Climate Analysis

Introduction Atmospheric Temperature Total Column Water Vapor

 

Introduction

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.

Atmospheric Temperature

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.

Tropospheric Temperature

There are three tropospheric temperature datasets available from RSS, TLT (Temperature Lower Troposphere), TMT (Temperature Middle Troposphere), and TTT (Temperature Tropical 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.

 

But....

  • The troposphere has not warmed as fast as almost all 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 thick black line is the measured data from RSS V3.3 MSU/AMSU Temperatures.  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 within the range of model predictions.

Fig. 1.  Global (80S to 80N) Mean TLT 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 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 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. Again, 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. 3. Northern Polar (55N to 80N) Mean TLT 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. For this latitude band, the observations remain withing the model envelope.
 
The reasons for the discrepancy between the predicted and observed warming rate are currently under investigation by a number of research groups.  Possible reasons include increased oceanic circulation leading to increased subduction of heat into the ocean, higher than normal levels of stratospheric aerosols due to volcanoes during the past decade, incorrect ozone levels used as input to the models, lower than expected solar output during the last few years, or poorly modeled cloud feedback effects.  It is possible (or even likely) that a combination of these candidate causes is responsible.
 

Stratospheric Temperature

The temperature of the lower stratosphere has been monitored since late 1978 by the MSU and AMSU instruments.  The RSS merged lower stratospheric temperature data product is called TLS, or temperature lower stratosphere.  Unlike the troposphere, which warmed slowly over this period, the lower stratosphere has been cooling due to both decreases in stratospheric ozone caused by CFC’s, and increases in well-mixed greenhouse gases causes by human activity.  This slow cooling trend is punctuated occasionally by temporary increases in stratospheric aerosols caused by major volcanic eruptions.  In the plot below, we show the global mean temperature anomaly from the RSS TLS data, and the 5% to 95% envelope from the CMIP-5 historical simulations.
 
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.

 

Total Column Water Vapor

Over the oceans, we can monitor decadal-scale changes in the total amount of water vapor in the atmosphere using our merged water vapor product, derived from measurements made by SSM/I, SSMIS, AMSRE, and WindSat.  For a description of this dataset, see the Atmospheric Water Vapor Measurement page.  As the Earth's troposphere warms, it is able to "hold" more water vapor without the vapor condensing into clouds and then rain.  Assuming the relative humidity remains constant, the amount of extra water vapor is governed by the Clausius-Clapeyron relationship, and is about 7% more water vapor per degree Kelvin increase in temperature.    The global increase in water vapor is easy to see in Figure 5, which shows the global mean time series of total column water vapor over the worlds oceans, expressed in percent change from average. 
 
Figure 5.  Time series of total column vapor anomaly, averaged over the world's oceans, from 60S to 60N. 
 
This increase can be formally attributed to human-induced climate change -- see Santer et al, 2007.  While there is a substantial overall increase in water vapor, it is by no means spatially uniform.  Figure 6 shows a map of water vapor trends over the 1988-2012 period. 
 
 
Figure 6.  Maps of Trends in Column Water Vapor, for the 1988-2012 period.
 
While much of the world shows moistening to various degrees, there are regions of very substantial drying in the central tropical Pacific Ocean on either side of the equator.  The trends in water vapor, either positive or negative, that lead to this pattern are almost all statistically significant compared to the estimated error in the water vapor trends.
 
In the deep tropics, changes in water vapor are very strongly correlated with changes in atmospheric and surface temperatures.   Figure 7 shows time series of water vapor and temperature anomalies (RSS TLT from MSU/AMSU for the lower troposphere, and the NOAA Merged Land-Ocean Surface Temperature Analysis (MLOST) for SST).  The data have been averaged over the oceans in the latitude band from 20S to 20N.
Figure 7.  Time series of total column vapor anomaly and temperature anomaly, averaged over the world's oceans, from 20S to 20N. 
 
Here, the changes in Sea Surface Temperature (SST) have been scaled by a factor of 1.45 to account for the vertical amplification of temperature changes in the troposphere.  The water vapor axis is scaled so that a 1 degree change in lower tropospheric temperature (TLT) corresponds to a 7% change in column water vapor.  The very tight correlation is clearly seen.  This correlation in both measurements and CMIP-3 model output is discussed in detail in Mears et al, 2007
 

References

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., 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.