Thornthwaite's formula is globally an optimum candidate for large-scale applications of potential evapotranspiration and aridity assessment at different climates and landscapes since it has lower data requirements compared to other methods and especially from the ASCE-standardized reference evapotranspiration (formerly FAO-56), which is the most data-demanding method and is commonly used as the benchmark method. The aim of the study is to develop a global database of local coefficients for correcting the formula of monthly Thornthwaite potential evapotranspiration (Ep) using as benchmark the ASCE-standardized reference evapotranspiration method (Er). The validity of the database will be verified by testing the hypothesis that a local correction coefficient, which integrates the local mean effect of wind speed, humidity, and solar radiation, can improve the performance of the original Thornthwaite formula. The database of local correction coefficients was developed using global gridded temperature, rainfall, and Er data of the period 1950–2000 at 30 arcsec resolution (∼ 1 km at Equator) from freely available climate geodatabases. The correction coefficients were produced as partial weighted averages of monthly Er/Ep ratios by setting the ratios' weight according to the monthly Er magnitude and by excluding colder months with monthly values of Er or Ep < 45 mm per month because their ratio becomes highly unstable for low temperatures. The validation of the correction coefficients was made using raw data from 525 stations of Europe; California, USA; and Australia including data up to 2020. The validation procedure showed that the corrected Thornthwaite formula Eps using local coefficients led to a reduction of RMSE from 37.2 to 30.0 mm m−1 for monthly step estimations and from 388.8 to 174.8 mm yr−1 for annual step estimations compared to Ep using as a benchmark the values of the Er method. The corrected Eps and the original Ep Thornthwaite formulas were also evaluated by their use in Thornthwaite and UNEP (United Nations Environment Program) aridity indices using as a benchmark the respective indices estimated by Er. The analysis was made using the validation data of the stations, and the results showed that the correction of the Thornthwaite formula using local coefficients increased the accuracy of detecting identical aridity classes with Er from 63 % to 76 % for the case of Thornthwaite classification and from 76 % to 93 % for the case of UNEP classification. The performance of both aridity indices using the corrected formula was extremely improved in the case of non-humid classes. The global database of local correction factors can support applications of reference evapotranspiration and aridity index assessment with the minimum data requirements (i.e., temperature) for locations where climatic data are limited. The global grids of local correction coefficients for the Thornthwaite formula produced in this study are archived in the PANGAEA database and can be assessed using the following link: https://doi.org/10.1594/PANGAEA.932638 (Aschonitis et al., 2021).
Abstract. Long-term global monitoring of terrestrial Gross Primary Production (GPP) is crucial for assessing ecosystem response to global climate change. In recent years and decades, great advances in estimating GPP on a global level have been made and many global GPP datasets have been published. These global data records are either based on observations from optical remote sensing, are upscaled from in situ measurements, or rely on process-based models. The different estimation approaches are well established within the scientific community but also exhibit significant discrepancies among each other. Here, we introduce the new VODCA2GPP dataset, which utilizes microwave remote sensing estimates of Vegetation Optical Depth (VOD) to estimate GPP on a global scale. VODCA2GPP is able to complement existing products with long-term GPP estimates covering the period 1988–2020. VODCA2GPP applies a previously developed carbon sink-driven approach (Teubner et al., 2019, 2021) to estimate GPP from the Vegetation Optical Depth Climate Archive (Zotta et al., in prep.; Moesinger et al., 2020), which merges VOD observations from multiple sensors into one long-running, coherent data record. VODCA2GPP was trained and evaluated against FLUXNET in situ observations of GPP and assessed against largely independent state-of-the art GPP datasets (MODIS GPP, FLUXCOM GPP, and GPP estimates from the TRENDY-v7 model ensemble). These assessments show that VODCA2GPP exhibits very similar spatial patterns compared to existing GPP datasets across all biomes but with a consistent positive bias. In terms of temporal dynamics, a high agreement was found for regions outside the humid tropics, with median correlations around 0.75. Concerning anomalies from the long-term climatology, VODCA2GPP correlates well with MODIS and TRENDY-v7 GPP (Pearson’s r: 0.53 and 0.61) but less with FLUXCOM GPP (Pearson’s r: 0.29). A trend analysis for the period 1988–2019 did not exhibit a significant trend in VODCA2GPP on a global scale but rather suggests regionally differing long-term changes in GPP. Significant similar increases of global GPP that were found for VODCA2GPP, MODIS GPP, and the TRENDY-v7 ensemble for the shorter overlapping observation period (2003–2015) supports the theory of elevated CO2 uptake potentially induced by increased atmospheric CO2 concentrations and the associated rising temperatures. The VODCA2GPP dataset is available at TU Data (https://doi.org/10.48436/1k7aj-bdz35; Wild et al., 2021).
This paper presents an updated estimation of the bottom-up global surface seawater dimethyl sulfide (DMS) climatology. This update, called DMS-Rev3, is the third of its kind and includes five significant changes from the last climatology, L11 (Lana et al., 2011), that was released about a decade ago. The first change is the inclusion of new observations that have become available over the last decade, creating a database of 873 539 observations leading to an ∼ 18-fold increase in raw data as compared to the last estimation. The second is significant improvements in data handling, processing, and filtering, to avoid biases due to different observation frequencies which result from different measurement techniques. Thirdly, we incorporate the dynamic seasonal changes observed in the geographic boundaries of the ocean biogeochemical provinces. The fourth change involves the refinement of the interpolation algorithm used to fill in the missing data. Lastly, an upgraded smoothing algorithm based on observed DMS variability length scales (VLS) helps to reproduce a more realistic distribution of the DMS concentration data. The results show that DMS-Rev3 estimates the global annual mean DMS concentration to be ∼ 2.26 nM (2.39 nM without a sea-ice mask), i.e., about 4 % lower than the previous bottom-up L11 climatology. However, significant regional differences of more than 100 % as compared to L11 are observed. The global sea-to-air flux of DMS is estimated at ∼ 27.1 TgS yr−1, which is about 4 % lower than L11, although, like the DMS distribution, large regional differences were observed. The largest changes are observed in high concentration regions such as the polar oceans, although oceanic regions that were under-sampled in the past also show large differences between revisions of the climatology. Finally, DMS-Rev3 reduces the previously observed patchiness in high productivity regions. The new climatology, along with the algorithm, can be found in the online repository: https://doi.org/10.17632/hyn62spny2.1 (Mahajan, 2021).
Abstract. We present results from the FAOSTAT agri-food systems emissions database, relative to 236 countries and territories and over the period 1990–2019. We find that in 2019, world-total food systems emissions were 16.5 billion metric tonnes (Gt CO2eq yr−1), corresponding to 31 % of total anthropogenic emissions. Of the agri-food systems total, global emissions within the farm gate –from crop and livestock production processes including on-farm energy use—were 7.2 Gt CO2eq yr−1; emissions from land use change, due to deforestation and peatland degradation, were 3.5 Gt CO2eq yr−1; and emissions from pre- and post-production processes –manufacturing of fertilizers, food processing, packaging, transport, retail, household consumption and food waste disposal—were 5.8 Gt CO2eq yr−1. Over the study period 1990–2019, agri-food systems emissions increased in total by 17 %, largely driven by a doubling of emissions from pre- and post-production processes. Conversely, the FAO data show that since 1990 land use emissions decreased by 25 %, while emissions within the farm gate increased only 9 %. In 2019, in terms of single GHG, pre- and post- production processes emitted the most CO2 (3.9 Gt CO2 yr−1), preceding land use change (3.3 Gt CO2 yr−1) and farm-gate (1.2 Gt CO2 yr−1) emissions. Conversely, farm-gate activities were by far the major emitter of methane (140 Mt CH4 yr−1) and of nitrous oxide (7.8 Mt N2O yr−1). Pre-and post- processes were also significant emitters of methane (49 Mt CH4 yr−1), mostly generated from the decay of solid food waste in landfills and open-dumps. The most important trend over the 30-year period since 1990 highlighted by our analysis is the increasingly important role of food-related emissions generated outside of agricultural land, in pre- and post-production processes along food supply chains, at all scales from global, regional and national, from 1990 to 2019. In fact, our data show that by 2019, food supply chains had overtaken farm-gate processes to become the largest GHG component of agri-food systems emissions in Annex I parties (2.2 Gt CO2eq yr−1). They also more than doubled in non-Annex I parties (to 3.5 Gt CO2eq yr−1), becoming larger than emissions from land-use change. By 2019 food supply chains had become the largest agri-food system component in China (1100 Mt CO2eq yr−1); USA (700 Mt CO2eq yr−1) and EU-27 (600 Mt CO2eq yr−1). This has important repercussions for food-relevant national mitigation strategies, considering that until recently these have focused mainly on reductions of non-CO2 gases within the farm gate and on CO2 mitigation from land use change. The information used in this work is available as open data at: https://zenodo.org/record/5615082 (Tubiello et al., 2021d). It is also available to users via the FAOSTAT database (FAO, 2021a), with annual updates.
Sedimentary charcoal records are widely used to reconstruct regional changes in fire regimes through time in the geological past. Existing global compilations are not geographically comprehensive and do not provide consistent metadata for all sites. Furthermore, the age models provided for these records are not harmonised and many are based on older calibrations of the radiocarbon ages. These issues limit the use of existing compilations for research into past fire regimes. Here, we present an expanded database of charcoal records, accompanied by new age models based on recalibration of radiocarbon ages using IntCal20 and Bayesian age-modelling software. We document the structure and contents of the database, the construction of the age models, and the quality control measures applied. We also record the expansion of geographical coverage relative to previous charcoal compilations and the expansion of metadata that can be used to inform analyses. This first version of the Reading Palaeofire Database contains 1676 records (entities) from 1480 sites worldwide. The database (RPDv1b – Harrison et al., 2021) is available at https://doi.org/10.17864/1947.000345.
