You, Q., Z. Cai, F. Wu, H. W. Chen, D. Chen, and J. Cohen (2020):
Arctic warming revealed by multiple CMIP6 models:
Evaluation of historical simulations and quantification of future projection uncertainties.
Under review at Journal of Climate.
Lai, H.-W., H. W. Chen, J. Kukulies, T. Ou, and D. Chen (2020):
Regionalization of seasonal precipitation over the Tibetan Plateau and associated large-scale
Under review at Journal of Climate.
Fang, M., X. Li, D. Chen, and H. W. Chen (2020):
Arctic amplification over the past millennium mainly driven by
internal climate variability.
Under preparation for resubmission.
Kaminski, T., M. Scholze, P. Rayner, M. Voßbeck, M. Buchwitz, M. Reuter,
W. Knorr, H. W. Chen, A. Agusti-Panareda, A. Löscher, and Y. Meijer (2020):
Atmospheric CO2 observations from space can support national inventories.
Under revision at Nature Communications.
Cohen, J., X. Zhang, J. Francis, T. Jung, R. Kwok, J. Overland, T. Ballinger,
U. S. Bhatt, H. W. Chen, D. Coumou, S. Feldstein, D. Handorf, G. Henderson,
M. Ionita, M. Kretschmer, F. Laliberte, S. Lee, H. W. Linderholm, W. Maslowski,
Y. Peings, K. Pfeiffer, I. Rigor, T. Semmler, J. Stroeve, P. C. Taylor,
S. Vavrus, T. Vihma, S. Wang, M. Wendisch, Y. Wu, and J. Yoon (2020):
Divergent consensus on the influence of Arctic amplification on mid-latitude severe winter weather.
Nature Climate Change,
The Arctic has warmed more than twice as fast as the global average since the late twentieth century,
a phenomenon known as Arctic amplification (AA).
there have been considerable advances in understanding the physical contributions to AA,
and progress has been made in understanding the mechanisms that link it to midlatitude weather variability.
Observational studies overwhelmingly support that AA is contributing to winter continental cooling.
Although some model experiments support the observational evidence,
most modelling results show little connection between AA and severe midlatitude weather or suggest the export of excess heating from the Arctic to lower latitudes.
Divergent conclusions between model and observational studies,
and even intramodel studies,
continue to obfuscate a clear understanding of how AA is influencing midlatitude weather.
This study systematically examines the regional uncertainties and biases in carbon dioxide (CO2)mole fractions
from two of the state‐of‐the‐art global CO2 analysis products,
the Copernicus Atmosphere Monitoring Service (CAMS) real‐time atmospheric analysis from the European Centre for Medium‐Range Weather Forecasts (ECMWF)
and the CarbonTracker Near‐Real‐Time (CT‐NRT) reanalysis from the National Oceanic and Atmospheric Administration (NOAA),
by evaluation against hundreds of hours of airborne in situ measurements from the summer 2016 and winter 2017 Atmospheric Carbon and Transport (ACT)‐America field campaigns.
Both the CAMS and CT‐NRT analyses agree reasonably well with the independent ACT‐America airborne CO2 measurements in the free troposphere,
with root‐mean‐square deviations (RMSDs) between analyses and observations generally between 1 and 2 ppm
but show considerably larger uncertainties in the atmospheric boundary layer where the RMSDs exceed 8 ppm in the lowermost 1 km of the troposphere in summer.
There are strong variations in accuracy and bias between seasons,
and across three different subregions in the United States
(Mid‐Atlantic, Midwest, and South),
with the largest uncertainties in the Mid‐Atlantic region in summer.
the RMSDs of the CAMS and CT‐NRT analyses against airborne data are comparable to each other and largely consistent with the differences between the two analyses.
The current study provides uncertainty estimates for both analysis products over North America and suggests that these two independent estimates can be used to approximate regional CO2 analysis uncertainties.
Both statistics are important in future studies in quantifying the uncertainties in regional CO2 mole fraction and flux estimates.
Inference of CO2 surface fluxes using atmospheric CO2 observations in atmospheric inversions depends critically on accurate representation of atmospheric transport.
Here we characterize regional‐scale CO2 transport uncertainties due to uncertainties in meteorological fields using a mesoscale atmospheric model and an ensemble of simulations with flow‐dependent transport errors.
During a 1‐month summer period over North America,
transport uncertainties yield an ensemble spread in instantaneous CO2 at 100 m above ground level comparable to the CO2 uncertainties resulting from 48% relative uncertainty in 3‐hourly natural CO2 fluxes.
Temporal averaging reduces transport uncertainties but increases the influence of CO2 uncertainties from the lateral boundaries.
The influence of CO2 background uncertainties is especially large for column‐averaged CO2.
These results suggest that transport errors and CO2 background errors limit regional atmospheric inversions at two distinct timescales
and that the error characteristics of transport and background errors should guide the design of regional inversion systems.
Using Arctic sea ice concentration derived from passive microwave satellite observations in autumn and early winter over the 1979–2014 period,
the Arctic region was objectively classified into several smaller regions based on the interannual sea ice variability through self‐organizing map analyses.
The trend in regional sea ice extent (RSIE) in each region was removed using an adaptive,
and nonstationary method called Ensemble Empirical Mode Decomposition,
which captures well the accelerating decline of Arctic RSIEs in recent decades.
Although the linear trend in RSIE is negative in all regions in both seasons,
there are marked differences in RSIE trends and variability between regions,
with the largest negative trends found during autumn in the Beaufort Sea,
the Barents‐Kara Seas,
and the Laptev‐East Siberian Seas.
Winter weather patterns associated with the nonlinearly detrended RSIEs show distinct features for different regions and tend to be better correlated with the autumn than early winter RSIE anomalies.
Sea ice losses in the Beaufort Sea and the Barents‐Kara Seas are both associated with a cooling of Eurasia,
but in the former case the circulation anomaly is reminiscent of a Rossby wave train,
whereas in the latter case the pattern projects onto the negative phase of the Arctic Oscillation.
These results highlight the nonuniform changes in Arctic sea ice and suggest that regional sea ice variations may play a crucial role for the winter weather patterns.
The significance and robustness of the link between Arctic sea ice loss and changes in midlatitude weather patterns is investigated through a series of model simulations from the Community Atmosphere Model, version 5.3,
with systematically perturbed sea ice cover in the Arctic.
Using a large ensemble of 10 sea ice scenarios and 550 simulations,
it is found that prescribed Arctic sea ice anomalies produce statistically significant changes for certain metrics of the midlatitude circulation but not for others.
the significant midlatitude circulation changes do not scale linearly with the sea ice anomalies and are not present in all scenarios,
indicating that the remote atmospheric response to reduced Arctic sea ice can be statistically significant under certain conditions but is generally nonrobust.
Shifts in the Northern Hemisphere polar jet stream and changes in the meridional extent of upper-level large-scale waves due to the sea ice perturbations are generally small and not clearly distinguished from intrinsic variability.
Reduced Arctic sea ice may favor a circulation pattern that resembles the negative phase of the Arctic Oscillation and may increase the risk of cold outbreaks in eastern Asia by almost 50%,
but this response is found in only half of the scenarios with negative sea ice anomalies.
In eastern North America the frequency of extreme cold events decreases almost linearly with decreasing sea ice cover.
This study’s finding of frequent significant anomalies without a robust linear response suggests interactions between variability and persistence in the coupled system,
which may contribute to the lack of convergence among studies of Arctic influences on midlatitude circulation.
The Barents Oscillation (BO) is an anomalous wintertime atmospheric circulation pattern in the Northern Hemisphere that has been linked to the meridional flow over the Nordic Seas.
There are speculations that the BO has important implications for the Arctic climate; however,
it has also been suggested that the pattern is an artifact of Empirical Orthogonal Function (EOF) analysis due to an eastward shift of the Arctic Oscillation/North Atlantic Oscillation (AO/NAO).
In this study,
EOF analyses are performed to show that a robust pattern resembling the BO can be found during different time periods,
even when the AO/NAO is relatively stationary. This "BO" has a high and stable temporal correlation with the geostrophic zonal wind over the Barents Sea,
while the contribution from the AO/NAO is small.
The surface air temperature anomalies over the Barents Sea are closely associated with this mode of climate variability.
The Köppen climate classification was developed based on the empirical relationship between climate and vegetation.
This type of climate classification scheme provides an efficient way to describe climatic conditions defined by multiple variables and their seasonalities with a single metric.
Compared with a single variable approach,
the Köppen classification can add a new dimension to the description of climate variation.
it is generally accepted that the climatic combinations identified with the Köppen classification are ecologically relevant.
The classification has therefore been widely used to map geographic distribution of long term mean climate and associated ecosystem conditions.
Over the recent years, there has also been an increasing interest in using the classification to identify changes in climate and potential changes in vegetation over time.
These successful applications point to the potential of using the Köppen classification as a diagnostic tool to monitor changes in the climatic condition over various time scales.
This work used a global temperature and precipitation observation dataset to reveal variations and changes of climate over the period 1901–2010,
demonstrating the power of the Köppen classification in describing not only climate change,
but also climate variability on various temporal scales.
It is concluded that the most significant change over 1901–2010 is a distinct areal increase of the dry climate (B) accompanied by a significant areal decrease of the polar climate (E) since the 1980s.
The areas of spatially stable climate regions for interannual and interdecadal variations are also identified,
which have practical and theoretical implications.
Cohen, J., X. Zhang, J. Francis, T. Jung, R. Kwok, J. Overland, P. C. Taylor,
S. Lee, F. Laliberte, S. Feldstein, W. Maslowski, G. Henderson, J. Stroeve,
D. Coumou, D. Handorf, T. Semmler, T. Ballinger, M. Hell, M. Kretschmer,
S. Vavrus, M. Wang, S. Wang, Y. Wu, T. Vihma, U. Bhatt, M. Ionita,
H. Linderholm, I. Rigor, C. Routson, D. Singh, M. Wendisch, D. Smith,
J. Screen, J. Yoon, Y. Peings, H. Chen, and R. Blackport (2018):
Arctic change and possible influence on mid-latitude climate and weather: A US CLIVAR white paper.
US CLIVAR Report 2018-1,