Earth System Science Interdisciplinary Center

Accelerating climate-tech innovation in the formative phase is crucial to meeting climate goals. However, effective green industrial policies require an understanding of when and where to target policy interventions within the value chain. We conceptualize nascent value chains for climate-tech as product clusters and explore innovation patterns within and across them. We analyze 14 climate-tech sectors using early-stage private investments in over 3,600 North American firms (2006-2021). In terms of product clusters, only 15% of firms develop end products, while 59% provide components, manufacturing, or optimization products, and 26% develop services. Investment evolution reveals three patterns of innovation: maturing innovation (e.g., energy efficiency), ongoing innovation (e.g., energy storage), and emerging innovation (e.g., agriculture). This characterization of nascent value chains offers an analytical basis for green industrial policy, identifying critical structural segments for intervention and illustrating how different value chain positions can create varied opportunities and pathways for regional benefit.

Droughts are associated with widespread adverse impacts across social, economic and ecological systems (Wilhite, 1992; Wilhite et al., 2007). The frequency, severity and persistence of drought events have increased across much of the Earth’s surface over the 20th and 21st centuries, and increasingly droughts coincide with periods of elevated temperature generating compound hot-dry conditions (Dai, 2011; Mazdiyasni and AghaKouchak, 2015; Chiang et al., 2021; King et al., 2024; Gebrechorkos et al., 2025). However, drought is a complex natural hazard that can result from both natural climate variability and anthropogenic influence (Wilhite and Glantz, 1985; Wilhite, 2000). With less than 100 years of instrumental data across much of the Earth’s surface, and the most severe droughts developing over an extended period, extreme droughts are few (Brunner et al., 2021; Seneviratne et al., 2021; Williams et al., 2022). It is therefore challenging to characterise worst-case outcomes, which is crucial for informing adaptation efforts. Here we use the Palmer Drought Severity Index (PDSI) calculated from CMIP6 last millennium simulations and independent PDSI reconstructions from tree-ring width chronologies to characterise drought conditions over the last millennium and place recent extreme drought conditions within a longer-term context. The two datasets are used together to establish how the relative severity of extreme drought conditions varies between regions, the frequency with which these events occur, and the extent to which such events are generated by compound hot-dry conditions. Extreme value statistics are then applied to the three PDSI datasets to estimate the severity of 1-in-10, -50, and -100-year summer droughts and assess how these values vary between the datasets. The extremes of the last millennium are then compared to those of the instrumental period. Results highlight regions that are yet to experience droughts during the instrumental period that exceed those of the last millennium; these areas may be particularly vulnerable to future extremes, particularly where these are enhanced by anthropogenic warming.

Methane is the second most important anthropogenic greenhouse gas. Since 2007, atmospheric methane concentrations have resumed growth following a period of relative stabilization, with the growth rate accelerating in recent years. At the same time, a reversal in the isotopic trend of atmospheric methane suggests possible changes in sources driving the observed increase in atmospheric CH₄.In this study, we use a novel approach based on the Miller-Tans method, which uses short-term devations from a smooth background signal in observed time series of CH4 mole fraction and isotopic composition to infer δ¹³C-CH₄ source signatures of this short-term component. This allows us to focus on regional scale changes in methane sources .We use 20 years of long-term atmospheric methane and carbon isotope (δ¹³C-CH₄) observations from a global sampling network to conduct a comprehensive analysis of the spatial distribution, seasonal variability, and long-term trends of δ¹³C-CH₄ source signatures. In particular, we observe a pronounced decrease in δ¹³C-CH₄ source signatures at high northern latitudes after 2007, indicating a relative increase in isotopically depleted, biogenic methane sources in this region. By comparing our observation-based results with inverse modeling studies, we further discuss how this regional shift is reflected at the global scale and explore possible explanations underlying the substantial shifts in the source mix at high northern latitudes.

Aerosol absorption (scattering) property is a key parameter for assessing aerosol radiative effects and identifying aerosol composition. However, current geostationary Earth orbit (GEO) satellite aerosol retrieval algorithms lack accuracy in estimating aerosol absorption. The Geostationary Environment Monitoring Spectrometer (GEMS) provides hyperspectral observations of Earth-reflected solar radiation from 300 nm to 500 nm, which is sensitive to aerosol absorption. However, the current aerosol retrieval algorithm for GEMS struggles to quantify aerosol loading and aerosol absorption simultaneously. Meanwhile, the Advanced Meteorological Imager (AMI) conducts band observations of Earth-reflected solar radiation from 470 nm to 1,330 nm. The longer visible wavelength bands of AMI are less sensitive to assumption errors related to aerosol absorption properties. As a result, aerosol optical depth (AOD) products retrieved from AMI are generally more stable than those from GEMS. Therefore, synergistic use of the GEMS and the AMI can be more effective than using a single instrument to obtain both aerosol loading and absorption data. Furthermore, a wide range of wavelength from UV to visible is covered by using both GEMS and AMI. This study presents sensitivity analyses and preliminary results of a synergistic retrieval algorithm for aerosol spectral absorption properties using synergistic observations from GEMS and AMI. The algorithm incorporates a Transformer-based deep learning model for radiative transfer (RT) calculations. By replacing the traditional line-by-line RT code with a deep learning model, the algorithm enables real-time RT calculations embedded within the retrieval process. This online RT calculation approach enhances the flexibility of the aerosol retrieval algorithm and reduces errors that arise from look-up table interpolation. The developed algorithm can work on other GEO-ring missions such as GeoXO, TEMPO, ABI, Sentinel-4, and FCI.

Monitoring plant water stress requires plant hydraulic measurements, such as measurements of water potential. However, such measurements are challenging to make at scales beyond single plants and over extended time periods. Observing plant water conditions across broad spatiotemporal scales is now enabled by passive microwave remote sensing. Specifically, vegetation optical depth (VOD) retrieved from satellite radiometers (SMAP, AMSR) provides a measure of vegetation water volume in the canopy at tens of kilometers. While satellite-based VOD has been used for a range of applications, rigorous validations of satellite VOD have not been carried out due to a need for labor intensive, widespread in-situ biomass and plant water potential measurements. A new method has enabled direct measurements of in-situ VOD, from Global Navigation Satellite Systems (GNSS). However, they have been less commonly used to evaluate shorter statured vegetation, which dominates most ecosystems. Here, we explore how satellite-based VOD from SMAP and AMSR-2 compare with field-based microwave observations from 272 GNSS-based interferometric reflectometry (GNSS-IR) sites located throughout the Western U.S as a part of the Plate Boundary Observatory (PBO) H20 network. These sensors use GNSS signals to estimate a normalized microwave reflectance index (NMRI), a proxy for VOD at a scale of tens of meters. We find that satellite VOD generally positively correlates with GNSS NMRI with correlations between 0.2 to 0.6 across sites, which is encouraging considering the vast differences in spatial scale (10s of meters for field sensors versus 10s of kilometers for the satellites). These correlations increase to 0.3 to 0.7 when evaluating sites in regions with low spatial vegetation type heterogeneity, low tree cover, and large seasonal vegetation dynamics. The correlations are higher for X-band VOD, likely related to our finding that both X-band VOD and NMRI are both more sensitive to seasonal vegetation variations relative to daily-scale responses than C-band and L-band VOD products are. These findings suggest that satellite VOD is capturing field-based GNSS signals, and therefore that these sensors are a critical (and arguably the only feasible) resource for calibrating and validating satellite VOD across spatial scales.

The atmospheric methane (CH4) growth rate surged after 2019, peaking at 16.2 parts per billion per year (ppb year-1) in 2020 before declining to 8.6 ppb year-1 in 2023. Using multiple atmospheric inversions constrained by observation- and model-based prescribed hydroxyl radical (OH) fields and CH4 atmospheric data, we show that a drop of OH radicals in 2020-2021, followed by recovery in 2022-2023, accounted for 83% of year-on-year variations in the CH4 growth rate, the rest being explained by wetland and inland water emissions, which increased between 2019 and 2020-2022 [+8.6 ± 2.6 teragrams of CH4 per year (TgCH4 year-1)] and then decreased between 2022 and 2023 (-9.9 ± 3.3 TgCH4 year-1). Most emission changes from 2019 to 2023 occurred in northern tropical wetlands in Africa and Asia, whereas South American wetlands emissions declined and Arctic emissions increased after 2019.

Abstract The El Niño–Southern Oscillation (ENSO) Transition Mode (ETM) is a distinct Southern Hemisphere mode characterized by a multidecadal seesaw in boreal spring sea level pressure about the date line in the southern Pacific Ocean. ETM significantly influences the equatorial winds and plays a key role in shaping ENSO’s boreal winter-to-summer seasonal transition. This study combines observational data and climate model simulations to examine how ETM affects global rainfall during boreal summer at multidecadal time scales. During its positive phase, ETM is linked to a cooler Northern Hemisphere, especially the northern Pacific, and a warmer Southern Hemisphere with maximum warming over the southern Indian Ocean. This creates an unfavorable interhemispheric sea surface temperature (SST) gradient, weakening the large-scale atmospheric circulation by reducing cross-equatorial mass flux and decreasing rainfall over the regions such as South Asia and the Sahel. These results underscore the significant impact of the Southern Hemisphere variability on the global climate patterns at multidecadal time scales. Significance Statement Understanding the monsoon variability in the Northern Hemisphere at decadal-to-multidecadal time scales can benefit billions of climate vulnerable people. While some studies have reported a significant increasing trend in the monsoons over the Northern Hemisphere in recent decades, it is unclear whether these changes are due to natural climate variability or anthropogenic changes, especially from the Southern Hemisphere. Using observations and atmospheric general circulation model (AGCM) simulations, we demonstrate that the natural El Niño–Southern Oscillation (ENSO) Transition Mode (ETM) from the Southern Hemisphere plays a crucial role in modulating boreal summer monsoons at multidecadal time scales. During its positive phase, a colder northern Pacific and a warmer southern Indian Ocean intensify the negative interhemispheric sea surface temperature (SST) gradient, which weakens the flow from the Southern Hemisphere and leads to a decline in rainfall over South Asia and the Sahel.

<strong class="journal-contentHeaderColor">Abstract.</strong> We present an updated evaluation of stratospheric ozone profile trends in the 60&deg;S&ndash;60&deg;N latitude range using long-term ground-based and satellite climate data records, as well as simulations by chemistry-climate models. The trends are evaluated using the LOTUS (Long-term Ozone Trends and Uncertainties in the Stratosphere) regression model. Analyses of satellite data confirm statistically significant positive ozone trends in the period 2000&ndash;2024 in the upper stratosphere of ~1&ndash;3% decade^-1, with larger trends at mid-latitudes compared to the tropics. The trends are slightly positive or close to zero in the middle stratosphere, and mostly negative, &minus;1&ndash;2% decade^-1, in the lower stratosphere; but they are not statistically significant. The ozone trends are similar to previous analyses (2000&ndash;2020 trends). Ozone trends in 2000&ndash;2024 predicted by climate model simulations are in good agreement with combined satellite trends. In the upper stratosphere, models predict a slightly stronger ozone recovery than observations. In the lower stratosphere, both models and satellite observations report negative trends of in the tropics, while modelled ozone trends are slightly positive at mid-latitudes. Ozone profile trends over several stations estimated from ground-based records capture the same overall vertical pattern of ozone trends as merged gridded satellite datasets. Analyses of regional ozone trends in 2003&ndash;2024 using merged satellite datasets confirmed the previous observations of a longitudinal structure in ozone trends in the NH mid-latitude stratosphere, with positive trends over Scandinavia and negative trends over Siberia. However, the magnitude of this dipole-like structure is reduced compared to previous analyses.

Accurately tracking the global distribution of precipitation is essential for both research and operational meteorology. Satellite observations remain the only means of achieving consistent, global precipitation monitoring. While machine learning has long been applied to satellite-based precipitation retrieval, the absence of a standardized benchmark dataset has hindered fair comparisons between methods. To address this, the International Precipitation Working Group has developed SatRain, the first AI benchmark dataset for satellite-based detection and estimation of rain. SatRain integrates multi-sensor satellite observations from the primary platforms used in precipitation remote sensing with high-quality reference precipitation estimates derived from gauge-corrected ground-based radar composites over the conterminous United States. It offers a standardized evaluation protocol and out-of-distribution testing data from Asia and Europe to enable robust and reproducible comparisons across machine learning approaches. In addition to algorithm evaluation, the diversity of sensors and inclusion of time-resolved geostationary observations make SatRain a valuable foundation for developing next-generation AI models to deliver more accurate global precipitation estimates.