Tag: climate variability

New Open Database Unifies 24 Years of Satellite Data to Track Earth’s Water Cycle

WISO- SAT is a new open- access database that standardizes and harmonizes ~24 years (2002- 2024) of satellite measurements of stable hydrogen isotopes (mainly δD, the deuterium- to- hydrogen ratio) in atmospheric water vapor from eight missions.

Water isotopes act as natural tracers or “fingerprint”. Their ratios reveal where water vapor originated and the processes (evaporation, condensation, transport) it experienced.

A global overview of the water cycle illustrating 24 years of satellite measurements, featuring satellite icons for NASA missions including AQ, GPM, SMAP, GRACE-FO, ICESat-2, and ESA's SMOS, along with data streams showing evaporation, precipitation, water vapor transport, and cloud formation.
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WISO- SAT is a new open- access database that standardizes and harmonizes ~24 years (2002- 2024) of satellite measurements of stable hydrogen isotopes (mainly δD, the deuterium- to- hydrogen ratio) in atmospheric water vapor from eight missions.

It was developed by an international team led by Rice University researcher Mampi Sarkar (corresponding author), with Sylvia Dee (associate professor of Earth, environmental and planetary sciences at Rice) and collaborators from NASA/JPL, European institutions, Japan, and others.

The work is described in a paper published in Scientific Data (Nature). The Rice University news release covering it was written by media relations specialist Rachel Leeson (dated Oct. 26, 2026).

Water isotopes act as natural tracers or “fingerprint”. Their ratios reveal where water vapor originated and the processes (evaporation, condensation, transport) it experienced.

Satellites have measured these for decades, but each mission (SCIAMACHY, GOSAT, GOSAT- 2, TROPOMI, TES, AIRS, CrIS, IASI) used different instruments, formats, calibrations, grids, altitudes, and sampling strategies.

Combining datasets previously required up to a year of labor- intensive cleaning and reconciliation, and efforts were often duplicated worldwide.

WISO- SAT solves this by collecting the raw data, applying bias corrections across platforms, quantifying uncertainty, and distributing instrument-specific gridded NetCDF products at 1° × 1° and 5° × 5° resolutions (monthly δD and moisture concentration). The standardization pipeline itself is public, so new data can be added in a consistent way.

Researchers can now more readily study:

  • Moisture transport, cloud formation, precipitation, and atmospheric rivers
  • Decadal climate variability and seasonal- cycle changes
  • El Niño and La Niña influences
  • Land- atmosphere interactions (e.g., deforestation, soil moisture, evapotranspiration)
  • Evaluation and improvement of isotope- enabled climate models (GCMs)

Satellites provide coverage of remote regions (oceans, poles) that ground instruments cannot easily reach and offer consistent long-term records.

The project was supported by the U.S. National Science Foundation and NASA.

The public database and processing code lower barriers for the broader scientific community, students, and others studying Earth’s water cycle.

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WISO-SAT: A global database of stable hydrogen isotopes in water vapor from eight satellite missions

Overview

This is an open- access Data Descriptor paper introducing WISO- SAT, a harmonized multi- instrument database of monthly δD (deuterium excess and stable hydrogen isotope ratio, HDO/H₂O) and moisture concentration retrievals in atmospheric water vapor.

It compiles and standardizes data from eight satellite missions spanning 2002–2024:

  • SCIAMACHY
  • GOSAT
  • GOSAT-2
  • TROPOMI
  • TES
  • AIRS
  • CrIS
  • IASI

Key features

  • Products: Instrument- specific gridded NetCDF files at 1° × 1° and 5° × 5° horizontal resolutions.
  • Standardization innovations: Cross- platform bias correction and uncertainty quantification to enable intercomparison despite differences in time periods, altitudes, formats, and instrument characteristics.
  • Open resources: Public repository with the NetCDF files and preprocessing and standardization codes. Example notebooks and scripts are included for model–satellite averaging-kernel application and regression- based bias correction (e.g., for surface δD comparisons).

Scientific motivation and applications

Stable hydrogen isotopes (especially δD) serve as tracers of the hydrological cycle and climate dynamics. Prior satellite records were fragmented, making multi- mission analysis labor- intensive. WISO- SAT fills observational gaps (especially remote regions) and supports:

  • Studies of decadal climate variability and seasonal- cycle changes
  • Process- level research on moisture transport, rain formation and evaporation, atmospheric rivers, etc.
  • Evaluation and development of isotope-enabled general circulation models (GCMs)
  • Broader atmospheric science and hydrology applications

Authors and funding

Corresponding author: Mampi Sarkar (Rice University).

Key co- authors include Sylvia Dee and Michelle Frazer (Rice University), plus collaborators from the University of Michigan, Oregon State University, SRON (Netherlands), University of Bremen, Karlsruhe Institute of Technology, NASA GISS, Jet Propulsion Laboratory (California Institute of Technology), and University of Bergen.⁠

Funding includes U.S. National Science Foundation awards (CAREER 2338861, SCI- SWIM 2513024) and NASA support (including the NEWS SPICE project and JPL contract).

Access

  • Article page: https://www.nature.com/articles/s41597-026-08218-8 (Open Access under CC BY-NC-ND 4.0).
  • PDF download link is available on the page.
  • Supplementary information PDF is also provided.
  • The actual data repository (NetCDFs and code) is referenced in the paper as open-access.

This is the primary scientific publication behind the recent Rice University press coverage on the open water-isotope database.

Journal: Scientific Data (Nature)

DOI: 10.1038/s41597-026-08218-8

Published: 8 September 2026 (early access / accepted version)

Provided: Rice University

Authors: Mampi Sarkar,
Sylvia Dee,
Michelle Frazer,
Adriana Bailey,
Stephen Good,
Soumyajit Mandal,
Stefan Noël,
Matthias Schneider,
Hayoung Bong,
Kevin Bowman,
Hans Christian Steen-Larsen,
Marja Haagsma &
John Worden

Abstract

Stable isotopes of hydrogen, including deuterium (δD), provide a critical tracer for understanding Earth’s hydrological cycle and climate dynamics. Recognizing this, the last two decades have seen a number of remote sensing missions geared towards measuring the ratio of HDO/H2O in water vapor. At present, however, observational gaps persist in remote regions and across satellite platforms. Furthermore, these data span different time periods, altitudes, and are inconsistently formatted, complicating intercomparison. To address this, we describe WISO-SAT, a harmonized multi-instrument database of monthly δ D and moisture concentration retrievals from eight satellite missions (SCIAMACHY, GOSAT, GOSAT-2, TROPOMI, TES, AIRS, CrIS, IASI) distributed as instrument-specific gridded NetCDF products at 1° × 1° and 5° × 5° horizontal resolutions. Key innovations and standardization of these 8 data products are made through bias correction across platforms and by providing uncertainty quantification. The dataset spans 2002–2024, enabling studies of decadal climate variability and changes in the seasonal cycle. These standardized and publicly available water vapor isotope products address a critical gap in isotopic observations from satellites across different timelines, supports climate model evaluation and development (e.g., isotope-enabled GCMs), and process-level studies of atmospheric phenomena such as moisture transport, rain formation and evaporation, and atmospheric rivers. An open-access repository includes NetCDF files and preprocessing codes used to format each dataset. We also provide example notebooks and scripts demonstrating model-satellite averaging kernel application and regression-based bias correction for surface δ D comparison. WISO-SAT enables novel applications in atmospheric science and hydrology, and provides an open data product that can be used to validate the simulation of Earth’s water cycle in GCMs.

Stefani on the Sun vs. CO2 as climate drivers

Frank Stefani, of the Helmholtz-Zentrum Dresden-Rossendorf, Institute of Fluid Dynamics, has published a very interesting new paper that compares the solar “aa” index and CO2 emissions to global SST (sea surface temperatures using the HadSST4.2 dataset) and finds a CO2 sensitivity (TCR or the “Transient Climate Response”) of 1.1 to 1.4K. This is at the low end of the IPCC TCR range of 1.2 to 2.4K (IPCC, 2021, p. 93), but quite close to the values calculated by Lewis and Curry and Nicola Scafetta (Lewis & Curry, 2018), (Scafetta, 2023), and (Lewis, 2023). Scafetta found a plausible range of TCR (versus HadSST4.2) of 1.0K to 1.2K and Lewis & Curry report a range of 0.9K to 1.7K for TCR versus HadCRUT4.

From Watts Up With That?

By Andy May

Frank Stefani, of the Helmholtz-Zentrum Dresden-Rossendorf, Institute of Fluid Dynamics, has published a very interesting new paper that compares the solar “aa” index and CO2 emissions to global SST (sea surface temperatures using the HadSST4.2 dataset) and finds a CO2 sensitivity (TCR or the “Transient Climate Response”) of 1.1 to 1.4K. This is at the low end of the IPCC TCR range of 1.2 to 2.4K (IPCC, 2021, p. 93), but quite close to the values calculated by Lewis and Curry and Nicola Scafetta (Lewis & Curry, 2018), (Scafetta, 2023), and (Lewis, 2023). Scafetta found a plausible range of TCR (versus HadSST4.2) of 1.0K to 1.2K and Lewis & Curry report a range of 0.9K to 1.7K for TCR versus HadCRUT4. The estimates are compared in Table 1.

Table 1. Estimates of TCR, or Transient Climate Response to a doubling of CO2. Sources: (Stefani, 2026), (Lewis & Curry, 2018), (Scafetta, 2023), and (IPCC, 2021, p. 93).

TCR Estimates
AuthorBest EstimateRange: Low endRange: High endNote
Stefani1.26K1.11.42026
Lewis & Curry1.2K0.91.72018
Scafetta1.1K11.22023
IPCC AR61.8K1.22.4p. 93

All the estimates in table 1 are based on regression models of varying complexity and all attempt to account for the influence of solar variability. The Lewis and Curry estimate does not incorporate a solar activity proxy directly but does use the Atlantic Multidecadal Oscillation (AMO) as an indicator of natural climate variability, which is, in large part, solar variability. Stefani uses the aa index of geomagnetic activity, essentially how disturbed Earth’s magnetic field is by the sun. It is measured in nanoteslas (nT).

Nearly every observable form of solar variability is a magnetic phenomenon at its core, including sunspots, flares, and solar wind variability. Thus, the aa index is a good indicator of changes in the sun’s state and output. The aa index as a measure of solar-geomagnetic coupling has been measured consistently since 1868.

Table 1 shows that Stefani’s aa index + CO2 model compares well to Lewis and Curry’s AMO + CO2 model and Scafetta’s solar proxies + CO2 model. Scafetta uses three estimates of total solar irradiance (TSI) in his study, although he acknowledges that variations in TSI may be only ~20% of the sun’s total influence on Earth’s climate. None of these observation-based models support the high-end IPCC TCR estimate of 2.4K per doubling of CO2 or their best estimate of 1.8K, but all are near the lower end of the IPCC range. The Lewis (2023) correction to the Sherwood (2020) assessment of ECS and TCR relied upon in AR6 is not included in the table. However, after changing Sherwood’s subjective Bayesian assessment of multiple estimates of TCR to an objective Bayesian assessment, Lewis calculated a TCR of 1.37 to 1.4 depending upon the assumptions made. This is still close to the other observation-based objective estimates in Table 1.

Stefani found that the solar aa index can successfully predict HadSST4.2 up to 1990-2000 on its own. After 1990 to 2000 the role of CO2 in the regression increases significantly. In figure 1, Stefani’s robust aa index weight of 0.04 K/nT (that is the HadSST4.2 anomaly temperature in °C per the aa index in nanoteslas) plus CO2 with a sensitivity of 1.26K per doubling is shown as a thin dark gray line. It is compared to the HadSST4.2 anomaly (shown as black dots). His projected aa index plus CO2 at 1.26K per doubling function to 2100 is shown as a red line. The maximum departure in 2023 and 2024 looks startling, but we need to remember that this follows two strong El Niños (2018-19 & 2023-24) and the Hunga Tonga volcanic eruption. In particular the El Niño from June 2023 to May 2024 was one of the strongest El Niños on record. The HadSST4.2 global anomaly has been falling since September 2024. Thus, a portion of the difference between the aa index plus CO2 function and HadSST4.2 may just be ENSO, the Hunga Tonga eruption, and weather.

Figure 1. A plot of Stefani’s regression of the aa index on HadSST4.2 as the dark gray line, HadSST4.2 data are shown as black dots, and the aa index projection to 2100 is shown as a red line. Data source: (Stefani, 2026).

Stefani’s optimal model projection to 2100 assumes constant emissions of 30-50 Gt of CO2 per year (roughly current levels) plus a simple linear carbon sink model and predicts a global SST increase of 0.6°C (1.1°F) compared to the standard HadSST4.2 reference period of 1961-1990. Using pessimistic parameters (high CO2 emissions, a low sensitivity to the aa-index, and a high sensitivity to CO2) yields a 2100 temperature increase of ~1K over 1960-1990. This is still a benign result.

A word on Transient Climate Response

The transient climate response is the modeled warming due to increasing the atmospheric CO2 concentration by 1% each year until it doubles, which would take about 70 years. This is different from the commonly cited value of ECS, which is the equilibrium climate sensitivity, or the final warming due to a sudden doubling of CO2. ECS is an untestable number, since it would take over 1,000 years for the atmosphere to completely come to equilibrium after CO2 suddenly doubles. Given the implausible scenario, ECS can never be tested, except in a climate model. Thus, it is not a scientific quantity per Karl Popper (Popper, 1962). TCR on the other hand is very realistic and could be tested given enough time and effort.

Conclusions

The paper is a welcome addition to the growing group of observation-based estimates of climate sensitivity to CO2. It is appropriate that Stefani chose to create his model around HadSST4.2. SSTs are more stable than air temperatures on land and they respond mainly to changes in insolation, whether due to cloud cover changes or changes in the sun itself. The changes in SST due to the greenhouse effect are smaller for the reasons discussed in my previous post. I recommend the paper, it is interesting, significant, and a good read.

Works Cited

IPCC. (2021). Climate Change 2021: The Physical Science Basis. In V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, . . . B. Zhou (Ed.)., WG1. Retrieved from https://www.ipcc.ch/report/ar6/wg1/

Lewis, N. (2023, May). Objectively combining climate sensitivity evidence. Climate Dynamics, 60, 3139-3165. https://doi.org/10.1007/s00382-022-06468-x

Lewis, N., & Curry, J. (2018). The Impact of Recent Forcing and Ocean Heat Uptake Data on Estimates of Climate Sensitivity. Journal of Climate, 31, 6051-6071. DOI: https://doi.org/10.1175/JCLI-D-17-0667.1.

Popper, K. R. (1962). Conjectures and Refutations, The Growth of Scientific Knowledge. New York: Basic Books. Retrieved from http://ninthstreetcenter.org/Popper.pdf

Scafetta, N. (2023). Empirical assessment of the role of the Sun in climate change using balanced multi-proxy solar records. Geoscience Frontiers, 14(6). Retrieved from https://www.sciencedirect.com/science/article/pii/S1674987123001172

Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., J., P. M., Hargreaves, C., . . . Knutti, R. (2020, July 22). An Assessment of Earth’s Climate Sensitivity Using Multiple Lines of Evidence. Reviews of Geophysics, 58. https://doi.org/https://doi.org/10.1029/2019RG000678

Stefani, F. (2026). Solar and Anthropogenic Climate Drivers: An Updated Regression Model and Refined Forecast. Atmosphere, 17(3). https://doi.org/10.3390/atmos17030252

Climate Change Skeptics’ Arguments Are Inconvenient Facts, Won’t Ever Be Refuted!

Huge data gaps, crude models, system complexity and endless unknowns frustrate climate scientists

From NoTricksZone

By P Gosselin 

Huge data gaps, crude models, system complexity and endless unknowns frustrate climate scientists

The science of climate change is very complex and there are many different points of view. This post summarizes some of the strongest arguments of climate skeptics.

The earth’s system is chaotic and unpredictable. Today’s climate models are still crude given the earth’s huge complexity. (Symbol image: NASA)

1. Natural climate variability

The Earth’s climate has always changed in the past, often dramatically, even without human influence. The current warming is therefore likely just part of a natural cycle, and one that is totally capable of reversing on its own.

2. Climate models are still in their infancy

Climate models are complex computer programs that attempt to simulate the climate system. These models provide different results and are not able to accurately reproduce past climate changes. All are filled with assumptions and guesses.

The climate is a highly complex, chaotic system and so much of it is still poorly understood. Much remains a complete mystery, which means it is impossible to accurately model. Model outputs are thus unreliable.

Remember that with chaotic systems like weather and climate, even very small changes in the initial inputs, which are many, can be amplified over time, thus making long-term predictions impossible. That’s a hard fact of life that climate scientists have to accept.

3. The influence of the sun

The sun is the earth’s main energy supplier and its activity certainly greatly influences the climate. There are hundreds of publications that show this. Many of these impacts are poorly understood, and so climate scientists like to pretend they don’t exist.

4. Limited effects of trace gas CO2

Carbon dioxide (CO2) is a trace, “greenhouse” gas that human activities release into the atmosphere. Many scientific publications show CO2’s impact on global warming is overestimated.

5. Oceanic cycles hugely impact climate change

The ocean acts like the earth’s giant heat re-distributor. There are many cycles that impact climate. Ocean currents move warm water from the equator towards the poles, and from higher depths to lower depths, thus redistributing energy. Ocean cycles play a crucial role in regulating Earth’s temperature and weather patterns. Changes to these cycles can have significant consequences for global climate.

The heat content of the oceans is about 100 times that of the atmosphere, so even small heat redistribution changes can significantly impact the atmosphere above.

Predictions are difficult because there is little historical data available from the ocean depths and scientists can only speculate what the oceans will do next.

6. Economic consequences of climate change

Measures to combat climate change entail extremely high costs and are especially socially unbearable for the poor. Study after study suggest these costs far outweigh the negative consequences of climate change, which we are unable to steer in the first place.