The rate of global surface warming is crucial for tracking progress towards global climate targets, but is strongly influenced by interannual-to-decadal variability, which precludes rapid detection of the temperature response to emission mitigation. Here we use a physics based Green’s function approach to filter out modulations to global mean surface temperature from sea-surface temperature (SST) patterns, and show that it results in an earlier emergence of a response to strong emissions mitigation. For observed temperatures, we find a filtered 2011–2020 surface warming rate of 0.24 °C per decade, consistent with long-term trends. Unfiltered observations show 0.35 °C per decade, partly due to the El Nino of 2015–2016. Pattern filtered warming rates can become a strong tool for the climate community to inform policy makers and stakeholder communities about the ongoing and expected climate responses to emission reductions, provided an effort is made to improve and validate standardized Green’s functions. The pattern of sea surface temperatures affects global mean temperatures from year to year. By filtering out parts of this natural variability, researchers show that they can more rapidly detect the influence of mitigation of CO2 emissions on the climate

NASA is a global leader in studying Earth’s changing climate.

It's a hot topic.

Readers may recall discussions of a paper by Thompson et al (2008) back in May 2008. This paper demonstrated that there was very likely an artifact in the sea surface temperature (SST) collation by the Hadley Centre (HadSST2) around the end of the second world war and for a few years subsequently, related to the different ways ocean temperatures were taken by different fleets. At the time, we reported that this would certainly be taken ...

Channel TLT (v4.0) TTT (v4.0) TMT (v4.0) TTS (v4.0) TLS (v4.0) C10 C11 C12 C13 C14 C25 Region Global (-82.5,82.5) Northern Hemisphere (0,82.5) Southern Hemisphere (-82.5,0) North Polar (60,82.5) North mid-latitudes (25,60) Tropics (-25,25) South mid-latitudes (-60,-25) South Polar (-82.5,-60) Continental United States Browse monthly temperature maps

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. 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), Assess

A new paper published in the Journal of Climate reveals that the lower part of the Earth’s atmosphere has warmed much faster since 1979 than scientists relying on satellite data had previously thought.

A rundown of what surface and satellite temperature records can and can't tell us about global temperature rise and climate change.

While global temperature is a simple idea, measuring it is harder than you might think. We take a look at how scientists measure global temperature.