea_state/data/v1.1_release/ (last access: 25 August 2020). Three products are available: a multi-mission along-track L2P product (http://dx.doi.org/10.5285/f91cd3ee7b6243d5b7d41b9beaf397e1, Piollé et al., 2020a), a daily merged multi mission along-track L3 product (http://dx.doi.org/10.5285/3ef6a5a66e9947d39b356251909dc12b, Piollé et al., 2020b) and a multi-mission monthly gridded L4 product (http://dx.doi.org/10.5285/47140d618dcc40309e1edbca7e773478, Piollé et al., 2020c).

Soil moisture evaluation over the Argentine Pampas using models, satellite estimations and in-situ measurements

Study regionThe Pampas region is located in the central-east part of Argentina, and is one of the most productive agricultural regions of the world under rainfed conditions. Study focusThis study aims at examining how different Land Surface Models (LSMs) and satellite estimations reproduce daily surface and root zone soil moisture variability over 8 in-situ observation sites. The ability of the LSMs to detect dry and wet events is also evaluated. New hydrological insights for the regionThe surface and root zone soil moisture of the LSMs and the surface soil moisture of the ESA CCI (European Space Agency Climate Change Initiative, hereafter ESA-SM) show in general a good performance against the in-situ measurements. In particular, the BHOA (Balance Hidrológico Operativo para el Agro) shows the best representation of the soil moisture dynamic range and variability, and the GLDAS (Global Land Data Assimilation System)-Noah, ERA-Interim TESSEL (Tiled ECMWF’s Scheme for Surface Exchanges over Land) and Global Drought Observatory (GDO)-LISFLOOD are able to adequately represent the soil moisture anomalies over the Pampas region. In addition to the LSM results, also the ESA-SM satellite estimated anomalies proved to be valuable. However, the LSMs and the ESA-SM have difficulties in reproducing the soil moisture frequency distributions. Based on this study, it is clear that accurate forcing data and soil parameters are critical to substantially improve the ability of LSMs to detect dry and wet events.

Assessing the Agreement between Deforestation Maps of Kalimantan from Various Sources

Due to its multiscale impacts, deforestation of tropical rainforests had become a global concern. A number of stakeholders comprising government, research agencies, and NGOs; ranging from local to international levels; have developed their own forest monitoring systems for detecting forest loss. However, discrepancies on deforestation reports from various producers often trigger public debates; which mostly degenerate the productivity of efforts in providing salient, legitimate and credible data on deforestation. Thus, we should reconcile the dispute by acknowledging the deforestation data from all sources. This study assessed the agreement between deforestation maps from various sources. In this case, deforestation maps of Kalimantan within 2009-2013 period from 4 sources were used; i.e. deforestation maps from European Space Agency - Climate Change Initiative (ESA-CCI), Forest Watch Indonesia (FWI), Global Forest Watch (GFW), and Indonesian Ministry of Environment and Forestry (MoEF). We found that the inter-rater agreement between deforestation maps were relatively low, as indicated by Cohen’s kappa (κ), ranging from slight (κ=0.18 between ESA-CCI and GFW) to fair (0.24 ≤ κ ≤ 0.35 for other pairs of sources); due to omission/commission disagreements (47.82% to 87.58%). It suggests that in order to reconcile the dispute, we should remove the omission disagreement by forming the union of deforestation maps. The results from further analyses proved that the union of deforestation maps increased the agreement to moderate (κ=0.44 between union map and FWI) and even substantial (κ=0.79 between union map and GFW). Findings of this study should support the implementation of one map policy.

Assessment of Remotely Sensed and Modelled Soil Moisture Data Products in the U.S. Southern Great Plains

Soil moisture (SM) plays a crucial role in the water and energy flux exchange between the atmosphere and the land surface. Remote sensing and modeling are two main approaches to obtain SM over a large-scale area. However, there is a big difference between them due to algorithm, spatial-temporal resolution, observation depth and measurement uncertainties. In this study, an assessment of the comparison of two state-of-the-art remotely sensed SM products, Soil Moisture Active Passive (SMAP) and European Space Agency Climate Change Initiative (ESACCI), and one land surface modeled dataset from the North American Land Data Assimilation System project phase 2 (NLDAS-2), were conducted using 17 permanent SM observation sites located in the Southern Great Plains (SGP) in the U.S. We first compared the daily mean SM of three products with in-situ measurements; then, we decompose the raw time series into a short-term seasonal part and anomaly by using a moving smooth window (35 days). In addition, we calculate the daily spatial difference between three products based on in-situ data and assess their temporal evolution. The results demonstrate that (1) in terms of temporal correlation R, the SMAP (R = 0.78) outperforms ESACCI (R = 0.62) and NLDAS-2 (R = 0.72) overall; (2) for the seasonal component, the correlation R of SMAP still outperforms the other two products, and the correlation R of ESACCI and NLDAS-2 have not improved like the SMAP; as for anomaly, there is no difference between the remotely sensed and modeling data, which implies the potential for the satellite products to capture the variations of short-term rainfall events; (3) the distribution pattern of spatial bias is different between the three products. For NLDAS-2, it is strongly dependent on precipitation; meanwhile, the spatial distribution of bias represents less correlation with the precipitation for two remotely sensed products, especially for the SMAP. Overall, the SMAP was superior to the other two products, especially when the SM was of low value. The difference between the remotely sensed and modeling products with respect to the vegetation type might be an important reason for the errors.

Evaluation of ESACCI satellite soil moisture product using in-situ CTCZ observations over India

Recently available European Space Agency Climate Change Initiative (ESACCI) soil moisture dataset, derived by merging soil moisture values calculated using measurements from satellite-based active and passive sensors, is validated over the Indian region using in-situ observations from 117 Continental Tropical Convergence Zone (CTCZ) Programme stations spread across India. The dataset is compared for the monsoon season (June–September: JJAS) of two years – 2011–2012, over six regional domains which differ in soil characteristics and mean soil moisture values, thus taking the spatial heterogeneity into account. Evaluation shows that the mean JJAS ESACCI volumetric soil moisture is 25.5% ( $$\hbox {m}^{3}\,\hbox {m}^{-3}$$ ), with an intra-seasonal standard deviation of 6%. The root mean squared difference (RMSD) between ESACCI soil moisture product and CTCZ observations is 10% over the Indian region. Over smaller homogeneous regions, the RMSD values between the two products are smaller than 5%, except over southern India and north-east India. Overall, the ESACCI soil moisture dataset is in good agreement with the CTCZ in-situ soil moisture observations, and has relatively higher accuracy over the plains of northern and central India, as compared to other regions. However, the ESACCI soil moisture dataset shows higher intra-seasonal variability at shorter time-scale of 2–4 days, as compared to the CTCZ observations, possibly due to the difference in the soil sampling depths between the two datasets.

Developing an Open Data Portal for the ESA Climate Change Initiative

We introduce the rationale for, and architecture of, the European Space Agency Climate Change Initiative (CCI) Open Data Portal ( http://cci.esa.int/data/ ). The Open Data Portal hosts a set of richly diverse datasets – 13 “Essential Climate Variables” – from the CCI programme in a consistent and harmonised form and to provides a single point of access for the (>100 TB) data for broad dissemination to an international user community. These data have been produced by a range of different institutions and vary across both scientific and spatio-temporal characteristics. This heterogeneity of the data together with the range of services to be supported presented significant technical challenges. An iterative development methodology was key to tackling these challenges: the system developed exploits a workflow which takes data that conforms to the CCI data specification, ingests it into a managed archive and uses both manual and automatically generated metadata to support data discovery, browse, and delivery services. It utilises both Earth System Grid Federation (ESGF) data nodes and the Open Geospatial Consortium Catalogue Service for the Web (OGC-CSW) interface, serving data into both the ESGF and the Global Earth Observation System of Systems (GEOSS). A key part of the system is a new vocabulary server, populated with CCI specific terms and relationships which integrates OGC-CSW and ESGF search services together, developed as part of a dialogue between domain scientists and linked data specialists. These services have enabled the development of a unified user interface for graphical search and visualisation – the CCI Open Data Portal Web Presence.

Comparison of GTO-ECV and adjusted MERRA-2 total ozone columns from the last 2 decades and assessment of interannual variability

Abstract. In this study we compare the satellite-based Global Ozone Monitoring Experiment (GOME)-type Total Ozone Essential Climate Variable (GTO-ECV) record, generated as part of the European Space Agency's Climate Change Initiative (ESA-CCI) ozone project, with the adjusted total ozone product from the Modern Era Retrospective Analysis for Research and Applications version 2 (adjusted MERRA-2) reanalysis, produced at the National Aeronautics and Space Administration (NASA) Global Modeling and Assimilation Office (GMAO). Total ozone columns and associated standard deviations show a very good agreement in terms of both spatial and temporal patterns during their 23-year overlap period from July 1995 to December 2018. The mean difference between adjusted MERRA-2 and GTO-ECV 5∘×5∘ monthly mean total ozone columns is -0.9±1.5 %. A small discontinuity in the deviations is detected in October 2004, when data from the Ozone Monitoring Instrument (OMI) were ingested in the GTO-ECV and adjusted MERRA-2 data records. This induces a small overall negative drift in the differences for almost all latitude bands, which, however, does not exceed 1 % per decade. The mean difference for the period prior to October 2004 is -0.5±1.7 %, whereas the difference is -1.0±1.1 % for the period from October 2004 to December 2018. The variability in the differences is considerably reduced in the period after 2004 due to a significant increase in data coverage and sampling. In the tropical region, the differences indicate a slight zonal variability with negative deviations over the Atlantic, Africa, and the Indian Ocean and positive deviations over the Pacific. Ozone anomalies and the distribution of their statistical moments indicate a very high correlation among both data records as to the temporal and spatial structures. Furthermore, we evaluate the consistency of the data sets by means of an empirical orthogonal function (EOF) analysis. The interannual variability is assessed in the tropics, and both GTO-ECV and adjusted MERRA-2 exhibit a remarkable agreement with respect to the derived patterns. The first four EOFs can be attributed to different modes of interannual climate variability, and correlations with the Quasi-Biennial Oscillation (QBO), the El Niño–Southern Oscillation (ENSO) signal, and the solar cycle were found.

Assessment with remotely sensed soil moisture products and ground-based observations over three dense network

Soil moisture (SM) is a key variable in hydrological processes, bio-ecological processes, and biogeochemical processes. Long-term observations of soil moisture over large areas are critical to research on flooding and drought monitoring, water resource management, and crop yield forecasts. In this paper, Fengyun (FY3B and FY3C) SM products, Japan Aerospace Exploration Agency (JAXA) SM products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Land Parameter Retrieval Model (LPRM) AMSR2 L3 SM products, the Version 2 (v2) global land parameter data record (LPDR) of SM products, Soil Moisture Ocean Salinity (SMOS) Centre Aval de Traitement des Donnees SMOS (CATDS) L3 SM products, the Soil Moisture Active Passive (SMAP) passive L3 SM products and the European Space Agency(ESA) Climate Change Initiative (CCI) SM products were evaluated using the ground-based observations in the Little Washita, Fort Cobb and Yanco networks. Long-time series comparison between measured and satellite products was conducted to evaluate the overall performance of FY3 series satellites SM products. Bias (mean bias), R (correlation coefficient), RMSE (root mean square error) and ubRMSE (unbiased root mean square error) were calculated to explore the agreement between satellite products and in-situ measurements. Taylor diagrams were used to compare the performance of various satellite products. The result showed that (1) FY3B, FY3C and LPRM AMSR2 ascending and descending products had an obvious overestimate with in-situ soil moisture in three networks. (2) JAXA AMSR2 ascending and descending products had considerable underestimation in three networks. (3) The validation result of SMOS ascending and descending products over the three networks was satisfactory, with a rather high correlation than other X-band products. (4) The validation result of SMAP ascending and descending products outperformed the other products over the Little Washita and Fort Cobb networks. (5) The ESA CCI product had the lowest values of RMSE and ubRMSE than the other products in the Yanco network, revealing the effectiveness of merging active and passive soil moisture products. (6)The LPDR descending SM products had better performance than the LPDR ascending SM products in the LW and FC networks.

Construction of a climate data record of sea surface temperature from passive microwave measurements

A statistical regression-based microwave sea surface temperature (SST) retrieval algorithm has been developed within the European Space Agency Climate Change Initiative (ESA-CCI) SST project. The retrieval algorithm was used to generate a climate data record (CDR) of passive microwave (PMW) SST from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and its follow-on instrument AMSR2 for the period June 2002–October 2017. Multisensor Matchup Datasets (MMDs), which includes satellite orbital data collocated with in situ and auxiliary data, was used to derive consistent algorithms for AMSR-E and AMSR2. The retrieval algorithms consist of wind speed (WS) retrievals and SST retrievals, with corresponding uncertainty retrievals. The WS retrieval consists of a two-step regression model, where the second step is a localized algorithm, trained to perform well over restricted WS intervals. A two-step multiple linear regression retrieval with localized algorithms is used to retrieve SST. The first-stage algorithm is trained to perform well over restricted latitude intervals for ascending and descending orbit, respectively, whereas the second-stage retrieval uses localized SST and WS algorithms. Furthermore, a new and effective method for detecting and screening for Radio Frequency Interference (RFI) was developed.Validation of the PMW SSTs against drifter in situ SSTs shows an overall bias of −0.02 K for quality level (QL) 4 and 5 AMSR-E retrievals with a standard deviation of 0.46 K. Validation results for QL 4 and 5 AMSR2 retrievals against drifter in situ SSTs give a bias of 0.002 K and a standard deviation of 0.45 K. The corresponding mean modelled SST uncertainties, including in situ and sampling uncertainties, are estimated to 0.45 K for QL 4 and 5 AMSR-E retrievals and 0.44 K for QL 4 and 5 AMSR2 retrievals. Validation against near-surface temperature measurements from Argo floats yielded comparable results, confirming the drifting-buoy validation. The validation results demonstrate a consistent PMW SST CDR with accurate SST observations and reliable uncertainty estimates.

Role of precipitation forcing on the uncertainty of land surface model simulated soil moisture estimates

Land surface processes considerably influence the global weather and climate patterns which makes its quantification significant to scientists, hydrologists as well as policymakers alike. Considering the lack of available in-situ measurement, retrieval of the land surface fluxes mostly relies on remotely sensed satellite retrieval and through simulations from land surface models (LSMs). Hence, it is essential to quantify the uncertainties present in the output of these land surface models which are mainly due to errors in forcing data, model parameters and model structure. Precipitation is one of the key input forcing data used in LSMs. With the advancement of remote sensing techniques, multiple sources of precipitation products are made available to the user community. This study examines the effect of precipitation uncertainty in LSM simulated soil moisture. For this study, four precipitation products are used namely, Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42RT v7, Global Data Assimilation System (GDAS), Climate Hazards Infrared Precipitation with Stations (CHIRPS) and Multi-Source Weighted-Ensemble Precipitation (MSWEP). These data products are used as meteorological forcing in Noah 3.6 LSM for the simulation of soil moisture. The uncertainty inherent in the precipitation products are examined using two approaches a) By evaluating the precipitation products against the gridded India Meteorological Department (IMD) precipitation dataset and b) By using the precipitation products for simulating soil moisture outputs. These were validated over the Indian subcontinent using validation data from the European Space Agency-Climate Change Initiative (ESA-CCI) soil moisture and Central Tibetan Plateau Soil Moisture and Temperature Monitoring Network (CTP-SMTMN) dataset for the years 2010 to 2012. The study utilizes various graphical as well as quantitative evaluation methods to determine the best performing precipitation product. Our study indicates that the simulated soil moisture forced with GDAS and MSWEP precipitation product performed consistently superior among all the other simulation outputs over India.

Contributions of climate change and groundwater extraction to soil moisture trends

Abstract. Climate change affects water availability for soil, and groundwater extraction influences water redistribution by altering water demand, both of which significantly affect soil moisture. Quantifying their relative contribution to the changes in soil moisture will further our understanding of the mechanisms underlying the global water cycle. In this study, two groups of simulations were conducted with and without groundwater (GW) extraction (estimated based on local water supply and demand) from 1979 to 2010 using the Chinese Academy of Sciences land surface model, CAS-LSM, with four global meteorological forcing datasets (GSWP3, PRINCETON, CRU-NCEP, and WFDEI). To investigate the contribution of climate change and GW extraction, a trajectory-based method was used. Comparing the simulated results with the in situ dataset of the International Soil Moisture Network (ISMN) and the satellite-based soil moisture product of the European Space Agency's Climate Change Initiative (ESA-CCI) indicated that the CAS-LSM reasonably reproduced the distribution of soil moisture and matched the temporal changes well. Globally, our results suggested a significant decreasing trend in surface soil moisture (0–10 cm, 0.98×10-4 mm3 mm−3 yr−1) over the 32-year period tested. The drying trends were mainly observed in arid regions such as the tropical desert regions in North Africa and the Arabian Peninsula, while the wetting trends were primarily in tropical forested areas in South America and northeastern Asia. Climate change contributed 101.2 % and 90.7 % to global drying and wetting trends of surface soil moisture, respectively, while GW extraction accounted for −1.2 % and 9.3 %, respectively. In deep soil, GW extraction contributed 1.37 % and −3.21 % to the drying and wetting trends, respectively. The weak influence of GW extraction may be because this activity occurs in limited areas. GW extraction contributed more than 35 % to the change in surface soil moisture in wetting areas where GW overexploitation occurs. GW is mainly extracted for irrigation to alleviate soil water stress in semiarid regions that receive limited precipitation, thereby slowing the drying trend and accelerating the wetting trend of surface soil. However, GW exploitation weakens the hydraulic connection between the soil and aquifer, leading to deeper soils drying up. Overall, climate change dominated the soil moisture trends, but the effect of GW extraction cannot be ignored.

Evaluation of Remotely-Sensed and Model-Based Soil Moisture Products According to Different Soil Type, Vegetation Cover and Climate Regime Using Station-Based Observations over Turkey

This study evaluates the performance of widely-used remotely sensed- and model-based soil moisture products, including: The Advanced Scatterometer (ASCAT), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), the European Space Agency Climate Change Initiative (ESA-CCI), the Antecedent Precipitation Index (API), and the Global Land Data Assimilation System (GLDAS-NOAH). Evaluations are performed between 2008 and 2011 against the calibrated station-based soil moisture observations collected by the General Directorate of Meteorology of Turkey. The calibration of soil moisture observing sensors with respect to the soil type, correction of the soil moisture for the soil temperature, and the quality control of the collected measurements are performed prior to the evaluation of the products. Evaluation of remotely sensed- and model-based soil moisture products is performed considering different characteristics of the time series (i.e., seasonality and anomaly components) and the study region (i.e., soil type, vegetation cover, soil wetness and climate regime). The systematic bias between soil moisture products and in situ measurements is eliminated by using a linear rescaling method. Correlations between the soil moisture products and the in situ observations vary between 0.57 and 0.87, while the root mean square errors of the products versus the in situ observations vary between 0.028 and 0.043 m3 m−3. Overall, according to the correlation and root mean square error values obtained in all evaluation categories, NOAH and ESA-CCI soil moisture products perform better than all the other model- and remotely sensed-based soil moisture products. These results are valid for the entire study time period and all of the sub-categories under soil type, vegetation cover, soil wetness and climate regime.

A patch-based algorithm for global and daily burned area mapping

Increasing availability of dense time series of moderate spatial resolution satellite data for mapping global burned areas calls for mapping algorithms designed to easily integrate data at different spatial and temporal resolutions, irrespective of particular grid constraints. In this paper, we describe a novel hybrid approach for global burned area mapping that combines active fire data and time series of surface reflectance using graphs, which provide a flexible and efficient way of extracting spatiotemporal consistent patches.Our approach has three main steps. Firstly, we analyze burn-sensitive vegetation index time series to determine for each location a set of events, which are the dates for which the spectral-temporal signal indicates the possibility of a burn. Secondly, we explore the spatiotemporal distribution of all events and active fires to determine a subset of events with strong evidence of corresponding to burned areas. Those events are used as positive occurrences for training a one-class maximum entropy classifier and obtain, for each candidate event, a likelihood of it actually corresponding to a burn. Finally, we build a graph that combines all previous information, from which we extract spatiotemporal patches of densely connected events. Patches with strong evidence of burning determine the burned area map at any given time period.This research is part of the European Space Agency's Climate Change Initiative (ESA-CCI) and aims ultimately at generating Sentinel-3 daily global burned area products at 500 m spatial resolution. Towards that end, we test our approach with spatially and spectrally similar MODIS gridded surface reflectance (MOD/MYD09GA), as well as non-gridded active fire (MCD14ML) 2008 data and CCI global land cover maps. Using 105 independent Landsat fire reference perimeters to validate global results, we show that our algorithm applied to MOD/MYD09GA data (PT-M09) has very similar accuracy (44%) measured by the Dice coefficient compared with MCD64A1 v006 (45%). Moreover, PT-M09 exhibits a higher commission error but a lower omission error than MCD64A1. Due to their coarse resolution, this kind of product cannot capture very small burn areas. The bias relative to the reference burned area indicates that the algorithm presented in the current study underestimates burned area by 14% of the area actually burned according to reference data, which is lower than the underestimation in MCD64A1 (28%).We also analyzed the temporal accuracy of the patch based algorithm and concluded that the average time difference to active fires detection is 3.43 days, which is similar to MCD64A1 (3.75 days). Finally, we performed a sensitivity analysis which shows that total mapped burned areas varies only 3% when the main algorithm threshold that separates “burned” and “unburned” patches varies from quantile 45% to 55%.

Influence of urban land cover data uncertainties on the numerical simulations of urbanization effects in the 2013 high-temperature episode in Eastern China

Updating the urban land cover information has been proved a necessary method for the numerical studies of urban climate and urban atmospheric environment in China, a fast urbanizing country. However, there are uncertainties in the urban land use/cover (ULUC) information in different datasets due to the uncertainties in raw data sources and produce methods. In this study, the Weather Research and Forecasting model is used to simulate the summer climate over the Yangtze River Delta in July and August 2013, when a heatwave episode occurred. Five numerical experiments are designed; one is the baseline experiment (non-urban land cover (NOURB)) in which all urban land covers are removed, and different urban land use/cover (ULUC) experiments from four different global land cover datasets are used in other four experiments. The differences between the ULUC experiments and the NOURB experiment are used to observe the urbanization effects. The results show a remarkable urbanization effect in all the ULUC experiments, and the urbanization influences are most different over the union-urban area, which defined as the area marked as urban area by at least one land cover dataset, because the uncertainties in urban land cover information are greatest in this area. All the ULUC experiments show the regional influence of urbanization as well, which means urbanization effects are not limited in cities. The Climate Change Initiative (CCI) experiment using the European Space Agency CCI 2013 dataset showed the greatest urbanization influence and the highest effect index in all analyzed variables, because of the largest urban land cover area in the analysis domain. The urban land cover area in the Global Land Cover experiment is the smallest among the ULUC experiments, but it distributes in a more concentrated way than in other experiments; it indicates that both city size and city shape affect the regional influence.

The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment

Abstract. The ECMWF OCEAN5 system is a global ocean and sea-ice ensemble of reanalysis and real-time analysis. This paper gives a full description of the OCEAN5 system, with the focus on upgrades of system components with respect to its predecessors, ORAS4 and ORAP5. An important novelty in OCEAN5 is the ensemble generation strategy that includes perturbation of initial conditions and a generic perturbation scheme for observations and forcing fields. Other upgrades include revisions to the a priori bias correction scheme, observation quality control and assimilation method for sea-level anomalies. The OCEAN5 historical reconstruction of the ocean and sea-ice state is the ORAS5 reanalysis, which includes five ensemble members and covers the period from 1979 onwards. Updated versions of observation data sets are used in ORAS5 production, with special attention devoted to the consistency of sea surface temperature (SST) and sea-ice observations. Assessment of ORAS5 through sensitivity experiments suggests that all system components contribute to an improved fit to observation in reanalyses, with the most prominent contribution from direct assimilation of ocean in situ observations. Results of observing system experiments further suggest that the Argo float is the most influential observation type in our data assimilation system. Assessment of ORAS5 has also been carried out for several key ocean state variables and verified against reference climate data sets from the ESA CCI (European Space Agency Climate Change Initiative) project. With respect to ORAS4, ORAS5 has improved ocean climate state and variability in terms of SST and sea level, mostly due to increased model resolution and updates in assimilated observation data sets. In spite of the improvements, ORAS5 still underestimates the temporal variance of sea level and continues exhibiting large SST biases in the Gulf Stream and its extension regions which are possibly associated with misrepresentation of front positions. Overall, the SST and sea-ice uncertainties estimated using five ORAS5 ensemble members have spatial patterns consistent with those of analysis error. The ensemble spread of sea ice is commensurable with the sea-ice analysis error. On the contrary, the ensemble spread is under-dispersive for SST.

Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: A comprehensive assessment using global ground-based observations

Comprehensive assessments on the reliability of remotely sensed soil moisture products are undeniably essential for their advancement and application. With the establishment of extensive dense networks across the globe, mismatches between satellite footprints and ground single-point observations can be feasibly relieved. In this study, five remotely sensed soil moisture products, namely, the Soil Moisture Active Passive (SMAP), two Soil Moisture and Ocean Salinity (SMOS) products, the Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2) and the European Space Agency (ESA) Climate Change Initiative (CCI), were systematically investigated by utilizing in-situ soil moisture observations from global dense and sparse networks. Distinguished from previous studies, several perturbing factors comprising the surface temperature, vegetation optical depth (VOD), surface roughness and spatial heterogeneity were taken into account in this investigation. Furthermore, products' skills under various climate regions were also evaluated.Through the results, the SMAP product captures temporal trends of ground soil moisture, exhibiting an averaged R of 0.729, whereas for overall accuracy, ESA CCI outperformed other products with a slightly smaller ubRMSE of 0.041 m3 m−3 and a bias of −0.005 m3 m−3. This complementarity between SMAP and ESA CCI was further demonstrated under different climate conditions and can afford the reference of their integration for a more reliable global soil moisture product. Though some underestimations still exist, the newly developed SMOS- INRA-CESBIO (SMOS-IC) was illustrated to gain considerable upgrades with regard to R and ubRMSE compared to SMOS-L3 product, especially in dense VOD conditions achieving the highest R compared to other products.Generally, the underestimations of the European Centre for Medium-Range-Weather Forecasts (ECMWF) surface temperature used for SMOS under moderate or high VOD, heterogeneity, and most surface roughness conditions were consistent with the underestimations of the soil moisture product and provide the directions of product promotions. As for LPRM surface temperature, the worse skills can partially explain the unsatisfactory performances for LPRM soil moisture products. In spite of relatively acceptable skills of SMAP and SMOS-IC soil moisture products concerning R under moderate or dense VOD, small surface roughness, low heterogeneity conditions and temperate and cold climate types, advances in soil moisture products under high or even slightly low VOD, high roughness or topography complexity and heterogeneity, as well as in tropical or desert regions, remain challenging. It is expected that these findings can contribute to algorithm refinements, product enhancements (e.g., fusion and disaggregation) and hydrometeorological usages.

Intercomparison in spatial distributions and temporal trends derived from multi-source satellite aerosol products

Abstract. Satellite-derived aerosol products provide long-term and large-scale observations for analysing aerosol distributions and variations, climate-scale aerosol simulations, and aerosol–climate interactions. Therefore, a better understanding of the consistencies and differences among multiple aerosol products is important. The objective of this study is to compare 11 global monthly aerosol optical depth (AOD) products, which are the European Space Agency Climate Change Initiative (ESA-CCI) Advanced Along-Track Scanning Radiometer (AATSR), Advanced Very High Resolution Radiometer (AVHRR), Multi-angle Imaging SpectroRadiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Visible Infrared Imaging Radiometer (VIIRS), and POLarization and Directionality of the Earth's Reflectance (POLDER) products. AErosol RObotic NEtwork (AERONET) Version 3 Level 2.0 monthly measurements at 308 sites around the world are selected for comparison. Our results illustrate that the spatial distributions and temporal variations of most aerosol products are highly consistent globally but exhibit certain differences on regional and site scales. In general, the AATSR Dual View (ADV) and SeaWiFS products show the lowest spatial coverage with numerous missing values, while the MODIS products can cover most areas (average of 87 %) of the world. The best performance is observed in September–October–November (SON) and the worst is in June–July–August (JJA). All the products perform unsatisfactorily over northern Africa and Middle East, southern and eastern Asia, and their coastal areas due to the influence from surface brightness and human activities. In general, the MODIS products show the best agreement with the AERONET-based AOD values on different spatial scales among all the products. Furthermore, all aerosol products can capture the correct aerosol trends at most cases, especially in areas where aerosols change significantly. The MODIS products perform best in capturing the global temporal variations in aerosols. These results provide a reference for users to select appropriate aerosol products for their particular studies.

Global Surface Soil Moisture Dynamics in 1979–2016 Observed from ESA CCI SM Dataset

Soil moisture (SM) is an important variable for the terrestrial surface system, as its changes greatly affect the global water and energy cycle. The description and understanding of spatiotemporal changes in global soil moisture require long time-series observation. Taking advantage of the European Space Agency (ESA) Climate Change Initiative (CCI) combined SM dataset, this study aims at identifying the non-linear trends of global SM dynamics and their variations at multiple time scales. The distribution of global surface SM changes in 1979–2016 was identified by a non-linear methodology based on a stepwise regression at the annual and seasonal scales. On the annual scale, significant changes have taken place in about one third of the lands, in which nonlinear trends account for 48.13%. At the seasonal scale, the phenomenon that “wet season get wetter, and dry season get dryer” is found this study via hemispherical SM trend analysis at seasonal scale. And, the changes in seasonal SM are more pronounced (change rate at seasonal scales is about 5 times higher than that at annual scale) and the areas seeing significant changes cover a larger surface. Seasonal SM fluctuations distributed in southwestern China, central North America and southern Africa, are concealed at the annual scale. Overall, non-linear trend analysis at multiple time scale has revealed more complex dynamics for these long time series of SM.

Crop residue open field burning in Pakistan; multi-year high spatial resolution emission inventory for 2000–2014

Over recent years, crop residue burning has become a widely practiced agricultural activity in developing countries due to varying economic and social reasons. To estimate current pollution generation from crop residue burning in Pakistan, an emission inventory was developed based on district level crop production data from 2000 to 2014. Spatial distribution of quantified emissions was achieved by using MODIS Active Fire Data (MOD/MYD14A1) at 1-day temporal and 1 × km spatial resolutions. The fire occurrences were extracted over agricultural land using land cover data provided by European Space Agency/Climate Change Initiative (ESA/CCI) at 300 m spatial resolution for 2000 to 2014. Results show that CO emissions have increased by 40% at an average annual rate of 2.7% from 1.16 Tg in 2000 to 1.63 Tg in 2014 and increase in emissions of CO2, CH4, NMVOCS, N2O, NH3, SO2, NOx, PM2.5, PM10, OC and BC ranged between 37%, to 63% during same period. Spatially, CO emissions were more significant in districts of Punjab and Sindh provinces with intensive agricultural activities as compared to other areas of Pakistan with highest emission grids located in Muzaffargarh, Jafferabad, and Noushahro Feroze districts. Crop residue burning exhibited similar trend with highest emissions in June followed by February and November over 15-year period. The emissions for most of the considered pollutants were comparable to the limited previous studies available. Finally, the study conducted provides better understanding regarding spatial and temporal patterns of crop residue burning across Pakistan and can be utilized for air quality modeling and management with higher accuracy.