NOAA/NESDIS/Center for Satellite Applications and Research Toward a Long-Term Climate Change Monitoring in the JPSS Era Cheng-Zhi Zou Thanks Bruce for the introduction. The title of my talk is ‘MSU/AMSU/SSU CDR development. I gave a talk three years ago in the STAR science forum about the MSU inter-calibration and trend and I kept talking this subject in the last few years in various occasions and hope people don’t get tired of it. But today I try to provide a comprehensive review of the current status and a discussion of various science issues include various bias correction, validation, inter-comparisons, web data support, and so on. I have reserved the room for two hours, but I will only talk about 45 minutes to one hour and allow plenty of time for questions. So don’t get scared about the length of the talk as suggested in the announcement. NOAA/NESDIS/Center for Satellite Applications and Research 2016 STAR JPSS Annual Meeting, July 8-12, 2016

Outline The NOAA/NASA/MetOp POES Temperature Sounders Current Capability at STAR Applications of Atmospheric Temperature CDR Perspective Mitch provided long time support for project from the program side, such as leveraging funding. And Fuzhong also provides program support and coordinate the CRTM team to work with our product development team. You may notice that most of my SDS science team members are from his Branch. I also want to thank Changyong for his long time support from scince side. I can always count on his support when I need a discussion on science. even when I need support from his team members.

NOAA/NASA/MetOp Satellite Series Carrying Microwave/Infrared Sounders MSU SSU AMSU-A AIRS ATMS CrIS AMSU-A IASI MWS IASI-NG Present

The NOAA/NASA/MetOp Temperature Sounders MSU+SSU; AMSU-A Have been measuring atmospheric temperature profiles from surface to stratosphere Nearly 40 years of observations with global coverage MSU : From surface to lower- stratosphere SSU: From mid-stratosphere to lower-mesosphere AMSU-A: From surface to upper- This project focus on developing MSU/AMSU atmospheric temperature CDRs. The window channels will be addressed by another NOAA group. In summary, we work on 15 atmospheric temperature channels, involving 15 satellites, with tempral coverage from late 1978 to present. Each channel needs to be intercalibrated seperately. With that, let me first talk about the MSU atmospheric temperature CDR development. The MSU started from 1978 on TIROS-N and ended on NOAA-14 in May 2007. So we don’t have MSU observations now. We have already completed the MSU CDR development and gained a lot of experiences. We have developed many new techniques for various bias correction procedure. So I am going to talk with more details on this instrument.

The NOAA/NASA/MetOp Atmospheric Temperature Souder Series ATMS: From surface to upper- stratosphere AIRS/IASI/CrIS: From surface to lower-mesosphere Pressure (hPa) This project focus on developing MSU/AMSU atmospheric temperature CDRs. The window channels will be addressed by another NOAA group. In summary, we work on 15 atmospheric temperature channels, involving 15 satellites, with tempral coverage from late 1978 to present. Each channel needs to be intercalibrated seperately. With that, let me first talk about the MSU atmospheric temperature CDR development. The MSU started from 1978 on TIROS-N and ended on NOAA-14 in May 2007. So we don’t have MSU observations now. We have already completed the MSU CDR development and gained a lot of experiences. We have developed many new techniques for various bias correction procedure. So I am going to talk with more details on this instrument. Weighting functions of ATMS and AIRS

El Chichon (1982) Pinatubo (1991) El Nino (1998) El Nino (2010) Current Capability at STAR – Merged MSU/AMSU-A Time Series (1978-present) Here is mid-tropopshere, tropopause, lower stratosphere temperature trends derived from version 2 merged MSU/AMSU time series. The time period is from late 1978 to the end of 2009. Each pixel represents a 5-day averaged global temperatures for a particular MSU/AMSU instrument. Satellites are reprented using different colors. We can see the merging are very good, with point-to-point agreement. For TMT, the trend is about 0.15 K/dec, little trend in TTS, A -0.4 k/dec trend in TLS The is mathematical chart for this transfer. The instrument is desinged that this transfer is very close to a linear. For purely linear calibration, the two calibration points will uniquely determine a linear calibration equation. However, in reality, the MSU instrument contains a week nonlinear term, with the magnitude is unknown. The uncertainty of this nonlinear magnitude caused a whole lot of problems for the trends. In our current calibration system, we use Simultaneous nadir overpass method to determine the nonlinear coefficients and the constant offset between different satellites. El Chichon (1982) Pinatubo (1991) El Nino (1998) El Nino (2010) Monthly and global-mean temperature anomaly time series, Updated and delivered to NCEI every month for ‘operationally’ archiving and distribution

AMSU-A Only Time Series (1998-present) Similar procedure and Methodology More channels and thus better vertical resolution Shorter time period (1998- present) Diurnal effect in stratospheric channels are larger and was removed

SSU-only Time Series (1978-2006) SSU Temperature Anomaly Time Series Before Recalibration (Raw Data) After Recalibration So here is a summary that needs to be deal with in the MSU/AMSU reprocessing. These issues include, but are not limited to, ………. In this talk, I’ll be focusing on the intersatellite biases and warm target temperature contamination; but also touch a little bit on other problems. Recalibration successfully rehabilitated the SSU data record, making the inconsistent raw SSU time series with large time-varying biases between satellites before correction (left) to a consistent, well-intercalibrated and -merged satellite time series suitable for climate change studies after correction (right).

Merged SSU and AMSU-A Time Series (1978-present) SSU time series after removing seasonal cycle 1) Time series are not linked to each other due to the cell pressure problems Merged SSU and AMSU-A time series

Application -- Involves in Global Warming Debate SSU time series after removing seasonal cycle 1) Time series are not linked to each other due to the cell pressure problems Three groups, University of Alabama at Huntsville (UAH), Remote Sensing Systems (RSS), and NOAA/STAR produce the same variable, Temperature of Mid-Troposphere (TMT), but having different long-term trend values

Support Congress Hearing on Climate Change Debate SSU time series after removing seasonal cycle 1) Time series are not linked to each other due to the cell pressure problems Plot from Jan. 2016 Congress Climate Change Hearing

Support NOAA and IPCC climate change assessment IPCC provide climate change assessment every few years NOAA/NCEI provide climate assessment every year STAR data used as a reference in characterizing atmospheric temperature changes SSU time series after removing seasonal cycle 1) Time series are not linked to each other due to the cell pressure problems Global mean temperature time series in Lower-troposphere and lower-stratosphere (Plot from IPCC AR5 Report)

Monitoring Climate Change in Near Real Time SSU time series after removing seasonal cycle 1) Time series are not linked to each other due to the cell pressure problems 1998 EL Nino 2016 EL Nino

Investigation of Climate Change Phenomenon —Temperature trends responding to ozone trends Linear trends (K/Dec) and 2-sigma uncertainty of the residual time series after the 11-year solar cycle and aerosol effect were removed from the extended SSU global mean time series.   9719-2015 1979-1997 1998-2015 SSU Ch3 -0.64 (0.06) -0.93 (0.10) -0.39 (0.10) SSU Ch2 -0.55 (0.06) -0.88 (0.07) -0.30 (0.10) SSU Ch1 -0.48 (0.06) -0.76 (0.07) -0.25 (0.07) SSU time series after removing seasonal cycle 1) Time series are not linked to each other due to the cell pressure problems

Validating Chemistry-Climate Model Simulations in the Stratosphere Trends are nearly identical for channels 2 and 3 Model cooling trends are slightly weaker for channel 1 Solar cycle amplitude in models is weaker

Spatial Trend Pattern of the Mid-Tropospheric Temperature So here is a summary that needs to be deal with in the MSU/AMSU reprocessing. These issues include, but are not limited to, ………. In this talk, I’ll be focusing on the intersatellite biases and warm target temperature contamination; but also touch a little bit on other problems.

Future Perspective Present

Perspective Merge ATMS channels 5, 7 and 9 to the existing MSU/AMSU time series for extended TMT, TUT, and TLS. This will extend the time series from 1978 to possibly 2038 in the JPSS era. The extended time series will span for over 60 years. It will provide more accurate estimate on tropospheric temperature trend: the typical time length for tropospheric temperature trend estimates with 20% error is about 35 years Merge ATMS channels 4 through 14 to AMSU-A channels. This will extend the AMSU-A only time series from 1998 to 2038. The extended time series will span for 40 years with high vertical resolution for trend and variability studies So here is a summary that needs to be deal with in the MSU/AMSU reprocessing. These issues include, but are not limited to, ………. In this talk, I’ll be focusing on the intersatellite biases and warm target temperature contamination; but also touch a little bit on other problems.

Perspective Merge ATMS channels to SSU/AMSU-A channels for extended TMS, TUS, and TSM. This will again extend the time series from 1978 to 2038. The extended time series will span for over 60 years with 6 complete solar cycles. This stratospheric temperature time series is expected to largely improve our understanding on the impact of the solar cycle and aerosol on the climate change Merge AIRS/IASI/CrIS stratospheric channels to SSU channel 3. Since AMSU-A/ATMS channel 14 is not high enough to cover the lower- mesospheric layers that the SSU channel 3 was sensing, this merging will likely provide a more accurate SSU extended time series over the lower- mesospheric layers So here is a summary that needs to be deal with in the MSU/AMSU reprocessing. These issues include, but are not limited to, ………. In this talk, I’ll be focusing on the intersatellite biases and warm target temperature contamination; but also touch a little bit on other problems.

Preliminary Work on AMSU-A and ATMS Merging In this talk, I’ll be focusing on the intersatellite biases and warm target temperature contamination; but also touch a little bit on other problems. Plot showing effect of inter-satellite calibration between AMSU-A and ATMS (from Zou X. et al. 2014)

Thank you!