Hans Chen

Postdoctoral Fellow in the Department of Physical Geography and Ecosystem Science, Lund University

Portrait
  • Hans W. Chen
  • Postdoctoral Fellow
  • Department of Physical Geography and Ecosystem Science
  • Lund University

Introduction

I'm an atmospheric and climate scientist interested in climate variability and change, atmospheric dynamics, terrestrial carbon cycle dynamics, and applying data assimilation and machine learning techniques for geophysical problems. My current research focuses on developing data assimilation and inverse techniques to estimate regional-scale sources and sinks of CO2 using primarily satellite observations. Such methods are useful both to study the natural carbon cycle and to provide independent estimates of human fossil fuel emissions.

I'm working as a postdoc in the Department of Physical Geography and Ecosystem Science at Lund University in Sweden.

Education

2013–2018 Ph.D. in Meteorology and Atmospheric Science, Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania, United States.
2010–2012 M.S. in Atmospheric Sciences, Oceanography and Climate, Department of Meteorology, Stockholm University, Stockholm, Sweden.
2007–2010 B.S. in Meteorology, Department of Meteorology, Stockholm University, Stockholm, Sweden.

More information can be found in my CV (PDF).

Research interests

  • Climate variability and change
  • Large-scale atmospheric dynamics
  • Data assimilation and parameter estimation
  • Inverse modeling of greenhouse gas fluxes

My research aims to improve our understanding of the climate system by combining information from observations and numerical models using innovative methods based on data assimilation, Bayesian inference, statistics, and machine learning techniques. I'm particularly interested processes that affect the weather and climate we experience on daily to multidecadal time scales. At the moment my research focuses on two topics: (1) Arctic amplification and linkages to mid-latitude weather and climate, and (2) data assimilation and inverse methods to estimate regional-scale CO2 sources and sinks.

Research projects

Data assimilation and inverse methods to estimate CO2 sources and sinks

ACT-America logo
More about ACT-America
Satellite monitoring human fossil fuel emissions
More about CHE

I'm currently involved with the CO2 Human Emissions (CHE) project, which is an initiative lead by ECMWF and comprised of 22 European partners to explore the development of a European system to monitor human-induced CO2 emissions across the world. My role in CHE is to evaluate how enhanced space-borne and in situ observations can improve top-down quantification of fossil fuel CO2 emissions.

Previously I worked on the NASA Atmospheric Carbon and Transport - America (ACT-America) mission, which aims at improving our understanding of atmospheric transport uncertainties and regional-scale sources and sinks of CO2 and methane through targeted airborne campaigns and inverse modeling.

Arctic amplification and linkages to mid-latitude weather and climate

Spatial pattern of the Barents Oscillation
More about the Barents Oscillation

The Arctic region is warming more than twice as fast as the rest of the world, with dramatic changes in the Arctic climate system including a rapid decline of Arctic sea ice. Some studies have suggested that the changes in the Arctic climate can cause systematic shifts in mid-latitude weather and climate, such as colder winters over the continents, known as the "Warm Arctic Cool Continents" pattern. I study Arctic climate change, Arctic amplification and linkages to mid-latitude weather and climate using a combination of observations, numerical modeling, and advanced statistical methods.

Related pages

Barents Oscillation

Quantifying climate variation and change

Worldmap of Köppen types
More about the Köppen climate classification

The climate has undoubtedly changed in the recent decades, but how to quantify this change in a useful way is not always straightforward. In my research, I use different metrics to quantify and diagnose climate change and climate variations.

Related pages

Köppen climate classification

Peer-reviewed

2020

  • Fang, M., X. Li, D. Chen, and H. W. Chen (2020): Arctic amplification over the past millennium mainly driven by internal climate variability. Nature Communications, under review.
  • 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. Nature Communications, under revision.
  • 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, 10, 20–29, doi:10.1038/s41558-019-0662-y.

2019

  • Chen, H. W., L. N. Zhang, F. Zhang, T. Lauvaux, K. J. Davis, S. Pal, B. Gaudet, and J. P. DiGangi (2019): Evaluation of regional CO2 mole fractions in the ECMWF CAMS real-time atmospheric analysis and NOAA CarbonTracker Near-Real Time reanalysis with airborne observations from ACT-America field campaigns. Journal of Geophysical Research–Atmospheres, 124, 8119–8133, doi:10.1029/2018JD029992.
  • Chen, H. W., F. Zhang, T. Lauvaux, K. J. Davis, S. Feng, M. P. Butler, and R. B. Alley (2019): Characterization of regional-scale CO2 transport uncertainties in an ensemble with flow-dependent transport errors. Geophysical Research Letters, 46, 4049–4058, doi:10.1029/2018GL081341.

2016

  • Chen, H. W., R. B. Alley, and F. Zhang (2016): Interannual Arctic sea ice variability and associated winter weather patterns: A regional perspective for 1979–2014. Journal of Geophysical Research–Atmospheres, 121, 14,433–14,455, doi:10.1002/2016JD024769.
  • Chen, H. W., F. Zhang, and R. B. Alley (2016): The robustness of midlatitude weather pattern changes due to Arctic sea ice loss. Journal of Climate, 29, 7831–7849, doi:10.1175/JCLI-D-16-0167.1.

2013

  • Chen, H. W., Q. Zhang, H. Körnich, and D. Chen (2013): A robust mode of climate variability in the Arctic: The Barents Oscillation. Geophysical Research Letters, 40, 2856–2861, doi:10.1002/grl.50551.
  • Chen, D. and H. W. Chen (2013): Using the Köppen classification to quantify climate variation and change: An example for 1901–2010. Environmental Development, 6, 69–79, doi:10.1016/j.envdev.2013.03.007.

Other publications

  • Ying, Y., X. Chen, Y. Zhang, M. Minamide, R. Nystrom, H. Chen, J. Poterjoy, C. Melhauser, Y. Weng, Z. Meng, A. Aksoy, F. Zhang (2018): PSU WRF EnKF/4DVar Hybrid Regional Data Assimilation System: Technical Notes.
  • 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, 41pp, doi:10.5065/D6TH8KGW.

Other pages

Data

Software

Links