Department of Meteorology

CLIMATE Analysis and Modeling


Faculty

Phone

E-mail

Arkin, Phillip 301-405-2147 parkin@essic.umd.edu
Baer, Ferdinand 301-405-5387
Berbery, Ernesto 301-405-5351
Busalacchi, Antonio 301-405-5599 tonyb@essic.umd.edu
Carton, James 301-405-5365
Chepurin, Gennady 301-405-5369
Fournier, Aime 303-497-1614
Grodsky, Semyon 301-405-5330
Jin, Menglin 301-405-8833
Joseph, Renu 301-405-1270
Kalnay, Eugenia 301-405-5370
Kirk-Davidoff, Daniel 301-405-5386

Faculty

Phone

E-mail

Lau, William 301-614-6332 William.K.Lau@nasa.gov
Lee, Hai-Tien 301-405-0494 lee@essic.umd.edu
Li, Zhangqing 301-405-6699
Murtugudde, Raghu 301-314-2622 ragu@essic.umd.edu
Nigam, Sumant 301-405-5381
Pickering, Kenneth 301-405-7639
Rasmusson, Eugene 301-405-5376
Ruiz-Barradas, Alfredo 301-405-0160
Vernekar, Anandu 301-405-5385
Vinnikov, Konstantin 301-405-5382
Yoon, Jinho 301-405-7567
Zeng, Ning 301-405-5377

Climate analysis and modeling research seeks to refine our understanding of the functioning of the climate system from statistical and dynamical analysis of earth observations, and from numerical modeling of ocean-atmosphere-land-cryosphere interactions. Physical and biophysical interactions operative in past and present climates are targeted.

Climate analysis has recently focused on differentiating natural variability from anthropogenic climate change. This has spurred detection of the global warming fingerprints in surface temperature and hydroclimate as well as diagnosis of the structure and mechanisms of seasonal and interannual climate variability. Notable examples of the latter include the intriguing seasonal-cycles in the eastern tropical Pacific and Atlantic basins with coldest SSTs in the Northern summer/fall, and the well-known large-scale patterns of recurrent interannual variability – El Nino Southern Oscillation, North Atlantic Oscillation, North Pacific Oscillation, and Pacific Decadal variability.

Climate model assessment is a recurring theme: Simulations produced by the state-of-the-art climate system models are scrutinized to assess the realism of the circulation and hydroclimate variability patterns. Representation of the atmospheric water-cycle and extreme hydrologic events (droughts and floods) in both regional and global reanalysis data sets and model simulations is a special focus.

Climate modeling activities target interactions of the climate system components: ocean-atmosphere, atmosphere-land-surface (including vegetation), physical-biochemical (carbon-cycle), and biophysical feedback. Diagnostic modeling is used in investigating the dynamical and thermodynamical interactions occurring in the troposphere and the troposphere-stratosphere region.

Climate research at Maryland addresses key problems in:

    Global Change

  • Global warming detection: Trends in sea-ice, snow cover, and climate variability (Vinnikov)
  • Seasonal and diurnal cycles of climate trends (Vinnikov)
  • Impact of urban and land-use changes on climate trends (Kalnay)
  • Estimation of global and regional land-surface temperature trends from AVHRR (Jin)
  • Analysis and modeling of climate sensitivity to greenhouse gas concentrations (Lau, Kirk-Davidoff)
  • Sampling issues in satellite climate monitoring (Kirk-Davidoff)
  • Coupled atmosphere-land-vegetation modeling of Sahelian climate (Lau, Zeng)
  • Carbon cycle and climate change: physical-biochemical interactions (Murtugudde, Zeng)
  • Bio-climate feedbacks (Murtugudde)
  • Earth System Modeling: Past, present, and future climates (Zeng, Murtugudde, Busalacchi)
    Atmospheric & Oceanic Reanalyses
  • Development of Global and Regional NCEP Reanalyses (Kalnay)
  • Ocean Data Assimilation analysis of the global upper ocean 1950-1995 (Carton)
  • Diagnosis of 3D diabatic heating from ECMWF & NCEP Reanalyses (Nigam)
    Hydroclimate Studies
  • Water & energy cycles and land-surface interactions (Arkin, Berbery, Jin, Lau, Nigam, Rasmusson, Ruiz-Barradas)
  • Detection and prediction of urban effects on water and energy cycles (Jin)
  • Analysis of global soil moisture variability and its remote sensing (Vinnikov, Zeng, Yoon)
  • Surface/atmosphere radiative fluxes: Diagnosis; Land/ocean energy budgets and LDAS (Pinker, Berbery, Ruiz-Barradas)
  • US Droughts: Initiation, Maintenance, Linkage with Pacific SSTs (Kalnay, Rasmusson, Nigam, Ruiz-Barradas)
  • Asian & African Droughts (Zeng, Lau, Pinker, Yoon)
    Ocean-Atmosphere Interaction
  • Analysis and modeling of ENSO air-sea interactions (Busalacchi, Carton, Murtugudde, Nigam, Rasmusson, Zeng)
  • Evolution of eastern tropical Pacific climate: ocean-atmosphere coupling & stratus clouds (Nigam, Murtugudde)
  • Analysis/modeling of tropical Atlantic variability (Busalacchi, Carton, Chepurin, Grodsky, Murtugudde, Nigam, Ruiz-Barradas, Zeng)
  • Diagnosis and modeling of mid-latitude air-sea interaction (Kalnay, Nigam)
    Monsoons
  • Dynamical modeling of Asian summer-monsoon variability, including its linkage with ENSO (Lau, Vernekar, Nigam, Zeng, Yoon)
  • Mesoscale and multiscale modeling of monsoon systems (Berbery, Fox-Rabinovitz)
  • Seasonal evolution and interannual variability of North American and South American Monsoons (Berbery, Nigam, Zeng, Yoon, Lau)
    Extratropical Interannual Variability
  • Dynamical simulation of seasonal climate anomalies (Joseph, Nigam)
  • Structure and dynamics of stormtrack variability (Berbery, Nigam)
  • Analysis of North Atlantic Oscillation (NAO) structure and dynamics (Nigam, Joseph)
  • Excitation and forcing of PNA and NPO variability (Nigam)
    Clouds and Radiation
  • Analysis of cloud-radiation interactions (Li, Lau, Lee)
  • Remote sensing of clouds, forest fires, aerosols, and the radiation budget (Li)
  • Analysis of spectrally resolved infrared radiance from space (Kirk-Davidoffi)
    NWP methods in Climate Modeling
  • Enhanced ocean-atmosphere predictability from coupled breeding (Lyapunov) vectors (Kalnay)
  • Modeling regional phenomenon with the Spectral Element Atmospheric Model (Baer, Fournier)
  • Devising integration schemes that allow larger time-steps without impacting variability (Baer)
  • Hybrid coupled models for tropical climate variability (Murtugudde)