Qigang Wu

University of Oklahoma, Oklahoma City, OK, United States

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Publications (8)64.51 Total impact

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    ABSTRACT: Significant changes in physical and biological systems are occurring on all continents and in most oceans, with a concentration of available data in Europe and North America. Most of these changes are in the direction expected with warming temperature. Here we show that these changes in natural systems since at least 1970 are occurring in regions of observed temperature increases, and that these temperature increases at continental scales cannot be explained by natural climate variations alone. Given the conclusions from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report that most of the observed increase in global average temperatures since the mid-twentieth century is very likely to be due to the observed increase in anthropogenic greenhouse gas concentrations, and furthermore that it is likely that there has been significant anthropogenic warming over the past 50 years averaged over each continent except Antarctica, we conclude that anthropogenic climate change is having a significant impact on physical and biological systems globally and in some continents.
    Nature 05/2008; 453(7193):353-357. DOI:10.1038/nature06937 · 42.35 Impact Factor
  • Qigang Wu
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    ABSTRACT: A maximum covariance analysis (MCA) of monthly anomaly data from the NCEP-NCAR reanalysis during 1981-2007 suggests that the atmosphere over subtropical-midlatitude South Atlantic during the austral late winter and spring is significantly correlated to the underlying sea surface temperature (SST) anomalies (SSTA) up to at least 4 months earlier. Such SST impact on the atmosphere is independent from the tropical Pacific and tropical South Atlantic influence, and is confirmed by the regression analysis based on the SSTA centers of action. The MCA pattern of SST resembles the dominant mode of SST variability and the monopole atmospheric signal is barotropic through the troposphere and hemispheric in extent. This implies predictability of the late winter and spring atmospheric circulation in the subtropical-midlatitude South Atlantic with a lead time of up to 4 months.
    Geophysical Research Letters 01/2008; 35(22). DOI:10.1029/2008GL035488 · 4.46 Impact Factor
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    ABSTRACT: We have analyzed the seasonal variations of global mean surface air temperature (SAT) and surface energy budgets of 17 AR4 models. Considerable differences in the amplitude of seasonal cycle (A) in the global mean SAT in the pre-industrial control simulations among the models have been traced, to a large degree, to differences in their simulated clear-sky downward longwave radiation (LW "«) and latent heat flux (LH). We suggest that water vapor feedback process influence the seasonal changes of SAT through its roles on the seasonal variations of LW "« and LH. This implies that the simulated seasonal change of global mean SAT might contain a clue about the sensitivity of water vapor feedback and the A of in SAT thus provides some constraint on climate sensitivity since both are subject to the same feedback process.
    01/2008; 35(8). DOI:10.1029/2008GL033454
  • Qigang Wu · David J. Karoly
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    ABSTRACT: [1] Previous studies have shown that observed significant warming trends in surface air temperature (SAT) consistent with the response to anthropogenic forcing are detected at scales on the order of 500 km in many regions of the globe. However, regional SAT trends project strongly on the dominant natural atmospheric circulation modes, such as the Arctic Oscillation (AO) and the hemispheric Pacific-North America (PNA)-like patterns. The warming associated with the changes of atmospheric circulation is not well simulated in current coupled climate models. In this study, we explore the influence of the exclusion of warming related to changes of the atmospheric circulation on the detection of a regional response to combined anthropogenic and natural forcings. We compare observed SAT trends over the second half of the 20th century with those simulated in response to natural and anthropogenic climate forcings in a suite of six current coupled general circulation models. Control runs from these models are used to provide estimates of the internal variability of trends. We find that the detection of the regional response to combined anthropogenic and natural forcing is robust to the exclusion of warming related to changes of the atmospheric circulation considered here.
    04/2007; 34(8). DOI:10.1029/2006GL028502
  • David J. Karoly · Qigang Wu
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    ABSTRACT: Trends in surface temperature over the last 100, 50, and 30 yr at individual grid boxes in a 5° latitude longitude grid are compared with model estimates of the natural internal variability of these trends and with the model response to increasing greenhouse gases and sulfate aerosols. Three different climate models are used to provide estimates of the internal variability of trends, one of which appears to overestimate the observed variability of surface temperature at interannual and 5-yr time scales. Significant warming trends are found at a large fraction of the individual grid boxes over the globe, a much larger fraction than can be explained by internal climate variations. The observed warming trends over the last 50 and 30 yr are consistent with the modeled response to increasing greenhouse gases and sulfate aerosols in most of the models. However, in some regions, the observed century-scale trends are significantly larger than the modeled response to increasing greenhouse gases and sulfate aerosols in the atmosphere. Warming trends consistent with the response to anthropogenic forcing are detected at scales on the order of 500 km in many regions of the globe.
    Journal of Climate 11/2005; 18(21):4337-4343. DOI:10.1175/JCLI3565.1 · 4.90 Impact Factor
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    Qigang Wu · Gerald R. North
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    ABSTRACT: This study makes use of calendar month averages of surface temperature taken from the long control runs of 13 coupled atmosphere/ocean general circulation models (GCMs) as well as from the record of observational surface temperature measurements. After aggregating the data into global averages, we examine the mean seasonal cycle as well as the anomaly statistics. We find a large range of different results for the models in the seasonal cycle of anomaly statistics. There appears to be an “empirical” relationship between the published sensitivity of the models to CO2 doubling and the seasonal cycle of the anomaly statistics. We draw an inference about the sensitivity of the real climate based upon this relationship. The relationship appears to be plausible based upon simple considerations of the land–sea distribution and the wintertime variance of climatic noise forcing. This inference leads us to estimate the sensitivity of climate to a doubling of CO2 to be about 2.7°C with the 95% confidence interval (2.3°C, 3.1°C).
    Journal of Geophysical Research Atmospheres 01/2003; 108. DOI:10.1029/2002JD002218 · 3.44 Impact Factor
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    Qigang Wu · Gerald R. North
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    ABSTRACT: We compare several Atmosphere/Ocean General Circulation model control runs by fitting long control runs to a simple discrete (annual averages) stochastic model: Rate of change of global heat content = difference of absorbed and emitted radiation + white noise. If the heat content is taken to be proportional to the surface temperature we can estimate the effective (broad frequency-band averaged) heat capacity for the system by a regression procedure. We find a wide range of effective heat capacities that correlate significantly with the published sensitivities of the models. Models with large sensitivities have large thermal inertia. Several interpretations are discussed.
    Geophysical Research Letters 08/2002; 29(15):2-1. DOI:10.1029/2002GL014864 · 4.46 Impact Factor
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    Gerald R. North · Qigang Wu
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    ABSTRACT: Estimates of the amplitudes of the forced responses of the surface temperature field over the last century are provided by a signal processing scheme utilizing space-time empirical orthogonal functions for several combinations of station sites and record intervals taken from the last century. These century-long signal fingerprints come mainly from energy balance model calculations, which are shown to be very close to smoothed ensemble average runs from a coupled ocean-atmosphere model (Hadley Centre Model). The space-time lagged covariance matrices of natural variability come from 100-yr control runs from several well-known coupled ocean-atmosphere models as well as a 10000-yr run from the stochastic energy balance climate model (EBCM). Evidence is found for robust, but weaker than expected signals from the greenhouse [amplitude 65% of that expected for a rather insensitive model (EBCM: T2×CO2 2.3°C)], volcanic (also about 65% expected amplitude), and even the 11-yr component of the solar signal (a most probable value of about 2.0 times that expected). In the analysis the anthropogenic aerosol signal is weak and the null hypothesis for this signal can only be rejected in a few sampling configurations involving the last 50 yr of the record. During the last 50 yr the full strength value (1.0) also lies within the 90% confidence interval. Some amplitude estimation results based upon the (temporally smoothed) Hadley fingerprints are included and the results are indistinguishable from those based on the EBCM. In addition, a geometrical derivation of the multiple regression formula from the filter point of view is provided, which shows how the signals `not of interest' are removed from the data stream in the estimation process. The criteria for truncating the EOF sequence are somewhat different from earlier analyses in that the amount of the signal variance accounted for at a given level of truncation is explicitly taken into account.
    Journal of Climate 04/2001; 14(8):1839-1863. DOI:10.1175/1520-0442(2001)014<1839:DCSUST>2.0.CO;2 · 4.90 Impact Factor

Publication Stats

626 Citations
64.51 Total Impact Points


  • 2005–2008
    • University of Oklahoma
      • School of Meteorology
      Oklahoma City, OK, United States
  • 2002
    • Texas A&M University
      • Department of Atmospheric Sciences
      College Station, Texas, United States