Flux Datasets Research Articles

基于多源遥感数据的城市热环境响应与归因分析——以深圳市为例

城市热环境是城市局部气候与环境的综合表现，它与土地利用格局密切相关，但是相应的机制研究还不充分。以深圳市为例，首先基于2018年LANDSAT 8遥感影像，采用支持向量机方法和线性光谱混合模型提取土地利用与覆盖度信息，分析了城市土地覆盖对地表温度以及热量收支状况的影响。基于2003-2018的MODIS地表温度数据，进一步研究了深圳市城市热岛现象的时空变化，从地表能量的角度分析城市热岛变化背后的形成机制。结果表明，深圳市地表温度从西北到东南逐渐降低，城市不透水面温度显著高于植被覆盖区域，城市热岛效应明显。不透水面和城市植被共同影响深圳市的地表温度与热量收支状况，不透水地表与感热具有较好的相关性，城市植被与潜热具有较好的相关性。长时间序列分析表明深圳的城市热岛现象在夏季较高而冬季较低，月均热岛强度为2.14℃；对于年际变化，深圳在2003-2018表现出显著的下降趋势。归因分析显示感热通量的影响在深圳起主导作用，这一模式在全年和季节上都较为明显。结果表明深圳市经过高速扩张阶段，目前发展方向是提高建成区的利用效率，该现象强调了热传输在加强城市热岛效应过程中对近地面湍流的干扰作用。本研究可以为缓解热岛效应与景观格局优化研究提供借鉴。;Urban thermal environment is a comprehensive expression of local climate and environment. While it is closely related to land use patterns, its underlying mechanisms are largely unexplored. Urban heat island affects not only regional climate, vegetation growth and air quality, but also human health. Therefore, the urban thermal environment has been regarded as a critical variable. It is important to characterize the spatial and temporal patterns of urban thermal environments and quantify the response of the associated influencing factors. This study uses the city of Shenzhen as an example where rapid urbanization has occurred. Based on LANDSAT 8 remote sensing images collected in 2018, we first extract land cover types and coverage information using support vector machine and multiple linear spectral analysis models. The accuracies of the estimated land covers are acceptable, which provides confidence for further analyses. The impacts of urban land cover on land surface temperature and heat energy component are then investigated. Using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature dataset obtained from 2003 to 2018, this study further explores the spatiotemporal variations of urban heat island in Shenzhen, and analyzes its potential formation mechanisms from the perspective of surface energy balance. Our results show that the land surface temperature gradually decreases from northwest to Southeast of Shenzhen. This is largely attributed to the spatial distribution of impervious surfaces and the urban vegetation. Specifically, the land surface temperature of impervious surfaces is substantially higher than that of vegetation-covered area, implying that the urban heat island effect is obvious in Shenzhen. Impervious surface and urban vegetation jointly affect the surface temperature and heat budget through, for example, latent heat flux and sensible heat flux. Impervious surface is more correlated with sensible heat flux while urban vegetation is more correlated with latent heat flux. Obvious differences occur in sensible heat and latent heat among land cover types, which provides implications for the formation and elimination of urban heat island. Time series analysis regarding the period of 2003-2018 illustrates that the urban heat island effect in Shenzhen is stronger during summer season whereas relatively weaker during winter season. The urban heat island effect is remarkably exhibited using the time series dataset, with an average monthly intensity of 2.14 ℃. Regarding the internal variation, the downward trend in urban heat island intensity is shown from 2003 to 2018. This negative trend is significantly present during all seasons except spring, with a change rate of approximately 0.1 ℃/annual. Attribution analysis based on MODIS dataset and FLDAS flux dataset reveals that sensible heat flux plays a more important role in the urban heat island of Shenzhen in comparison to latent heat flux. This is consistent in the both annual and seasonal scales, and the relative contribution of latent heat flux and sensible heat flux is 42% and 58%, respectively. Results indicate that after a rapid urban expansion period in Shenzhen, the current development focus is to improve the utilization efficiency of built-up areas. This emphasizes the interference of heat transfer with the near-surface turbulence that strengthens the urban heat island effect. Our study provides reference values for mitigating the urban heat island effect and optimizing landscape patterns.

New Dataset Based on Multi-Source Land Surface Flux Data and Its Application in the East Asian Summer Monsoon Boundary Area

The EASM (East Asian summer monsoon) boundary area is active with intense interaction between the land surface process and the regional climate. However, the lack of high-quality long-term flux datasets for this region limits the study of the interaction among the land surface water, heat exchange, and climate. It is necessary to reconstruct a new dataset based on the currently available multiple flux data and then apply it in climate research. In this study, the datasets of land surface energy fluxes in the EASM boundary area in China were reconstructed by integrating the field observations conducted over northern China and several gridded datasets. Based on the selection of sites with good underlying representative surface and the investigation of the scattering distribution of simulations and observations, a set of monthly average sensible heat, latent heat, and net radiation datasets was generated using a multiple regression model. The cross-validation results showed that the accuracy of the constructed dataset was improved compared with several original gridded datasets, and the systematic deviation of the original lattice data was maximally eliminated. Further analysis suggests that among the surface energy balance components, the response of land surface turbulent flux to summer monsoon was more significant, and the interannual variation of the land surface turbulent heat flux with the summer monsoon duration in the EASM boundary area showed logarithmic distribution. The turbulent heat fluxes presented more significant interannual variations, as the summer monsoon was in a low persistent state. A weaker summer monsoon system may lead to a stronger impact of land surface processes on climate change. The new dataset based on multi-source flux data fusion can support climate change research and further clarify the interaction between land surface processes and monsoon climate.

Seasonal variability of air-sea fluxes in two contrasting basins of the North Indian Ocean

Latent Heat Flux (LHF) and Sensible Heat Flux (SHF) are the two important parameters in air-sea interactions and hence have significant implications for any coupled ocean-atmospheric model. These two fluxes are conventionally computed from met-ocean parameters using bulk aerodynamic formulations; or the Coupled Ocean Atmosphere Response Experiment (COARE) bulk flux algorithms. Here COARE 3.5 algorithm is used to estimate the heat flux from two Ocean Moored Buoy Network for northern Indian Ocean (OMNI) buoy met-ocean observations in Arabian Sea (AS) and the Bay of Bengal (BoB). The AS and BoB are two ocean basins which are situated in same latitudinal range, but experience drastically differing in their met-ocean conditions, especially during the monsoon seasons. In this study, we have computed and compared the LHF and SHF at two different buoy locations in the AS and BoB and analysed their variability during three different seasons from November 2012 to September 2013. Additionally, 20 years (1998–2017) of Objectively Analysed (OA) Flux data sets collocated with the OMNI buoy locations were also utilised to the analyse the long period seasonal variabilities. The flux terms show strong seasonal variability with several peaks during the monsoon seasons in both the ocean basins. LHF varies directly with wind speed (WS) and inversely with relative humidity (RH). The correlation of LHF with WS is greater than 0.7 and RH is nearly -0.6 with few exceptions during pre-monsoon season in the AS and southwest monsoon in the BoB. However, SHF is less correlated with WS (∼0.3 to 0.5). The difference of sea surface temperature and air temperature (denoted as SST-AT) plays a significant role in determining SHF with a correlation greater than 0.6 in both the basins.

A long-term (2005–2019) eddy covariance data set of CO<sub>2</sub> and H<sub>2</sub>O fluxes from the Tibetan alpine steppe

Abstract. The Tibetan alpine steppe ecosystem covers an area of roughly 800 000 km2 and contains up to 3.3 % soil organic carbon in the uppermost 30 cm, summing up to 1.93 Pg C for the Tibet Autonomous Region only (472 037 km2). With temperatures rising 2 to 3 times faster than the global average, these carbon stocks are at risk of loss due to enhanced soil respiration. The remote location and the harsh environmental conditions on the Tibetan Plateau (TP) make it challenging to derive accurate data on the ecosystem–atmosphere exchange of carbon dioxide (CO2) and water vapor (H2O). Here, we provide the first multiyear data set of CO2 and H2O fluxes from the central Tibetan alpine steppe ecosystem, measured in situ using the eddy covariance technique. The calculated fluxes were rigorously quality checked and carefully corrected for a drift in concentration measurements. The gas analyzer self-heating effect during cold conditions was evaluated using the standard correction procedure and newly revised formulations (Burba et al., 2008; Frank and Massman, 2020). A wind field analysis was conducted to identify influences of adjacent buildings on the turbulence regime and to exclude the disturbed fluxes from subsequent computations. The presented CO2 fluxes were additionally gap filled using a standardized approach. The very low net carbon uptake across the 15-year data set highlights the special vulnerability of the Tibetan alpine steppe ecosystem to become a source of CO2 due to global warming. The data are freely available at https://doi.org/10.5281/zenodo.3733202 (Nieberding et al., 2020a) and https://doi.org/10.11888/Meteoro.tpdc.270333 (Nieberding et al., 2020b) and may help us to better understand the role of the Tibetan alpine steppe in the global carbon–climate feedback.

Comparisons of Net Heat Flux Data Sets Over the Western North Pacific

This study focuses on the intercomparisons of 22 net heat flux (NHF) data sets in terms of mean, linear trend, and interannual variability during 1993–2007 over the western North Pacific and its surrounding waters. The data sets can be categorized into the dominant group that shares common interannual variabilities and the outcasts that exhibit exceptional variability. Based on our analysis, we provide guidelines for users to choose reliable NHF data sets in the study regions. In addition, many NHF data sets tend to represent similar interannual variabilities in the marginal sea regions, whereas the opposite is true in open oceans where interannual variability is weak. Also, we find an evidence showing the mean biases of the NHF are mainly due to the difference in calculation of the latent heat flux. The aforementioned information will be useful for a wide community of scientists who are interested in using heat flux data.

COSORE: A community database for continuous soil respiration and other soil-atmosphere greenhouse gas flux data.

Globally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil‐to‐atmosphere CO2 flux, commonly though imprecisely termed soil respiration (R S), is one of the largest carbon fluxes in the Earth system. An increasing number of high‐frequency R S measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open‐source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long‐term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured R S, the database design accommodates other soil‐atmosphere measurements (e.g. ecosystem respiration, chamber‐measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package.

Technical note: Estimating light-use efficiency of benthic habitats using underwater O<sub>2</sub> eddy covariance

Abstract. Light-use efficiency defines the ability of primary producers to convert sunlight energy to primary production and is computed as the ratio between the gross primary production and the intercepted photosynthetic active radiation. While this measure has been applied broadly within terrestrial ecology to investigate habitat resource-use efficiency, it remains underused within the aquatic realm. This report provides a conceptual framework to compute hourly and daily light-use efficiency using underwater O2 eddy covariance, a recent technological development that produces habitat-scale rates of primary production under unaltered in situ conditions. The analysis, tested on two benthic flux datasets, documents that hourly light-use efficiency may approach the theoretical limit of 0.125 O2 per photon under low-light conditions, but it decreases rapidly towards the middle of the day and is typically 10-fold lower on a 24 h basis. Overall, light-use efficiency provides a useful measure of habitat functioning and facilitates site comparison in time and space.

Distinct atmosphere-ocean coupling processes on the onset phase of Indian summer monsoon during 2017 and 2018 as revealed through SCATSAT-1 and its comparison with CFSv2

ABSTRACT Distinct atmosphere-ocean coupled processes determine the inter-annual variability in the onset processes of Indian Summer Monsoon. In this work, we have analysed the distinct characteristics of onset process during 2017 (MOK17) and 2018 (MOK18) using SCATSAT-1 observations. Two years of dedicated SCATSAT-1 analysed winds and other flux datasets are used to understand distinct-coupled processes and further compared with coupled model forecasts from National Centres for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2). Air-sea coupled mechanism during 2017 and 2018 suggests that there is a delayed response (about one pentad) of atmosphere-ocean coupling over North Indian Ocean on the monsoon onset and its propagation during 2018. It is found that the influence of cyclone Mekunu formed over Western Arabian Sea disturbed the onset vortex formed over Eastern Arabian Sea during MOK18. The presence of cyclone Mekunu results in a sudden spike of East-west Sea Surface Temperature gradient, Latent Heat Flux and wind stress over Arabian Sea during one pentad before monsoon onset over Kerala. This created an onset like situation in the next pentad, and later the conditions are not favoured for the further progress of the monsoon. It leads to a weak onset followed by a sluggish and halted northward progression of monsoon during 2018. In the absence of a cyclonic system during the onset phase of MOK17, conventional onset and its further progress took place during 2017. Further comparison of SCATSAT-1 and other analysed data sets with the CFSv2 model show that the CFSv2 model is able to capture the atmosphere-ocean coupled processes during the onset phase up to four pentads in advance during MOK17. However, due to the presence of Mekunu cyclone before the monsoon onset limited the forecast skill of CFSv2 only up to two pentads in advance during MOK18. We found that the presence of strong convective instability due to strong synoptic-scale system can influence the skill of coupled model forecast in the extended range. The new SCATSAT-1 observations provide high-quality observations that can be used to quantify the errors and identify common biases in the coupled models.

A daily 0.25° × 0.25° hydrologically based land surface flux dataset for conterminous China, 1961–2017

This paper describes a daily 0.25° × 0.25° terrestrial hydrological dataset (VIC-CN05.1) for China during 1961–2017. The dataset is simulated by the latest variable infiltration capacity (VIC) model and driven by pure station-based atmospheric forcings and high-resolution soil parameters based on field surveys. Multiple observational (in situ and remote sensing) products and five statistical metrics, including biases, relative error (RE), root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NSE), and correlation coefficient (R), are used to validate the model outputs. The results indicate that the seasonal cycles of simulated total runoff and composite runoff from the University of New Hampshire and Global Runoff Data Centre (UNH/GRDC) show good agreement in the Yangtze, southeast, and Pearl River basins, with positive REs ≤ 7% and NSEs ≥ 0.87. At the Changbaishan, Haibei Shrubland, and Qianyanzhou flux towers, VIC-CN05.1 reproduces the monthly latent heat fluxes well during 2003–2005. The monthly modeled 0–10 cm soil moisture anomalies are highly correlated to in situ measurements (438 stations) during 2003–2016, with mean R = 0.80. The spatial patterns of simulated and observed snow depth, snow water equivalent (SWE), and snow cover in winter are generally consistent in the northeastern and northwestern China. Except for the northwest basin, the simulated monthly terrestrial water storage changes in most river basins (particularly the Yangtze, Huai, and Yellow River basins) match well with observations based on the Gravity Recovery and Climate Experiment (GRACE) during 2003–2016, with positive NSEs and mean R = 0.41. This terrestrial hydrology dataset is valuable for hydroclimatic research in China, such as model evaluations and long-term trend analyses.

Environmental controls on ecosystem-scale cold-season methane and carbon dioxide fluxes in an Arctic tundra ecosystem

Abstract. Understanding the processes that influence and control carbon cycling in Arctic tundra ecosystems is essential for making accurate predictions about what role these ecosystems will play in potential future climate change scenarios. Particularly, air–surface fluxes of methane and carbon dioxide are of interest as recent observations suggest that the vast stores of soil carbon found in the Arctic tundra are becoming more available to release to the atmosphere in the form of these greenhouse gases. Further, harsh wintertime conditions and complex logistics have limited the number of year-round and cold-season studies and hence too our understanding of carbon cycle processes during these periods. We present here a two-year micrometeorological data set of methane and carbon dioxide fluxes, along with supporting soil pore gas profiles, that provide near-continuous data throughout the active summer and cold winter seasons. Net emission of methane and carbon dioxide in one of the study years totalled 3.7 and 89 g C m−2 a−1 respectively, with cold-season methane emission representing 54 % of the annual total. In the other year, net emission totals of methane and carbon dioxide were 4.9 and 485 g C m−2 a−1 respectively, with cold-season methane emission here representing 82 % of the annual total – a larger proportion than has been previously reported in the Arctic tundra. Regression tree analysis suggests that, due to relatively warmer air temperatures and deeper snow depths, deeper soil horizons – where most microbial methanogenic activity takes place – remained warm enough to maintain efficient methane production whilst surface soil temperatures were simultaneously cold enough to limit microbial methanotrophic activity. These results provide valuable insight into how a changing Arctic climate may impact methane emission, and highlight a need to focus on soil temperatures throughout the entire active soil profile, rather than rely on air temperature as a proxy for modelling temperature–methane flux dynamics.

Clouds and the Earth’s Radiant Energy System strategy for intercalibrating the new-generation geostationary visible imagers

The advanced baseline imager (ABI) instrument onboard Geostationary Operational Environmental Satellite (GOES)-16 is the first of National Oceanic and Atmospheric Administration (NOAA’s) new-generation geostationary earth orbiting (GEO) imagers that provides high-quality calibrated and geolocated Earth observations in six reflective solar bands (RSBs). The spectral similarity between the Visible Infrared Imaging Radiometer Suite (VIIRS) and ABI RSB offers an opportunity for deriving VIIRS-quality cloud retrievals from the ABI radiances. NASA’s Clouds and the Earth’s Radiant Energy System (CERES) project utilizes GEO imager (including ABI) radiances to retrieve clouds and derive broadband fluxes that are used to account for the regional diurnal flux variation between the CERES measurements and to convert the CERES observed radiances into fluxes. In order to derive a seamless cloud and flux datasets for CERES, it is important that the GEO, MODIS, and VIIRS imagers are all placed on the same radiometric scale. We describe an absolute radiometric intercomparison between the NOAA-20 VIIRS and GOES-16 ABI RSB using ray-matched radiance/reflectance pairs over all-sky tropical ocean scenes as well as a deep convective cloud invariant target calibration algorithm. Results indicate that the ABI and VIIRS RSB calibration are within 5%, except for the 0.47-μm band, for which the radiometric inconsistency is found to be ∼7 % . The GOES-16 radiometric scaling factors referenced to NOAA-20 VIIRS were computed from the two independent calibration methods to agree within 1% for ABI bands 1 to 4, and within 3% for bands 5 and 6. Results from this study were used to propose a future CERES GEO intercalibration algorithm referenced to NOAA-20 VIIRS, given the eventual demise of the Terra and Aqua satellites.

The Canadian model for peatlands (CaMP): A peatland carbon model for national greenhouse gas reporting

A model framework for national greenhouse gas emission and removal estimation for Canadian peatlands (CaMP v2.0) was developed and tested. It provides a module that can work alongside the upland forest Generic Carbon Budget Model (GCBM) developed to eventually replace the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) as the core model in Canada's National Forest Carbon Monitoring, Accounting and Reporting System. The CaMP (v2.0) provides a simple model foundation that can be applied nationally for 11 different peatland categories. It tracks the growth, turnover and decay in annual time steps of different vegetation components (foliage, branches, stems, and roots of trees, shrubs, sedges and mosses). It uses a Q10 relationship to model peat C pool decomposition as a function of mean annual temperature, and models methane flux response to deviations in annual water table depth. The CaMP takes a simple approach to modeling hydrology for large spatial scales by using the nationally-available Canadian Fire Weather Index Drought Code to predict long-term and annual water table depth. The CaMP (v2.0) provides the framework needed to model disturbances but only includes wildfire in this version. Model behavior and sensitivity were assessed, and evaluated against observed flux data. Results suggest that the CaMP (v2.0) provides an appropriate structure for large spatial- and temporal-scale estimation of emissions, owing to the model behaving as expected relative to shifts in environmental variables, and to reasonably small mean observed to modeled residuals. Methane was overestimated by the model on average by 6 g C ha−1y − 1 (n = 53 years of data across 11 peatland sites), and by 8 g C ha−1y − 1 when weighted by site location (n = 12 sites, ≥ 3 years of data per site). The model overestimated net ecosystem exchange (NEE) by 20 g C ha−1y − 1 (n = 36 years of data across 12 peatland sites), and by 2 g C ha−1y − 1 when weighted by site location (n = 11 sites, ≥ 3 years of data per site), and results demonstrate that inter-site variation is greater than temporal variation across NEE measures. Several aspects were identified as requiring further work to increase explained variation in finer-scale emission estimates. Recommendations include further expanding the existing peatland databases to re-calibrate peat decomposition rates and better parameterize NPP rates by region for certain vegetation layers and peatland types, as well as developing a national annual-scale soil temperature model that could serve to replace the air temperature (Q10) decay relationship currently used in the CaMP (v2.0). Data gaps that were identified include the need for annualized methane flux datasets with appropriate annual-scale meta-data. Future work is required to include permafrost dynamics, as well as additional natural, and anthropogenic disturbances.

Comparison of the third-generation Japanese ocean flux data set J-OFURO3 with numerical simulations of Typhoon Dujuan (2015) traveling south of Okinawa

Insufficient in situ observations in high winds make it difficult to verify climatological data sets and the results of tropical cyclone (TC) simulations. Reliable data sets are necessary for developing numerical models that predict TCs more accurately. This study attempted to compare the third-generation Japanese Ocean Flux Data Sets with Use of Remote-Sensing Observations (J-OFURO3) data, with TC simulations conducted by a 2 km mesh coupled atmosphere-wave-ocean model. This is a case study of Typhoon Dujuan (2015) and the area of approximately 20̊N, 130̊E, south of Okinawa, was selected. The comparison reveals that J-OFURO3 data are reliable for verifying the atmospheric and oceanic components of TC simulations with two different initial sea surface temperature (SST) conditions, although the blank area remains within the inner core area for air temperature, specific humidity, and latent heat flux owing to issues with the construction method. Simulated maximum surface wind speeds (MSWs) are significantly correlated with J-OFURO3 MSWs. The asymmetrical distribution of simulated surface wind speeds within the inner core area can be reproduced well in the J-OFURO3 data set. In terms of the oceanic response to the TC, TC-induced sea surface cooling was reproduced well in the J-OFURO3 data set and is consistent with the simulation results. Unlike simulated SST, simulated surface wind speeds, surface air temperature, and surface specific humidity are still inconsistent with the J-OFURO3 data, even when the J-OFURO3 SST is used as the initial condition. New algorithms, more satellite data used, and model improvement are expected in the future.

Characterizing Extreme Geomagnetic Storms Using Extreme Value Analysis: A Discussion on the Representativeness of Short Data Sets

AbstractOne of the main goals when studying Space Weather is to characterize extreme events occurrences and related characteristics. To do so, dedicated statistical methods from the so‐called extreme value analysis (EVA) field have been developed. In this study we used Ca index, derived from aa, in order to characterize geoeffectiveness from the radiation belts point of view with a 150‐year‐long data set. The analysis performed in this study thus focuses on this newsworthy index to provide clues on the reliability of EVA methods. The first main result we present here is that the 1‐in‐10‐, 1‐in‐50‐, and 1‐in‐100‐year events, respectively, match Ca values of 100.39, 131.39, and 142.84 nT. Consequently, the only 1‐in‐100 event observed during the Space Era would be the “Halloween Storm” in 2003 that reached a Ca value of 147.6 nT. The second main result highlighted in this work is that performing the same analysis with shorter subsets (20 years long) can give significantly different results for two reasons. The first reason is that some short time periods do not display the same distribution of events as the full period. The second reason is that the choice of the correct threshold (when using a Peaks Over Threshold approach) is made difficult with a short data set and leads to inaccurate results. This is a strong result as for accurate estimation of the induced effects of extreme events in radiation belts, we may only rely on short flux data sets from one or another mission (mostly shorter than 20 years).

Analysis of the 21-years long carbon dioxide flux dataset from a Central European tall tower site

Eddy-covariance based carbon flux datasets spanning decades are becoming available worldwide due to the effort of associated scientists. Tall tower based monitoring stations are relatively rare, but provide important information about the carbon balance of a larger region surrounding the tower. In this study we report and analyze the 21-year-long dataset provided by the Hungarian tall tower site, Hegyhátsál. The daily and annual cycles of net ecosystem exchange (NEE), gross primary production, and total ecosystem respiration are presented. Footprint analysis reveals that the fluxes mostly originate from the surrounding arable lands; the main source region is located within 1 km around the tower. Long-term, mean NEE was -0.467 gC m-2 day-1, or -170 gC m-2 year-1, revealing that the complex region was a net carbon sink from the atmospheric perspective. Trend analysis indicates that overall, NEE decreased (i.e. became more negative, which means stronger sink) by 12 gC m-2 year-1, a trend that is explained by improved agrotechnology and climate change. Net biome production (NBP) was estimated using crop census data and assumptions about the management practices that affect lateral carbon flux. Long term mean NBP was -30 gC m-2 year-1, which indicates that the soils may be losing carbon, though this loss is within the range of uncertainty of the measurements (~50 gC m-2 year-1). Analysis of county-scale yield statistics suggested that the results can be representative to at least county scale (1651 km2). Interannual variability was analyzed by using environmental variables such as maximum and minimum temperature, precipitation, vapor pressure deficit, soil water content, and satellite based vegetation index aggregated at monthly and longer time intervals. The results indicate that environmental conditions in spring have a major role in the annual carbon balance. Moreover, water availability represented by soil water content rather than by precipitation is a major driver of the interannual variability of NEE. The statistical analysis suggested that the large positive NEE anomaly during 2001–2003 was caused by interplay among the environmental drivers, in particular maximum temperature, vapor pressure deficit and radiation in April, followed by a soil water content deficit during the growing season. This novel statistical analysis provides insight into the drivers of the carbon balance of a mixed agricultural region.

Hot moments in ecosystem fluxes: High GPP anomalies exert outsized influence on the carbon cycle and are differentially driven by moisture availability across biomes

The ‘hot spot-hot moment’ concept is a long-standing and popular framework often invoked to explain spatially or temporally variable rates of biogeochemical cycling. However, this concept has been rarely extended to ecosystem fluxes such as gross primary productivity (GPP), in part due to the lack of a quantitative definition of hot moments that can be applied to large flux datasets. Here, we develop a general statistical framework for quantifying hot moments in GPP and identify their spatial patterns and climatic drivers. Using 308 site-years of eddy covariance data from the FLUXNET2015 dataset spanning 32 U.S. sites, we found hot moments in GPP to comprise a disproportionate percentage of annual carbon (C) uptake relative to the frequency of their occurrence. For example, at five sites over 12% of annual C uptake occurred during the ∼2% most extreme half-hourly or hourly observations of GPP. Hot moments were most quantitatively important for the C cycle in short-stature, arid ecosystem such as grasslands, woody savannas, and open shrublands, where these positive anomalies in GPP were caused by increases in moisture availability. In contrast, hot moments were less important for annual C uptake in more mesic ecosystems, where their occurrence was largely determined by high temperature and light availability. Our results point to a need to consider how short-term spikes in environmental conditions exert an outsized influence on annual GPP, and how future shifts in these conditions could impact the terrestrial C cycle.

Validation of different global data sets for sea surface wind-stress

ABSTRACT Different surface wind-stress products over the global ocean were validated by comparing with in-situ measurements from moored buoys, and by inter-comparing among them. The products are ones constructed from satellite observations by microwave scatterometers and radiometers, and reanalysed ones by data assimilation and numerical models. Comparisons with buoy measurements revealed that the Cross-Calibrated Multi-Platform Version 2 (CCMP v2.0) has the lowest positive bias, the smallest root mean square error (RMSE) and the largest correlation coefficient in this inter-comparison, meaning that the CCMP v2.0 gives the best performance. Inter-comparisons among the different wind-stress products exhibited that the meridional wind-stress data of the National Centre for Environmental Prediction/National Centre for Atmospheric Research (NCEP/NCAR) are not different in the subtropical region from those of other ones, and zonal and meridional wind-stress data of the National Centre for Environmental Prediction/Climate Forecast System Reanalysis (NCEP/CFSR) have relatively low values in the westerly region. In the zonal-spectral analysis, we found that the zonal wind-stress/wind-vector data from the Institut Français de Recherche pour la Recherche et l’Exploitation de la Mer (IFREMER) have short wave-like oscillations. Thus, the surface wind and wind-stress data in IFREMER products have been well affected by spatial resolution of background data used in their gridding procedure. We also investigated the spatial characteristics of wind-stress curl fields for the different data sets, and found that there are differences between the CCMP v2.0 and Japanese Ocean Flux Data sets with Use of Remote Sensing Observations Version 3 (J-OFURO3) which are characterized by spike-like features in the equatorial Pacific where there are the Tropical Atmosphere Ocean and the TRIangle Trans-Ocean buoy Network (TAO/TRITON) buoys. These features were spatially correlated with buoy locations, which suggests that the reliability of the CCMP v2.0 products is governed by buoy locations in this region.

Parameterization and Surface Data Improvements and New Capabilities for the Community Land Model Urban (CLMU).

The Community Land Model Urban (CLMU) is an urban parameterization developed to simulate urban climate within a global Earth System Model framework. This paper describes and evaluates parameterization and surface data improvements, and new capabilities that have been implemented since the initial release of CLMU in 2010 as part of version 4 of the Community Land Model (CLM4) and the Community Earth System Model (CESM®). These include: 1) an expansion of model capability to simulate multiple urban density classes within each model grid cell; 2) a more sophisticated and realistic building space heating and air conditioning submodel; 3) a revised global dataset of urban morphological, radiative, and thermal properties utilized by the model, including a tool that allows for generating future urban development scenarios, and 4) the inclusion of a module to simulate various heat stress indices. The model and data are evaluated using observed data from five urban flux tower sites and a global anthropogenic heat flux (AHF) dataset. Generally, the new version of the model simulates urban radiative and turbulent fluxes, surface temperatures, and AHF as well or better than the previous version. Significant improvements in the global and regional simulation of AHF are also demonstrated that are primarily due to the new building energy model. The new model is available as part of the public release of CLM5 and CESM2.0.

Inverse Modeling of Global and Regional Energy and Water Cycle Fluxes using Earth Observation Data

AbstractThe NASA Energy and Water Cycle Study (NEWS) climatology is a self-consistent coupled annual and seasonal cycle solution for radiative, turbulent, and water fluxes over Earth’s surface using Earth observation data covering 2000–09. Here we seek to improve the NEWS solution, particularly over the ocean basins, by considering spatial covariances in the observation errors (some evidence for which is found by comparing five turbulent flux products over the oceans) and by introducing additional horizontal transports from ocean reanalyses as weak constraints. By explicitly representing large error covariances between surface heat flux components over the major ocean basins we retain the flux contrasts present in the original data and infer additional heat losses over the North Atlantic Ocean, more consistent with a strong Atlantic overturning. This change does not alter the global flux balance but if only the errors in evaporation and precipitation are correlated then those fluxes experience larger adjustments (e.g., the surface latent heat flux increases to 85 ± 2 W m−2). Replacing SeaFlux v1 with J-OFURO v3 (Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations) ocean fluxes also leads to a considerable increase in the global latent heat loss as well as a larger North Atlantic heat loss. Furthermore, including a weak constraint on the horizontal transports of heat and freshwater from high-resolution ocean reanalyses improves the net fluxes over the North Atlantic, Caribbean Sea, and Arctic Ocean, without any impact on the global flux balances. These results suggest that better characterized flux uncertainties can greatly improve the quality of the optimized flux solution.

Canopy Resistance and Estimation of Evapotranspiration above a Humid Cypress Forest

This study presented a two-year data set of sensible heat and water vapor fluxes above a humid subtropical montane Cypress forest, located at 1650 m a.s.l. in northeastern Taiwan. The focuses of this study were to investigate (1) the diurnal and seasonal variations of canopy resistance and fluxes of sensible heat and water vapor above this forest; and (2) the mechanism of why a fixed canopy resistance could work when implementing the Penman–Monteith equation for diurnal hourly evapotranspiration estimation. Our results showed distinct seasonal variations in canopy resistance and water vapor flux, but on the contrary, the sensible heat flux did not change as much as the water vapor flux did with seasons. The seasonal variation patterns of the canopy resistance and water vapor flux were highly coupled with the meteorological factors. Also, the results demonstrated that a constant (fixed) canopy resistance was good enough for estimating the diurnal variation of evapotranspiration using Penman–Monteith equation. We observed a canopy resistance around 190 (s/m) for both the two warm seasons; and canopy resistances were around 670 and 320 (s/m) for the two cool seasons, respectively. In addition, our analytical analyses demonstrated that when the average canopy resistance is higher than 200 (s/m), the Penman–Monteith equation is less sensitive to the change of canopy resistance; hence, a fixed canopy resistance is suitable for the diurnal hourly evapotranspiration estimation. However, this is not the case when the average canopy resistance is less than 100 (s/m), and variable canopy resistances are needed. These two constraints (200 and 100) were obtained based on purely analytical analyses under a moderate meteorological condition (Rn = 600 W·m−2, RH = 60%, Ta = 20°C, U = 2 m·s−1) and a measurement height around two times of the canopy height.