|Introduction||Atmospheric Temperature||Total Column Water Vapor|
Climate is the average weather in a given location, averaged over a fairly long time period, at least 10 years. When we talk about climate, we often talk about average values of meteorological or oceanographic variables, such as air temperatures, precipitation, humidity, wind speed or ocean temperature at a given location at a given time of year. If the climate changes over time, it can directly affect human activities by altering the crops that can be grown, the supply of fresh water, or the mean level of the ocean. It can also affect natural ecosystems, causing deserts to expand, wildfires to become more prevalent, or permafrost to melt.
Over the past two decades, there has been growing concern about the effects of human-produced greenhouse gases and other environmental pollutants on Earth's climate. These changes are predicted by climate models, which are also used to project changes into the next centuries. Satellite data records are beginning to be long enough to evaluate multi-decadel changes. These changes can be examined for evidence of climate change, and used to see if climate models can do a good job when used to "predict" the changes that have already occurred.
In order to produce a data record that extends long enough for climate change studies, measurements from different satellites must be intercalibrated with each other and then combined together into a single record. We have completed this process for atmospheric temperature and total column water vapor, and are about to release an intercalibrated wind speed product.
Compared to in situ measurements, the main advantage of satellite data records from polar orbiting satellites is the nearly complete global coverage and homogeneous data quality. The in situ data record is fairly sparse in regions located away from industrialized countries, which are concentrated on the land masses and in the northern hemisphere mid-latitudes. For example, there are very few weather balloons launched in the Eastern Tropical Pacific Ocean, even though this region is where the changes in Sea Surface Temperature due to the El Nino - Southern Oscillation cycle are largest.
Below, we discuss some basic climate results obtained using Remote Sensing Systems microwave data, and discuss some climate related research we have performed.
See the Upper Air Temperature Measurement page for details about how the atmospheric temperature datasets are produced. Here we present applications of this dataset to climate change analysis.
There are three tropospheric temperature datasets available from RSS, TLT (Temperature Lower Troposphere), TMT (Temperature Middle Troposphere), and TTT (Temperature Total Troposphere, after Fu and Johansen). Using these datasets, we can investigate whether there have been significant changes in the tropospheric temperature over the last 35 years, and whether or not the spatial patterns of these changes agree with those predicted by climate models.
Over the past decade, we have been collaborating with Ben Santer at LLNL (along with numerous other investigators) to compare our tropospheric results with the predictions of climate models. Our results can be summarized as follows:
- Over the past 35 years, the troposphere has warmed significantly. The global average temperature has risen at an average rate of about 0.13 degrees Kelvin per decade (0.23 degrees F per decade).
- Climate models cannot explain this warming if human-caused increases in greenhouse gases are not included as input to the model simulation.
- The spatial pattern of warming is consistent with human-induced warming. See Santer et al 2008, 2009, 2011, and 2012 for more about the detection and attribution of human induced changes in atmospheric temperature using MSU/AMSU data.
- The troposphere has not warmed quite as fast as most climate models predict.
To illustrate this last problem, we show several plots below. Each of these plots has a time series of TLT temperature anomalies using a reference period of 1979-2008. In each plot, the blue band is the 5% to 95% envelope for the RSS V3.3 MSU/AMSU Temperature uncertainty ensemble. (For a detailed explanation of the uncertainty ensemble, see Mears et al. 2011.) The yellow band shows the 5% to 95% envelope for the results of 33 CMIP-5 model simulations (19 different models, many with multiple realizations) that are intended to simulate Earth's Climate over the 20th Century. For the time period before 2005, the models were forced with historical values of greenhouse gases, volcanic aerosols, and solar output. After 2005, estimated projections of these forcings were used. If the models, as a whole, were doing an acceptable job of simulating the past, then the observations would mostly lie within the yellow band. For the first two plots (Fig. 1 and Fig 2), showing global averages and tropical averages, this is not the case. Only for the far northern latitudes, as shown in Fig. 3, are the observations mostly within the range of model predictions.
Fig. 1. Global (80S to 80N) Mean TLT Anomaly plotted as a function of time. The blue band is the 5% to 95% envelope for the RSS V3.3 MSU/AMSU Temperature uncertainty ensemble. The yellow band is the 5% to 95% range of output from CMIP-5 climate simulations. The mean value of each time series average from 1979-1984 is set to zero so the changes over time can be more easily seen. Note that after 1998, the observations are likely to be below the simulated values, indicating that the simulation as a whole are predicting too much warming.
Fig. 2. Tropical (30S to 30N) Mean TLT Anomaly plotted as a function of time. The the blue band is the 5% to 95% envelope for the RSS V3.3 MSU/AMSU Temperature uncertainty ensemble. The yellow band is the 5% to 95% range of output from CMIP-5 climate simulations. The mean value of each time series average from 1979-1984 is set to zero so the changes over time can be more easily seen. Again, after 1998, the observations are likely to be below the simulated values, indicating that the simulation as a whole are predicting more warming than has been observed by the satellites.
Fig. 3. Northern Polar (55N to 80N) Mean TLT Anomaly plotted as a function of time. The blue band is the 5% to 95% envelope for the RSS V3.3 MSU/AMSU Temperature uncertainty ensemble. The yellow band is the 5% to 95% range of output from CMIP-5 climate simulations. The mean value of each time series average from 1979-1984 is set to zero so the changes over time can be more easily seen. For this latitude band, the observations remain withing the model envelope.
Fig. 4. Global (80S to 80N) Mean TLS Anomaly plotted as a function of time. The thick black line is the observed time series from RSS V3.3 MSU/AMSU Temperatures. The yellow band is the 5% to 95% range of output from CMIP-5 climate simulations. The mean value of each time series average from 1979-1984 is set to zero so the changes over time can be more easily seen. Note that the response to the volcanic eruptions of El Chichón (1983) and Pinatubo (1991) is too large in some of the models, and that the models tend to show less overall cooling than the observations.
The basic features of the changes in stratospheric temperature are captured by the models, though some models appear to show too much response to volcanic eruptions and also appear to show too little overall cooling.
Figure 5. Time series of total column vapor anomaly, averaged over the world's oceans, from 60S to 60N.
Figure 6. Maps of Trends in Column Water Vapor, for the 1988-2012 period.
Figure 7. Time series of total column vapor anomaly and temperature anomaly, averaged over the world's oceans, from 20S to 20N.
Santer, B. D., J. F. Painter, C. A. Mears, C. Doutriaux, P. Caldwell, J. M. Arblaster, P. J. Cameron-Smith, N. P. Gillett, P. J. Gleckler, J. Lanzante, J. Perlwitz, S. Solomon, P. A. Stott, K. E. Taylor, L. Terray, P. W. Thorne, M. F. Wehner, F. J. Wentz, T. M. L. Wigley, L. J. Wilcox and C. Z. Zou, (2012) Identifying Human Influences on Atmospheric Temperature, Proceedings of the National Academy of Sciences, 110(1), 26-33, doi:10.1073/pnas.1210514109.
Santer, B. D., C. A. Mears, C. Doutriaux, P. M. Caldwell, P. J. Gleckler, T. M. L. Wigley, S. Solomon, N. Gillett, D. P. Ivanova, T. R. Karl, J. R. Lanzante, G. A. Meehl, P. A. Stott, K. E. Taylor, P. W. Thorne, M. F. Wehner and F. J. Wentz, (2011) Separating Signal and Noise in Atmospheric Temperature Changes: The Importance of Timescale, J. Geophys. Res., 116, D22105, doi:10.1029/2011JD016263.
Santer, B. D., K. E. Taylor, P. J. Gleckler, C. Bonfils, T. P. Barnett, D. W. Pierce, T. M. L. Wigley, C. A. Mears, F. J. Wentz, W. Bruggemann, N. Gillett, S. A. Klein, S. Solomon, P. A. Stott and M. F. Wehner, (2009) Incorporating Model Quality Information in Climate Change Detection and Attribution Studies, Proc. Natl. Acad. Sci. U. S. A., 106(35), 14778-14783, doi:10.1073/pnas.0901736106.
Santer, B. D., P. W. Thorne, L. Haimberger, K. E. Taylor, T. M. L. Wigley, J. R. Lanzante, S. Solomon, M. Free, P. J. Gleckler, P. D. Jones, T. R. Karl, S. A. Klein, C. A. Mears, D. Nychka, G. A. Schmidt, S. C. Sherwood and F. J. Wentz, (2008) Consistency of Modelled and Observed Temperature Trends in the Tropical Troposphere, International Journal of Climatology, 28(13), 1703-1722.
Mears, C. A., F. J. Wentz, P. Thorne, and D. Bernie (2011), Assessing uncertainty in estimates of atmospheric temperature changes from MSU and AMSU using a Monte-Carlo estimation technique, Journal of Geophysical Research, 116.
Mears, C. A., B. D. Santer, F. J. Wentz, K. E. Taylor and M. F. Wehner, (2007) Relationship Between Temperature and Precipitable Water Changes Over Tropical Oceans, Geophys. Res. Lett., 34, L24709, doi:10.1029/2007GL031936.
Santer, B. D., C. A. Mears, F. J. Wentz, K. E. Taylor, P. J. Gleckler, T. M. L. Wigley, T. P. Barnett, J. S. Boyle, W. Bruggemann, N. P. Gillett, S. Klein, D. W. Pierce, P. A. Stott and M. F. Wehner, (2007) Identification of Human-Induced Changes in Atmospheric Moisture Content, Proc. Natl. Acad. Sci. U. S. A., 104, 15248-15253.