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In this work, long-term trends in convective parameters are compared between ERA5, MERRA2, and observed rawinsonde profiles over Europe and the United States including surrounding areas. A 39-year record (1980–2018) with 2.07 million quality-controlled measurements from 84 stations at 0000 and 1200 UTC is used for the comparison, along with collocated reanalysis profiles. Overall, reanalyses provide similar signals to observations, but ERA5 features lower biases. Over Europe, agreement in the trend signal between rawinsondes and the reanalyses is better, particularly with respect to instability (lifted index), low-level moisture (mixing ratio) and 0–3 km lapse rates as compared to mixed trends in the United States. However, consistent signals for all three datasets and both domains are found for robust increases in convective inhibition (CIN), downdraft CAPE (DCAPE) and decreases in mean 0–4 km relative humidity. Despite differing trends between continents, the reanalyses capture well changes in 0–6 km wind shear and 1–3 km mean wind with modest increases in the United States and decreases in Europe. However, these changes are mostly insignificant. All datasets indicate consistent warming of almost the entire tropospheric profile, which over Europe is the fastest near-ground, while across the Great Plains generally between 2–3 km above ground level, thus contributing to increases in CIN. Results of this work show the importance of intercomparing trends between various datasets, as the limitations associated with one reanalysis or observations may lead to uncertainties and lower our confidence in how parameters are changing over time.
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How Accurate Are Modern Atmospheric Reanalyses for the Data-Sparse Tibetan Plateau Region?
More than 6000 independent radiosonde observations from three major Tibetan Plateau experiments during the warm seasons (May–August) of 1998, 2008, and 2015–16 are used to assess the quality of four leading modern atmospheric reanalysis products (CFSR/CFSv2, ERA-Interim, JRA-55, and MERRA-2), and the potential impact of satellite data changes on the quality of these reanalyses in the troposphere over this data-sparse region. Although these reanalyses can reproduce reasonably well the overall mean temperature, specific humidity, and horizontal wind profiles against the benchmark independent sounding observations, they have nonnegligible biases that can be potentially bigger than the analysis-simulated mean regional climate trends over this region. The mean biases and mean root-mean-square errors of winds, temperature, and specific humidity from almost all reanalyses are reduced from 1998 to the two later experiment periods. There are also considerable differences in almost all variables across different reanalysis products, though these differences also become smaller during the 2008 and 2015–16 experiments, in particular for the temperature fields. The enormous increase in the volume and quality of satellite observations assimilated into reanalysis systems is likely the primary reason for the improved quality of the reanalyses during the later field experiment periods. Besides differences in the forecast models and data assimilation methodology, the differences in performance between different reanalyses during different field experiment periods may also be contributed by differences in assimilated information (e.g., observation input sources, selected channels for a given satellite sensor, quality-control methods).
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- Award ID(s):
- 1712290
- PAR ID:
- 10158511
- Date Published:
- Journal Name:
- Journal of Climate
- Volume:
- 32
- Issue:
- 21
- ISSN:
- 0894-8755
- Page Range / eLocation ID:
- 7153 to 7172
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1175/JCLI-D-18-0705.1
