From: AlleyCat <katt@gmail.com>
Newsgroups: alt.global-warming,alt.fan.rush-limbaugh,can.politics,alt.politics.liberalism,alt.politics.democrats,alt.politics.usa.republican
Subject: Central Eurasia, An Area Larger Than Europe, Has Experienced Increased Snowfall And Colder Winters Over The Past 2 Decades
Date: Wed, 25 Jun 2025 18:15:02 -0500
Organization: AlleyCat Computing, Inc.
These changes were not predicted by models, which offer different
explanations; therefore, scientists don't know why it's happening.
Recent increase in snow cover as a contributing driver to autumn cooling in
central Eurasia
https://pbs.twimg.com/media/GsgCb23W8AA1bHO?format=jpg&name=medium
=====
Baofu Li*, Fangshu Dong, Lishu Lian, Tao Pan, Weijun Sun*, Bowen Sun, Yanfeng
Chen, Yunqian Wang, Yanhua Qin and Minghu Ding*
Published 7 May 2025 © 2025 The Author(s). Published by IOP Publishing Ltd
Environmental Research Letters, Volume 20, Number 5Citation Baofu Li et al
2025 Environ. Res. Lett. 20 054068DOI 10.1088/1748-9326/add02a
Abstract
In the context of global warming, autumn air temperatures in central Eurasia
have exhibited a cooling trend over the past two decades. However, the extent
to which snow cover contributes to the cooling remains unclear. This study
reveals that despite a general decrease in global snow cover extent, the
autumn snow cover percentage over central Eurasia has increased by 5.38% per
decade in the past two decades. Quantitative assessments indicate that the
contribution of this increase in snow cover to the observed cooling was 21.5%.
We also found that the increase in snow cover leads to a reduction in net
shortwave radiation, which is the primary mechanism of the cooling effect
induced by snow cover. This study advances our understanding of the evolution
of the global climate system and provides scientific support for addressing
climate change.
Export citation and abstractBibTeXRIS
Original content from this work may be used under the terms of the Creative
Commons Attribution 4.0 license. Any further distribution of this work must
maintain attribution to the author(s) and the title of the work, journal
citation and DOI.
Supplementary data
1. Introduction
Despite the unequivocal reality of global warming, extreme cold events occur
frequently worldwide (Lubitz et al 2024). For example, during the winter of
2022-2023, bomb cyclones or epic cold waves severely impacted the United
States, affecting more than 200 million people (Cuff 2023). Similarly, during
the autumn of 2022, China experienced frequent cold waves, with abrupt
temperature drops that adversely affected public transportation and
agricultural activities. This event was listed among the top ten weather and
climate events in China for 2022 by the China Meteorological Administration.
Recent studies (Li et al 2020, Tang et al 2022) have revealed that a cooling
trend has emerged in central Eurasia in autumn since 2004, although the
driving mechanisms remain unclear. While numerous studies have focused on
qualitatively addressing the coupling relationships between snow cover changes
in summer and spring and temperature variations in different regions of the
Northern Hemisphere (Preece et al 2023, Webster et al 2024), the quantitative
contribution of snow cover changes to cooling remains uncertain (Henderson et
al 2018, You et al 2020). Therefore, within the context of global warming,
quantitatively assessing the feedback effect of Eurasian snow cover changes on
autumn cooling can provide systematic insights for the early warning of
extreme weather and climate events, as well as for disaster prevention and
mitigation.
Approximately 98% of the Earth's seasonal snow cover is located in the
Northern Hemisphere, with Eurasian winter snow accounting for 60%-65% of the
Northern Hemisphere's total snow cover. In this study, central Eurasia refers
to the vast area generally defined by 40°-65° N and 50°-130° E (figure 1).
Variations in snow cover in this region have important implications for
regional and global climates (Peng et al 2024, Mekonnen et al 2025).
Figure 1. Location of central Eurasia and the meteorological stations.
Building on previous research advancements, the primary objectives of this
study are as follows: (1) to localize the Weather Research and Forecasting
(WRF) model for Eurasia and validate its simulation performance; (2) to
analyze the spatiotemporal characteristics of autumn air temperature changes
in central Eurasia from 2004 to 2020 based on WRF model simulations; (3) to
examine the spatiotemporal variations in the snow cover percentage (SCP) and
snow cover frequency (SCF) via the Interactive Multisensor Snow and Ice
Mapping System (IMS) snow and ice products; (4) to quantitatively assess the
contribution of snow cover changes to autumn cooling via control and
sensitivity experiments with the WRF model; and (5) to elucidate the
mechanisms by which snow cover changes influence autumn air temperature
fluctuations from the perspectives of radiative components and energy fluxes.
The findings of this study can provide significant insights into predicting
future regional climate change trends and formulating adaptive strategies.
2. Data and methods
2.1. Data
Snow cover data from 1 January 2004, to 31 December 2020, were obtained from
the IMS product provided by the National Snow and Ice Data Center. The data
have a spatial resolution of 4 km and a temporal resolution of 1 d, offering
cloud-free daily snow cover information for the Northern Hemisphere (Helfrich
et al 2007, Frei and Lee 2010).
To validate the reliability of snow cover variation results, the autumn
central Eurasian SCP presented in this study was compared with the Eurasian
snow cover extent dataset published by the Rutgers University Global Snow Lab
(https://snowcover.org, Robinson and Frei 2000, Estilow et al 2015).
The ERA5 reanalysis dataset was used as the driving data for the WRF model.
ERA5 data (Hersbach et al 2020), released by the European Center for Medium-
Range Weather Forecasts, have a spatial resolution of 0.25° × 0.25° and a
temporal resolution of 1 h. These data have been widely used in temperature-
related research (Ou et al 2020, Yang et al 2021). The study period covers
each year from 31 August, 00:00 UTC, to 1 December, 00:00 UTC, from 2003 to
2020.
To evaluate the reliability of the WRF model simulation results, the
relationship between the simulated daily air temperatures and the observed
meteorological station data was analyzed. Air temperature data from
meteorological stations were sourced from the National Centers for
Environmental Information. Stations with more than 20% of missing temperature
data for a given month were excluded. Ultimately, 408 daily meteorological
observation stations within the study area (40-65° N, 50-130° E) during autumn
from 2004 to 2020 were selected for analysis (figure 1).
To further validate the temperature simulation results from the WRF
sensitivity experiments, the relationship between the Climate Research Unit
(CRU) temperature data and the simulated temperature was analyzed. The CRU
temperature data, provided by the CRU of the University of East Anglia, had a
spatial resolution of 0.5° × 0.5°. The study utilized CRU TS v4.05 (CRU Time
Series version 4.05) for the period 2004-2020
(https://crudata.uea.ac.uk/cru/data/hrg/).
The annual land use data for the WRF model from 2003 to 2020 were derived from
the MODIS Land Cover Type product (MCD12C1), with a spatial resolution of
0.05° × 0.05°.
[...]
https://iopscience.iop.org/article/10.1088/1748-9326/add02a/meta
https://content.cld.iop.org/journals/1748-
9326/20/5/054068/revision3/erladd02af1_hr.jpg
https://content.cld.iop.org/journals/1748-
9326/20/5/054068/revision3/erladd02af1_lr.jpg
https://content.cld.iop.org/journals/1748-
9326/20/5/054068/revision3/erladd02af2_hr.jpg
https://content.cld.iop.org/journals/1748-
9326/20/5/054068/revision3/erladd02af2_lr.jpg
https://content.cld.iop.org/journals/1748-
9326/20/5/054068/revision3/erladd02af3_hr.jpg
https://content.cld.iop.org/journals/1748-
9326/20/5/054068/revision3/erladd02af3_lr.jpg
https://creativecommons.org/licenses/by/4.0/
https://crudata.uea.ac.uk/cru/data/hrg/
https://iopscience.iop.org/article/10.1088/1748-9326/adc74e
https://iopscience.iop.org/article/10.1088/1748-9326/adcc43
https://iopscience.iop.org/article/10.1088/1748-9326/add02a/data
https://iopscience.iop.org/article/10.1088/1748-9326/add02a/meta#erladd02abib1
https://iopscience.iop.org/article/10.1088/1748-
9326/add02a/meta#erladd02abib10
https://iopscience.iop.org/article/10.1088/1748-
9326/add02a/meta#erladd02abib11
https://iopscience.iop.org/article/10.1088/1748-
9326/add02a/meta#erladd02abib12
https://iopscience.iop.org/article/10.1088/1748-
9326/add02a/meta#erladd02abib13
https://iopscience.iop.org/article/10.1088/1748-
9326/add02a/meta#erladd02abib14
https://iopscience.iop.org/article/10.1088/1748-
9326/add02a/meta#erladd02abib15
https://iopscience.iop.org/article/10.1088/1748-
9326/add02a/meta#erladd02abib16
https://iopscience.iop.org/article/10.1088/1748-
9326/add02a/meta#erladd02abib17
https://iopscience.iop.org/article/10.1088/1748-
9326/add02a/meta#erladd02abib18
https://iopscience.iop.org/article/10.1088/1748-
9326/add02a/meta#erladd02abib19
https://iopscience.iop.org/article/10.1088/1748-9326/add02a/meta#erladd02abib2
https://iopscience.iop.org/article/10.1088/1748-
9326/add02a/meta#erladd02abib20
https://iopscience.iop.org/article/10.1088/1748-
9326/add02a/meta#erladd02abib21
https://iopscience.iop.org/article/10.1088/1748-
9326/add02a/meta#erladd02abib22
https://iopscience.iop.org/article/10.1088/1748-
9326/add02a/meta#erladd02abib23
https://iopscience.iop.org/article/10.1088/1748-
9326/add02a/meta#erladd02abib24
https://iopscience.iop.org/article/10.1088/1748-
9326/add02a/meta#erladd02abib25
https://iopscience.iop.org/article/10.1088/1748-
9326/add02a/meta#erladd02abib26
https://iopscience.iop.org/article/10.1088/1748-
9326/add02a/meta#erladd02abib27
https://iopscience.iop.org/article/10.1088/1748-
9326/add02a/meta#erladd02abib28
https://iopscience.iop.org/article/10.1088/1748-
9326/add02a/meta#erladd02abib29
https://iopscience.iop.org/article/10.1088/1748-9326/add02a/meta#erladd02abib3
https://iopscience.iop.org/article/10.1088/1748-9326/add02a/meta#erladd02abib4
https://iopscience.iop.org/article/10.1088/1748-9326/add02a/meta#erladd02abib5
https://iopscience.iop.org/article/10.1088/1748-9326/add02a/meta#erladd02abib6
https://iopscience.iop.org/article/10.1088/1748-9326/add02a/meta#erladd02abib7
https://iopscience.iop.org/article/10.1088/1748-9326/add02a/meta#erladd02abib8
https://iopscience.iop.org/article/10.1088/1748-9326/add02a/meta#erladd02abib9
https://iopscience.iop.org/article/10.1088/1748-9326/add02a/meta#erladd02af1
https://iopscience.iop.org/article/10.1088/1748-9326/add02a/meta#erladd02af2
https://iopscience.iop.org/article/10.1088/1748-9326/add02a/meta#erladd02af3
https://iopscience.iop.org/article/10.1088/1748-9326/add02a/pdf
https://iopscience.iop.org/export?type=article&doi=10.1088/1748-
9326/add02a&exportFormat=iopexport_bib&exportType=abs&navsubmit=Export+abstrac
t
https://iopscience.iop.org/export?type=article&doi=10.1088/1748-
9326/add02a&exportFormat=iopexport_ris&exportType=abs&navsubmit=Export+abstrac
t
https://iopscience.iop.org/issue/1748-9326/20/5
https://iopscience.iop.org/journal/1748-9326
https://iopscience.iop.org/journal/1748-9326/page/submission-options
https://iopscience.iop.org/volume/1748-9326/20
https://snowcover.org/
https://twitter.com/share?url=https%3A%2F%2Fdoi.org%2F10.1088%2F1748-9326%
2Fadd02a&text=Recent+increase+in+snow+cover+as+a+contributing+driver+to+autumn
+cooling+in+central+Eurasia&via=IOPenvironment
https://www.altmetric.com/details.php?domain=iopscience.iop.org&citation_id=
177695404
https://www.facebook.com/sharer.php?u=https%3A%2F%2Fdoi.org%2F10.1088%2F1748-
9326%2Fadd02a
https://www.mendeley.com/import/?doi=10.1088%2F1748-9326%2Fadd02a