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. (Note: this section was updated on June 30, 2017 to include results from TLT Version 4.0)

Tropospheric Temperature

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 0.18 degrees Kelvin per decade (0.32 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 quite as fast as most climate models predict.  Note that this problem has been reduced by the large 2015-2106 El Nino Event, and the updated version of the RSS tropospheric datasets.

 

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 are the results from the most recent version of the RSS satellite dataset.  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.  

Fig. 1.  Global (70S to 80N) Mean TLT Anomaly plotted as a function of time.  The black line is the time series for the  RSS V4.0 MSU/AMSU atmosperhic temperature dataset.  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 in the lower part of the model distribution, indicating that there is a small discrepancy between the model predictions and the satelllite observations.(All time series have been smoothed to remove variabilty on time scales shorter than 6 months.)
 
Fig. 2. Tropical (30S to 30N) Mean TLT Anomaly plotted as a function of time.  The black line is the time series for the  RSS V4.0 MSU/AMSU atmosperhic temperature dataset.  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 on the low side of the simulated values, indicating that the simulations as a whole are predicting more warming than has been observed by the satellites.  
 
 
Why does this discrepancy exist and what does it mean?  One possible explanation is an error in the fundamental physics used by the climate models.  In addition to this possibility, there are at least three other plausible explanations for the warming rate differences.  There are errors in the forcings used as input to the model simulations (these include forcings due to anthropogenic gases and aerosols, volcanic aerosols, solar input, and changes in ozone), errors in the satellite observations (partially addressed by the use of the uncertainty ensemble), and sequences of internal climate variability in the simulations that are difference from what occurred in the real world.  We call to these four explanations “model physics errors”, “model input errors”, “observational errors”, and “different variability sequences”. They are not mutually exclusive. In fact, there is hard scientific evidence that all four of these factors contribute to the discrepancy, and that most of it can be explained without resorting to model physics errors.  For a detailed discussion of all these reasons, see the post on the Skeptical Science blog by Ben Santer and Carl Mears, and the recent paper in Nature Geoscience by Santer et al.
 
 

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 1988-2016 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 temperatures.   Figure 7 shows time series of water vapor and temperature anomalies from the different satellite temperature datasets. 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. The top panel shows the time series.  The middle panel shows the running trend, starting in January 1988, and ending at the time on the x-axis.  The bottom panel shows the ratio of the vapor trend to the TLT trend.  Climate models suggest that this ratio should be about 6.2%/K.  All combinations of satellite dataset show larger ratio, suggesting that either the measurements show too much moistneing, or too little warming.  The most recent version of the RSS TLT dataset is closest to expectations.  This is Figure 13 in Mears and Wentz (2017).
 
This correlation in both measurements and CMIP-3 model output is discussed in detail in Mears et al, 2007 and revisited in Mears and Wentz (2017) .
 
 

References

Mears, C. A. and F. J. Wentz, (in press) A satellite-derived lower tropospheric atmospheric temperature dataset using an optimized adjustment for diurnal effects , J. Climate.

Santer, Benjamin D., Fyfe, John C., Pallotta, Giuliana, Flato, Gregory M., Meehl, Gerald A., England, Matthew H., Hawkins, Ed, Mann, Michael E., Painter, Jeffrey F., Bonfils, Celine, Cvijanovic, Ivana, Mears, Carl, Wentz, Frank J., Po-Chedley, Stephen, Fu, Qiang, Zou, Cheng-Zhi (2017), Causes of differences in model and satellite tropospheric warming rates, Nature Geosci, advance online publication, doi: 10.1038/ngeo2973.

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.