TPHiPr: a long-term (1979–2020) high-accuracy precipitation dataset (1∕30°, daily) for the Third Pole region based on high-resolution atmospheric modeling and dense observations

Earth system science data, Journal Year: 2023, Volume and Issue: 15(2), P. 621 - 638

Published: Feb. 8, 2023

Abstract. Reliable precipitation data are highly necessary for geoscience research in the Third Pole (TP) region but still lacking, due to complex terrain and high spatial variability of here. Accordingly, this study produces a long-term (1979–2020) high-resolution (1/30∘, daily) dataset (TPHiPr) TP by merging atmospheric simulation-based ERA5_CNN with gauge observations from more than 9000 rain gauges, using climatologically aided interpolation random forest methods. Validation shows that TPHiPr is generally unbiased has root mean square error 5.0 mm d−1, correlation 0.76 critical success index 0.61 respect 197 independent gauges TP, demonstrating remarkably better widely used datasets, including latest generation reanalysis (ERA5-Land), state-of-the-art satellite-based (IMERG) multi-source datasets (MSWEP v2 AERA5-Asia). Moreover, can detect extremes compared these datasets. Overall, provides new accuracy which may have broad applications meteorological, hydrological ecological studies. The produced be accessed via https://doi.org/10.11888/Atmos.tpdc.272763 (Yang Jiang, 2022).

Language: Английский

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Comparison of Reanalysis and Observational Precipitation Datasets Including ERA5 and WFDE5

Atmosphere, Journal Year: 2021, Volume and Issue: 12(11), P. 1462 - 1462

Published: Nov. 5, 2021

Precipitation is a key component of the hydrological cycle and one most important variables in weather climate studies. Accurate reliable precipitation data are crucial for determining trends variability. In this study, eleven different datasets compared, six reanalysis five observational datasets, including ERA5 WFDE5 from ECMWF family, to quantify differences between widely used identify their particular strengths shortcomings. The comparisons focused on common time period 1983 through 2016 monthly, seasonal, inter-annual times scales regions representing regimes, i.e., Tropics, Pacific Inter Tropical Convergence Zone (ITCZ), Central Europe, South Asian Monsoon region. For analysis, satellite-gauge Global Climatology Project (GPCP-SG) as reference. comparison shows that ERA5-Land clear improvement over ERA-Interim show cases smaller biases than other (e.g., around 13% high bias Tropics compared 17% MERRA-2 36% JRA-55). agrees well with observations Europe region but underestimates very low rates Tropics. particular, tropical ocean remains challenging reanalyses three out four products overestimating Atlantic Indian Ocean.

Language: Английский

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EM-DAT: the Emergency Events Database

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Dec. 27, 2023

Abstract The Emergency Events Database (EM-DAT) compiles global disaster data resulting from both technological and natural hazards. It details the human economic impacts 1900 to present, with systematic recording since 1988. Serving humanitarian, risk reduction, academic sectors, EM-DAT's transition open access increasing climate change concerns have expanded its reach visibility. dataset, freely available for non-commercial use, is downloadable as an Excel file. categorized by hazard type standardized individual events each country. With over 26,000 unique entries, database populated through monitoring of textual documents their manual processing. collection validation processes involve daily checks predefined sources, searches additional periodic thematic updates. evolution content mirrors societal advancements in reporting. Besides these progresses, known inconsistencies biases quality been reported. article acknowledges issues, highlighting potential implications research decision-making.

Language: Английский

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ForestTemp – Sub‐canopy microclimate temperatures of European forests

Global Change Biology, Journal Year: 2021, Volume and Issue: 27(23), P. 6307 - 6319

Published: Oct. 3, 2021

Ecological research heavily relies on coarse-gridded climate data based standardized temperature measurements recorded at 2 m height in open landscapes. However, many organisms experience environmental conditions that differ substantially from those captured by these macroclimatic (i.e. free air) grids. In forests, the tree canopy functions as a thermal insulator and buffers sub-canopy microclimatic conditions, thereby affecting biological ecological processes. To improve assessment of climatic climate-change-related impacts forest-floor biodiversity functioning, high-resolution grids reflecting forest microclimates are thus urgently needed. Combining more than 1200 time series situ near-surface with topographical, variables machine learning model, we predicted mean monthly offset between 15 cm above surface free-air over period 2000-2020 spatial resolution 25 across Europe. This was used to evaluate difference microclimate macroclimate space seasons finally enabled us calculate annual temperatures for European understories. We found air temperatures, being average 2.1°C (standard deviation ± 1.6°C) lower summer 2.0°C higher (±0.7°C) winter Additionally, our maps expose considerable variation within landscapes, not gridded products. The provided will enable future model below-canopy processes patterns, well species distributions accurately.

Language: Английский

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G‐RUN ENSEMBLE: A Multi‐Forcing Observation‐Based Global Runoff Reanalysis

Water Resources Research, Journal Year: 2021, Volume and Issue: 57(5)

Published: April 29, 2021

Abstract River discharge is an Essential Climate Variable (ECV) and one of the best monitored components terrestrial water cycle. Nonetheless, gauging stations are distributed unevenly around world, leaving many white spaces on global freshwater resources maps. Here, we use a machine learning algorithm historical weather data to upscale sparse in situ river measurements. We provide reanalysis monthly runoff rates for periods covering decades past century at resolution 0.5° (about 55 km), with up 525 ensemble members based 21 different atmospheric forcing sets. This reconstruction, named Global RUNoff ENSEMBLE (G‐RUN ENSEMBLE), evaluated using independent observations from large basins benchmarked against other publicly available sets over period 1981–2010. The accuracy set observed flow not used model calibration found compare favorably state‐of‐the‐art hydrological simulations. G‐RUN estimates mean volume range between 3.2 × 10 4 3.8 km 3 yr −1 . ( https://doi.org/10.6084/m9.figshare.12794075 ) has wide applications, including regional assessments, climate change attribution studies, hydro‐climatic process studies as well evaluation, refinement models.

Language: Английский

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