The 50 most-cited articles in Geophysical Research Letters, 2021

Abstract Earth's Energy Imbalance (EEI) is a relatively small (presently ∼0.3%) difference between global mean solar radiation absorbed and thermal infrared radiation emitted to space. EEI is set by natural and anthropogenic climate forcings and the climate system's response to those forcings. It is also influenced by internal variations within the climate system. Most of EEI warms the ocean; the remainder heats the land, melts ice, and warms the atmosphere. We show that independent satellite and in situ observations each yield statistically indistinguishable decadal increases in EEI from mid‐2005 to mid‐2019 of 0.50 ± 0.47 W m −2 decade −1 (5%–95% confidence interval). This trend is primarily due to an increase in absorbed solar radiation associated with decreased reflection by clouds and sea‐ice and a decrease in outgoing longwave radiation (OLR) due to increases in trace gases and water vapor. These changes combined exceed a positive trend in OLR due to increasing global mean temperatures.

Abstract The potential for carbonate clumped isotope thermometry to independently constrain both the formation temperature of carbonate minerals and fluid oxygen isotope composition allows insight into long‐standing questions in the Earth sciences, but remaining discrepancies between calibration schemes hamper interpretation of temperature measurements. To address discrepancies between calibrations, we designed and analyzed a sample suite (41 total samples) with broad applicability across the geosciences, with an exceptionally wide range of formation temperatures, precipitation methods, and mineralogies. We see no statistically significant offset between sample types, although the comparison of calcite and dolomite remains inconclusive. When data are reduced identically, the regression defined by this study is nearly identical to that defined by four previous calibration studies that used carbonate‐based standardization; we combine these data to present a composite carbonate‐standardized regression equation. Agreement across a wide range of temperature and sample types demonstrates…

Abstract How long will the four seasons be by 2100? Increasing evidence suggests that the length of a single season or in regional scales has changed under global warming, but a hemispherical‐scale response of the four seasons in the past and future remains unknown. We find that summer in the Northern Hemisphere midlatitudes has lengthened, whereas winter has shortened, owing to shifts in their onsets and withdrawals, accompanied by shorter spring and autumn. Such changes in lengths and onsets can be mainly attributed to greenhouse‐warming. Even if the current warming rate does not accelerate, changes in seasons will still be exacerbated in the future. Under the business‐as‐usual scenario, summer is projected to last nearly half a year, but winter less than 2 months by 2100. The changing seasonal clock signifies disturbed agriculture seasons and rhythm of species activities, more frequent heat waves, storms and wildfires, amounting to increased risks to humanity.

Abstract Arctic Amplification is robustly seen in climate model simulations of future warming and in the paleoclimate record. Here, we focus on the past century of observations. We show that Arctic Amplification is only a recent phenomenon, and that for much of this period the Arctic cooled while the global‐mean temperature rose. To investigate why this occurred, we analyze large ensembles of comprehensive climate model simulations under different forcing scenarios. Our results suggest that the global warming from greenhouse gases was largely offset in the Arctic by regional cooling due to aerosols, with internal climate variability also contributing to Arctic cooling and global warming trends during this period. This suggests that the disruption of Arctic Amplification was due to a combination of factors unique to the 20th century, and that enhanced Arctic warming should be expected to be a consistent feature of climate change over the coming century.

Abstract Multiple lines of evidence suggest climate change will result in increased precipitation variability and consequently more frequent extreme events. These hydroclimatic changes will likely have significant socioecological impacts, especially across water‐limited regions. Here we present an analysis of daily meteorological observations from 1976 to 2019 at 337 long‐term weather stations distributed across the western United States (US). In addition to widespread warming (0.2 °C ± 0.01°C/decade, daily maximum temperature), we observed trends of reduced annual precipitation (−2.3 ± 1.5 mm/decade) across most of the region, with increasing interannual variability of precipitation. Critically, daily observations showed that extreme‐duration drought became more common, with increases in both the mean and longest dry interval between precipitation events (0.6 ± 0.2, 2.4 ± 0.3 days/decade) and greater interannual variability in these dry intervals. These findings indicate that, against a backdrop of warming and drying, large regions of the western US are experiencing intensification…

Abstract The Southern Hemisphere (SH) surface westerlies fundamentally control regional patterns of air temperature, storm tracks, and precipitation while also regulating ocean circulation, heat transport and carbon uptake. Wind‐forced ocean perturbation experiments commonly apply idealized poleward wind shifts ranging between 0.5 and 10 degrees of latitude and wind intensification factors of between 10% and 300%. In addition, changes in winds are often prescribed ad hoc as a zonally uniform anomaly that neglects important regional and seasonal differences. Here we quantify historical and projected SH westerly wind changes based on examination of CMIP5, CMIP6, and reanalysis data. We find a significant reduction in the location bias of the CMIP6 ensemble and an associated reduction in the projected poleward shift compared to CMIP5. Under a high emission scenario, we find a projected end of 21st Century ensemble mean wind increase of ∼10% and a poleward shift of ∼0.8° latitude, although there are important seasonal and regional variations.

Abstract The Gravity Recovery and Climate Experiment (GRACE) mission has monitored global total water storage anomalies (TWSA) with an unprecedented accuracy since 2002. Yet, many applications require a longer record, that is, extending prior to the GRACE period. Here, we present a new global reconstruction of long‐term (1979–2020) TWSA fields by combining machine learning with time series decomposition and statistical decomposition techniques. We find that the long‐term TWSA reconstructed from GRACE fits well with the GRACE‐FO observation over most grids (0.5° resolution) of the global land and successfully reproduces the strong El Niño signal. Comparisons to Satellite Laser Ranging solutions and to observed global mean sea level change suggest our reconstruction (doi: https://doi.org/10.5061/dryad.z612jm6bt ) is more reliable than previously published products. This study provides a viable approach for both reconstructing past TWSA and filling the GRACE data gap at the global scale.

Abstract The present study aims to map forest canopy height by integrating ICESat‐2 and Sentinel‐1 data and investigate the effect of integrating forest canopy height information with Sentinel‐2 data‐derived spectral variables on the prediction of spatial distribution of forest aboveground biomass (AGB). Random forest (RF) algorithm was used to develop forest canopy height and AGB models. It was observed that ICESat‐2 and Sentinel‐1 based model was able to predict forest canopy height with R2 = 0.84 and %RMSE = 4.48%. Two forest AGB models were developed, with only spectral variables and by incorporating forest height information with spectral variables. The results reflected that incorporation of forest canopy height in the forest AGB model improved the accuracy of the AGB predictions (R2 = 0.83, %RMSE = 4.64%). The study presents a comprehensive methodology for mapping forest canopy height and AGB.

Abstract The Pacific inflow to the Arctic traditionally brings heat in summer, melting sea ice; dense waters in winter, refreshing the Arctic’s cold halocline; and nutrients year‐round, supporting Arctic ecosystems. Bering Strait moorings from 1990 to 2019 find increasing (0.010 ± 0.006 Sv/yr) northward flow, reducing Chukchi residence times by ∼1.5 months over this period (record maximum/minimum ∼7.5 and ∼4.5 months). Annual mean temperatures warm significantly (0.05 ± 0.02°C/yr), with faster change (∼0.1°C/yr) in warming (June/July) and cooling (October/November) months, which are now 2°C to 4°C above climatology. Warm (≥0°C) water duration increased from 5.5 months (the 1990s) to over 7 months (2017), mostly due to earlier warming (1.3 ± 0.7 days/yr). Dramatic winter‐only (January–March) freshening (0.03 psu/yr) makes winter waters fresher than summer waters. The resultant winter density change, too large to be compensated by Chukchi sea‐ice processes, shoals the Pacific Winter Water (PWW) equilibrium depth in the Arctic from 100–150 to 50–100 m, implying PWW no longer ventilates…

Abstract Supraglacial debris affects glacier mass balance as a thin layer enhances surface melting, while a thick layer reduces it. While many glaciers are debris‐covered, global glacier models do not account for debris because its thickness is unknown. We provide the first globally distributed debris thickness estimates using a novel approach combining sub‐debris melt and surface temperature inversion methods. Results are evaluated against observations from 22 glaciers. We find the median global debris thickness is ∼0.15 ± 0.06 m. In all regions, the net effect of accounting for debris is a reduction in sub‐debris melt, on average, by 37%, which can impact regional mass balance by up to 0.40 m water equivalent (w.e.) yr‐1. We also find recent observations of similar thinning rates over debris‐covered and clean ice glacier tongues is primarily due to differences in ice dynamics. Our results demonstrate the importance of accounting for debris in glacier modeling efforts.

Abstract Hydrogen is a clean fuel that can potentially revolutionize the energy supply chain and decarbonize fuel consumption. However, a key hurdle that needs to be overcome before a full‐scale hydrogen economy can be established is hydrogen storage which is currently the main limitation. Here, we propose that hydrogen gas can be stored in underground coal seams, where it adsorbs on the coal surface. However, currently, no hard data related to such a procedure exists. We, therefore, demonstrate experimentally that significant amounts of hydrogen gas can be stored via this route. Hydrogen adsorption capacities reached 0.6 moles H2/kg of coal at 14.3 MPa, and adsorption capacity initially increased strongly with pressure (up to ∼4 MPa) and then plateaued out, while temperature only had a very minor influence. This study provides fundamental data for hydrogen storage in coal seams, and thus aids in the industrial implementation of a hydrogen supply chain.

Abstract Australian wildfires burning from December 2019 to January 2020 injected approximately 0.9 Tg of smoke into the stratosphere; this is the largest amount observed in the satellite era. A comparison of numerical simulations to satellite observations of the plume rise suggests that the smoke mass contained 2.5% black carbon. Model calculations project a 1 K warming in the stratosphere of the Southern Hemisphere midlatitudes for more than 6 months following the injection of black‐carbon containing smoke. The 2020 average global mean clear sky effective radiative forcing at top of atmosphere is estimated to be −0.03 W m−2 with a surface value of −0.32 W m−2. Assuming that smoke particles coat with sulfuric acid in the stratosphere and have similar heterogeneous reaction rates as sulfate aerosol, we estimate a smoke‐induced chemical decrease in total column ozone of 10–20 Dobson units from August to December in mid‐high southern latitudes.

Abstract The future surface mass balance (SMB) of Antarctic ice shelves has not been constrained with models of sufficient resolution and complexity. Here, we force the high‐resolution Modèle Atmosphérique Régional with future simulations from four CMIP models to evaluate the likely effects on the SMB of warming of 1.5°C, 2°C, and 4°C above pre‐industrial temperatures. We find non‐linear growth in melt and runoff which causes SMB to become less positive with more pronounced warming. Consequently, Antarctic ice shelves may be more likely to contribute indirectly to sea level rise via hydrofracturing‐induced collapse, which facilitates accelerated glacial discharge. Using runoff and melt as indicators of ice shelf stability, we find that several Antarctic ice shelves (Larsen C, Wilkins, Pine Island, and Shackleton) are vulnerable to disintegration at 4°C. Limiting 21st century warming to 2°C will halve the ice shelf area susceptible to hydrofracturing‐induced collapse compared to 4°C.

We present a new solar irradiance reference spectrum representative of solar minimum conditions between solar cycles 24 and 25. The Total and Spectral Solar Irradiance Sensor-1 (TSIS-1) Hybrid Solar Reference Spectrum (HSRS) is developed by applying a modified spectral ratio method to normalize very high spectral resolution solar line data to the absolute irradiance scale of the TSIS-1 Spectral Irradiance Monitor (SIM) and the CubeSat Compact SIM (CSIM). The high spectral resolution solar line data are the Air Force Geophysical Laboratory ultraviolet solar irradiance balloon observations, the ground-based Quality Assurance of Spectral Ultraviolet Measurements In Europe Fourier transform spectrometer solar irradiance observations, the Kitt Peak National Observatory solar transmittance atlas, and the semi-empirical Solar Pseudo-Transmittance Spectrum atlas. The TSIS-1 HSRS spans 202-2730 nm at 0.01 to ∼0.001 nm spectral resolution with uncertainties of 0.3% between 460 and 2365 nm and 1.3% at wavelengths outside that range.

Abstract Extreme heat research has largely focused on dry‐heat, while humid‐heat that poses a substantial threat to human‐health remains relatively understudied. Using hourly high‐resolution ERA5 reanalysis and HadISD station data, we provide the first spatially comprehensive, global‐scale characterization of the magnitude, seasonal timing, and frequency of dry‐ and wet‐bulb temperature extremes and their trends. While the peak dry‐ and humid‐heat extreme occurrences often coincide, their timing differs in climatologically wet regions. Since 1979, dry‐ and humid‐heat extremes have become more frequent over most land regions, with the greatest increases in the tropics and Arctic. Humid‐heat extremes have increased disproportionately over populated regions (∼5.0 days per‐person per‐decade) relative to global land‐areas (∼3.6 days per‐unit‐land‐area per‐decade) and population exposure to humid‐heat has increased at a faster rate than to dry‐heat. Our study highlights the need for a multivariate approach to understand and mitigate future harm from heat stress in a warming world.

Abstract Concepts like the 100‐year flood event can be misleading if they are not updated to reflect significant changes over time. Here, we model observed annual maximum daily streamflow using a nonstationary approach to provide the first global picture of changes in: (a) the magnitudes of the 20‐, 50‐, and 100‐year floods (i.e., flows of a given exceedance probability in each year); (b) the return periods of the 20‐, 50‐, and 100‐year floods, as assessed in 1970 (i.e., flows of a fixed magnitude); and (c) corresponding flood probabilities. Empirically, we find the 20‐/50‐year floods have mostly increased in temperate climate zones, but decreased in arid, tropical, polar, and cold zones. In contrast, 100‐year floods have mostly decreased in arid/temperate zones and exhibit mixed trends in cold zones, but results are influenced by the small number of stations with long records, and highlight the need for continued updating of hazard assessments.

Abstract Predicting discharge in contiguously data‐scarce or ungauged regions is needed for quantifying the global hydrologic cycle. We show that prediction in ungauged regions (PUR) has major, underrecognized uncertainty and is drastically more difficult than previous problems where basins can be represented by neighboring or similar basins (known as prediction in ungauged basins). While deep neural networks demonstrated stellar performance for streamflow predictions, performance nonetheless declined for PUR, benchmarked here with a new stringent region‐based holdout test on a US data set with 671 basins. We tested approaches to reduce such errors, leveraging deep network's flexibility to integrate “soft” data, such as satellite‐based soil moisture product, or daily flow distributions which improved low flow simulations. A novel input‐selection ensemble improved average performance and greatly reduced catastrophic failures. Despite challenges, deep networks showed stronger performance metrics for PUR than traditional hydrologic models. They appear competitive for geoscientific…

Abstract Intense and frequent new particle formation (NPF) events have been observed in polluted urban environments, yet the dominant mechanisms are still under debate. To understand the key species and governing processes of NPF in polluted urban environments, we conducted comprehensive measurements in downtown Beijing during January–March, 2018. We performed detailed analyses on sulfuric acid cluster composition and budget, as well as the chemical and physical properties of oxidized organic molecules (OOMs). Our results demonstrate that the fast clustering of sulfuric acid (H2SO4) and base molecules triggered the NPF events, and OOMs further helped grow the newly formed particles toward climate‐ and health‐relevant sizes. This synergistic role of H2SO4, base species, and OOMs in NPF is likely representative of polluted urban environments where abundant H2SO4 and base species usually co‐exist, and OOMs are with moderately low volatility when produced under high NOx concentrations.

Abstract Changes in atmospheric composition, such as increasing greenhouse gases, cause an initial radiative imbalance to the climate system, quantified as the instantaneous radiative forcing. This fundamental metric has not been directly observed globally and previous estimates have come from models. In part, this is because current space‐based instruments cannot distinguish the instantaneous radiative forcing from the climate’s radiative response. We apply radiative kernels to satellite observations to disentangle these components and find all‐sky instantaneous radiative forcing has increased 0.53 ± 0.11 W/m 2 from 2003 to 2018, accounting for positive trends in the total planetary radiative imbalance. This increase has been due to a combination of rising concentrations of well‐mixed greenhouse gases and recent reductions in aerosol emissions. These results highlight distinct fingerprints of anthropogenic activity in Earth’s changing energy budget, which we find observations can detect within 4 years.

Throughout spring and summer 2020, ozone stations in the northern extratropics recorded unusually low ozone in the free troposphere. From April to August, and from 1 to 8 kilometers altitude, ozone was on average 7% (≈4 nmol/mol) below the 2000-2020 climatological mean. Such low ozone, over several months, and at so many stations, has not been observed in any previous year since at least 2000. Atmospheric composition analyses from the Copernicus Atmosphere Monitoring Service and simulations from the NASA GMI model indicate that the large 2020 springtime ozone depletion in the Arctic stratosphere contributed less than one-quarter of the observed tropospheric anomaly. The observed anomaly is consistent with recent chemistry-climate model simulations, which assume emissions reductions similar to those caused by the COVID-19 crisis. COVID-19 related emissions reductions appear to be the major cause for the observed reduced free tropospheric ozone in 2020.

Abstract Global greening trends have been widely reported based on long‐term remote sensing data of terrestrial ecosystems. Typically, a hypothesis test is performed for each grid cell; this leads to multiple hypothesis testing and false positive trend detection. We reanalyze global greening and account for this issue with a novel statistical method that allows robust inference on greening regions. Based on leaf area index (LAI) data, our methods reduce the detected greening from 35.2% to 15.3% of the terrestrial land surface; this reduction is most notable in nonwoody vegetation. Our results confirm several greening regions (China, India, Europe, Sahel, North America, Brazil, and Siberia), that are also supported by independent data products. We also report evidence for an increasing seasonal amplitude in LAI north of 35°N. Considering the widespread use of spatially replicated trend tests in global change research, we recommend adopting the proposed multiple testing procedure to control false positive outcomes.

Abstract While global warming has generally increased the occurrence of extreme precipitation, the physical mechanisms by which climate change alters regional and local precipitation extremes remain uncertain, with debate about the role of changes in the atmospheric circulation. We use a convolutional neural network (CNN) to analyze large‐scale circulation patterns associated with U.S. Midwest extreme precipitation. The CNN correctly identifies 91% of observed precipitation extremes based on daily sea level pressure and 500‐hPa geopotential height anomalies. There is evidence of increasing frequency of extreme precipitation circulation patterns (EPCPs) over the past two decades, although frequency changes are insignificant over the past four decades. Additionally, we find that moisture transport and precipitation intensity during EPCPs have increased. Our approach, which uses deep learning visualization to understand how the CNN predicts EPCPs, advances machine learning as a tool for providing insight into physical causes of changing extremes, potentially reducing uncertainty in…

Abstract Nowcasting of convective storms is urgently needed yet rather challenging. Current nowcasting methods are mostly based on radar echo extrapolation, which suffer from the insufficiency of input information and ineffectiveness of model architecture. A novel deep‐learning (DL) model, FURENet, is designed for extracting information from multiple input variables to make predictions. Polarimetric radar variables, K DP and Z DR , which provide extra microphysics and dynamic structure information of storms, are fed into the model to improve nowcasting. Two representative cases indicate that K DP and Z DR can help the DL model better forecast convective organization and initiation. Quantitative statistical evaluation shows using FURENet, K DP , and Z DR synergistically improve nowcasting skills (CSI score) by 13.2% and 17.4% for the lead time of 30 and 60 min, respectively. Further evaluation shows the microphysical information provided by the polarimetric variables can enhance the DL model in understanding the evolution of convective storms and making more trustable nowcasts.

Abstract Understanding warming on the Antarctic shelf is critical for projecting changes in Antarctic ice shelves and ice sheets. Here we assess Antarctic Shelf Bottom Water (ASBW) temperature mean‐state and trends in CMIP6 models. While CMIP6 models do not resolve ice shelves, future shelf water warming will impact ice shelf vulnerability. The CMIP6 multi‐model mean zonal temperature structure and mean‐state ASBW spatial pattern resemble observations, although there is considerable spread across the models and a multi‐model mean warm bias. The multi‐model mean projects an average ASBW warming of 0.36°C (interdecile range 0.07°C–0.60°C) under SSP245 and 0.62°C (interdecile range 0.16°C–0.95°C) under SSP585 by 2100, emphasizing the influence future emissions have on shelf water warming around Antarctica. Changes in the transport of Circumpolar Deep Water onto the shelf associated with changes in the Southern Annular Mode, as well as Circumpolar Deep Water warming, are predicted to conspire to warm ASBW in the future.

Abstract Projections of the sea level contribution from the Greenland and Antarctic ice sheets (GrIS and AIS) rely on atmospheric and oceanic drivers obtained from climate models. The Earth System Models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) generally project greater future warming compared with the previous Coupled Model Intercomparison Project phase 5 (CMIP5) effort. Here we use four CMIP6 models and a selection of CMIP5 models to force multiple ice sheet models as part of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). We find that the projected sea level contribution at 2100 from the ice sheet model ensemble under the CMIP6 scenarios falls within the CMIP5 range for the Antarctic ice sheet but is significantly increased for Greenland. Warmer atmosphere in CMIP6 models results in higher Greenland mass loss due to surface melt. For Antarctica, CMIP6 forcing is similar to CMIP5 and mass gain from increased snowfall counteracts increased loss due to ocean warming.

Abstract The record‐long 2020 Meiyu season since 1961 caused severe floods over the Yangtze and Huaihe River valleys (YHRV). Why the Meiyu duration doubled in 2020 remains a puzzle. We show that the long‐lasting Meiyu can be divided into three stages: advanced‐onset, strong‐persisting, and delayed‐withdrawal. The advanced‐onset was associated with an extremely negative‐phase East Atlantic/West Russia teleconnection. The strong‐persisting was attributed to a positive‐phase Pacific–Japan (PJ) pattern sustained by La Niña's rapid development. The delayed withdrawal was related to the combined effect of a positive PJ pattern and a mid‐troposphere “two ridge–one trough” pattern over Asia. Two subseasonal forecasting systems predicted the positive rainfall anomalies over the YHRV in the first two stages, but not the third stage, which may be associated with poor prediction of the two ridge–one trough pattern. These results highlight the role of subseasonal evolution in extreme climate events.

Abstract In September 2020, extremely strong wildfires in the western United States of America (i.e., mainly in California) produced large amounts of smoke, which was lifted into the free troposphere. These biomass‐burning‐aerosol (BBA) layers were transported from the US west coast toward central Europe within 3–4 days turning the sky milky and receiving high media attention. The present study characterizes this pronounced smoke plume above Leipzig, Germany, using a ground‐based multiwavelength‐Raman‐polarization lidar and the aerosol/cloud product of ESA’s wind lidar mission Aeolus. An exceptional high smoke‐AOT >0.4 was measured, yielding to a mean mass concentration of 8 μg m −3 . The 355 nm lidar ratio was moderate at around 40–50 sr. The Aeolus‐derived backscatter, extinction and lidar ratio profiles agree well with the observations of the ground‐based lidar PollyXT considering the fact that Aeolus’ aerosol and cloud products are still preliminary and subject to ongoing algorithm improvements.

Abstract Shallow nearshore coastal waters provide a wealth of societal, economic, and ecosystem services, yet their topographic structure is poorly mapped due to a reliance upon expensive and time intensive methods. Space‐borne bathymetric mapping has helped address these issues, but has remained largely dependent upon in situ measurements. Here we fuse ICESat‐2 lidar data with Sentinel‐2 optical imagery, within the Google Earth Engine cloud platform, to create openly available spatially continuous high‐resolution bathymetric maps at regional‐to‐national scales in Florida, Crete and Bermuda. ICESat‐2 bathymetric classified photons are used to train three Satellite Derived Bathymetry (SDB) methods, including Lyzenga, Stumpf, and Support Vector Regression algorithms. For each study site the Lyzenga algorithm yielded the lowest RMSE (approx. 10%–15%) when compared with validation data. We demonstrate a means of using ICESat‐2 for both model calibration and validation, thus cementing a pathway for fully space‐borne estimates of nearshore bathymetry in shallow, clear water environments.

Abstract Several very large high‐impact fires burned nearly 4,000 km2 of mesic forests in western Oregon during September 7–9, 2020. While infrequent, very large high‐severity fires have occurred historically in western Oregon, the extreme nature of this event warrants analyses of climate and meteorological drivers. A strong blocking pattern led to an intrusion of dry air and strong downslope east winds in the Oregon Cascades following a warm‐dry 60‐day period that promoted widespread fuel flammability. Viewed independently, both the downslope east winds and fuel dryness were extreme, but not unprecedented. However, the concurrence of these drivers resulted in compound extremes and impacts unmatched in the observational record. We additionally find that most large wildfires in western Oregon since 1900 have similarly coincided with warm‐dry summers during at least moderate east wind events. These results reinforce the importance of incorporating a multivariate lens for compound extremes in assessing wildfire hazard risk.

Abstract Due to limited resolution and inaccurate physical parameterizations, weather and climate models consistently develop biases compared to the observed atmosphere. Using the FV3GFS model at coarse resolution, we propose a method of machine learning corrective tendencies from a hindcast simulation nudged toward observational analysis. We show that a random forest can predict the nudging tendencies from this hindcast simulation with moderate skill using only the model state as input. This random forest is then coupled to FV3GFS, adding corrective tendencies of temperature, specific humidity and horizontal winds at each timestep. The coupled model shows no signs of instability in year‐long simulations and has significant reductions in short‐term forecast error for 500 hPa height, surface pressure and near‐surface temperature. Furthermore, the root mean square error of the annual‐mean precipitation is reduced by about 20%. Biases of other variables remain similar or in some cases, like upper‐atmospheric temperature, increase in the year‐long simulations.

Abstract Extreme summer precipitation often associated with flash floods has devastating impact on the local economies and livelihood of millions of people over East China. Tracking down the drivers of those extreme events will help to understand their formation mechanisms and to improve forecasts. Here the synoptic patterns associated with summer extreme precipitation events over East China during 1961–2018 have been identified systematically and quantitatively using a circulation clustering method. The results show that regional events over East China are dominated by the Eastern Asian summer monsoon associated Meiyu front, landfalling tropical cyclones and low‐pressure vortices. Most sub‐regions have seen increasing trends of extreme rainfall events during the past 6 decades with comparable contributions from the two main drivers. There was a decreasing trend over the North China Plain driven by the low‐level southeasterly winds.

Abstract Sea‐ice loss and radiative feedbacks have been proposed to explain Arctic amplification (AA)—the enhanced Arctic warming under increased greenhouse gases, but their relationship is unclear. By analyzing coupled CESM1 simulations with 1%/year CO 2 increases, we show that without large sea‐ice loss and AA, the lapse rate, Planck, and surface albedo feedbacks are greatly reduced, while the positive water vapor feedback changes little. The positive Arctic lapse rate feedback, which results from enhanced surface warming rather than the high stability of Arctic air, and changes in atmospheric energy transport across the Arctic Circle are a result, not a cause, of AA; while the water vapor feedback also plays a minor role. Instead, AA results from enhanced winter oceanic heating associated with sea‐ice loss that is aided by a positive surface albedo feedback in summer and positive cloud feedback in winter.

Abstract California has experienced increasingly severe autumn wildfires over the past several decades, which have exacted a rising human and environmental toll. Recent fire and climate science research has demonstrated a clear link between worsening California wildfires and climate change, mainly though the vegetation‐drying effect of rising temperatures and shifting precipitation seasonality. New work by Luković et al. (2021) explores observed changes in California's autumn precipitation in greater detail, finding that the rainy season has indeed become progressively delayed and that the “sharpness” of California precipitation seasonality has increased. These precipitation shifts have important implications for the region's ecology and wildfire risk, as they increase the degree of temporal overlap between extremely dry vegetation conditions and fire‐promoting downslope winds in late autumn. Both of these observed shifts are consistent with climate model projections for the region's future, suggesting that recent trends may offer an early preview of larger changes to come.

Abstract Remote, harsh conditions of the Southern Ocean challenge our ability to observe the region’s influence on the climate system. Southern Ocean air‐sea CO 2 flux estimates have significant uncertainty due to the reliance on limited ship‐dependent observations in combination with satellite‐based and interpolated data products. We utilize a new approach, making direct measurements of air‐sea CO 2 , wind speed, and surface ocean properties on an Uncrewed Surface Vehicle (USV). In 2019, the USV completed the first autonomous circumnavigation of Antarctica providing hourly CO 2 flux estimates. Using this unique data set to constrain potential error in different measurements and propagate those through the CO 2 flux calculation, we find that different wind speed products and sampling frequencies have the largest impact on CO 2 flux estimates with biases that range from −4% to +20%. These biases and poorly constrained interannual variability could account for discrepancies between different approaches to estimating Southern Ocean CO 2 uptake.

Abstract Traditional univariate analysis on weather and climate extremes failed to consider temporally compounding events and the resulting cascading impacts. A case in point is a sequence of flood and heatwave within a week, which slows recovery and amplifies damages. We show that across China, floods and heatwaves seldom occurred serially within seven days in the past, but after 2000 the probability is five‐to‐ten times higher in southern, northwestern and northeastern sectors. It is the significant increase in heatwaves that alters the clustering of independent extremes, and facilitates the emergence of sequential extremes. Typhoon‐participating sequential extremes have also increased significantly in frequency in both inland and coastal areas, with the fastest rate at around 200% decade−1 registered within the 30–35°N latitudinal band. The observed increases in sequential flood‐heatwave events are discernibly stronger and more widespread than what would be expected from pure random variability, implying a detectable role of anthropogenic forcings.

Abstract The timing of the India‐Asia collision onset, essential to understanding the evolution of the Himalayan‐Tibetan orogen, has been widely investigated through multidisciplinary approaches. Among these, the India to Asia provenance reversal (IAPR) documented in the Indian passive margin successions has proved to be most effective. We present integrated stratigraphic, sedimentological, and provenance data on Upper Cretaceous‐Paleogene strata from the newly investigated Mubala section exposed south of the Yarlung‐Zangbo suture zone (YZSZ) in southern Tibet, which preserves continuous deep‐marine turbiditic and biogenic sedimentation on the distal Indian passive margin. Sandstone petrography, heavy minerals, detrital zircon geochronology and Hf isotopes, and detrital Cr‐spinel geochemistry constrain the IAPR to later than 62.7 Ma (youngest zircon ages from the earliest Asian‐derived sandstone) and by 61.0 ± 0.3 Ma (SIMS age of a tuffaceous layer ∼30 m above this bed). The onset of intercontinental collision along the YZSZ began by 61 Ma.

Abstract South Asia (SA) is one of those hotspots where earliest exposure to deadly wet‐bulb temperatures ( T w >35°C) is projected in warmer future climates. Here we find that even today parts of SA experience the upper limits of labor productivity ( T w >32°C) or human survivability ( T w >35°C), indicating that previous estimates for future exposure to T w ‐based extremes may be conservative. Our results show that at 2°C global warming above pre‐industrial levels, the per person exposure approximately increases by 2.2 (2.7) folds for unsafe labor (lethal) threshold compared to the 2006–2015 reference period. Limiting warming to 1.5°C would avoid about half that impact. The population growth under the middle‐of‐the‐road socioeconomic pathway could further increase these exposures by a factor of ∼2 by the mid‐century. These results indicate an imminent need for adaptation measures, while highlighting the importance of stringent Paris‐compatible mitigation actions for limiting future emergence of such conditions in SA.

Abstract The consecutive heat wave and heavy rainfall (CHWHR) events, defined as the occurrence of heat waves followed by heavy rainfall, can cause more damages than individual extremes. Nevertheless, this type of compound event has not been diagnosed systematically. Here we examine the occurrence of CHWHR events and underlying characteristics. We find 22% of land areas experienced statistically significant CHWHR events within 7 days in China during 1981–2005, with an average 26% of heat waves being followed by heavy rainfall (vs. 10% expected by chance). More importantly, the shorter and hotter heat waves are more likely to be followed by heavy rainfall than other heat waves. This phenomenon is associated with atmospheric convection and moisture convergence. In addition, climate projection shows the CHWHR events will occur more frequently and abruptly in China throughout the 21st century, which contribute to the increased compound risk of back‐to‐back heat waves and flash floods.

Bathymetry retrievals from 2D, multispectral imagery, referred to as Satellite-Derived Bathymetry (SDB), afford the potential to obtain global, nearshore bathymetric data in optically clear waters. However, accurate SDB depth retrievals are limited in the absence of "seed depths." The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) space-based altimeter has proven capable of accurate bathymetry, but methods of employing ICESat-2 bathymetry for SDB retrievals over broad spatial extents are immature. This research aims to establish and test a baseline methodology for generating bathymetric surface models using SDB with ICESat-2. The workflow is operationally efficient (17-37 min processing time) and capable of producing bathymetry of sufficient vertical accuracy for many coastal science applications, with RMSEs of 0.96 and 1.54 m when using Sentinel-2 and Landsat 8, respectively. The highest priorities for further automation have also been identified, supporting the long-range goal of global coral reef habitat change analysis using ICESat-2-aided SDB.

Abstract Over half of global rivers and streams lack perennial flow, and understanding the distribution and drivers of their flow regimes is critical for understanding their hydrologic, biogeochemical, and ecological functions. We analyzed nonperennial flow regimes using 540 U.S. Geological Survey watersheds across the contiguous United States from 1979 to 2018. Multivariate analyses revealed regional differences in no‐flow fraction, date of first no flow, and duration of the dry‐down period, with further divergence between natural and human‐altered watersheds. Aridity was a primary driver of no‐flow metrics at the continental scale, while unique combinations of climatic, physiographic and anthropogenic drivers emerged at regional scales. Dry‐down duration showed stronger associations with nonclimate drivers compared to no‐flow fraction and timing. Although the sparse distribution of nonperennial gages limits our understanding of such streams, the watersheds examined here suggest the important role of aridity and land cover change in modulating future stream drying.

Abstract Over 1993–2016, studies have shown that the observed global mean sea level (GMSL) budget is closed within the current data uncertainties. However, non‐closure of the budget was recently reported when using Jason‐3, Argo and GRACE/GRACE Follow‐On data after 2016. This non‐closure may result from errors in the data sets used to estimate the GMSL and its components. Here, we investigate possible sources of errors affecting Jason‐3 and Argo data. Comparisons of Jason‐3 GMSL trends with other altimetry missions show good agreement within 0.4 mm/yr over 2016–present. Besides, the wet tropospheric correction uncertainty from the Jason‐3 radiometer contributes to up to 0.2 mm/yr. Therefore, altimetry alone cannot explain the misfit in the GMSL budget observed after 2016. Argo‐based salinity products display strong discrepancies since 2016, attributed to instrumental problems and data editing issues. Reassessment of the sea level budget with the thermosteric component provides about 40% improvement in the budget closure.

Abstract For the explanation of the observed decadal variations in surface solar radiation (known as dimming and brightening), the relative importance of clouds and the cloud‐free atmosphere (particularly aerosols) is currently disputed. Here, we investigate this issue using daily data from the prominent long‐term observational radiation record at Potsdam, Germany, over the 71‐year period 1947–2017. We identify cloud‐free days based on synop cloud observations as well as on days with maximum atmospheric transmission. Irrespective of the cloud‐screening method, strong dimming and brightening tendencies in the atmospheric transmission are evident not only under all‐sky but also of similar magnitude under clear‐sky conditions, causing multidecadal variations in surface solar radiation on the order of 10 Wm−2. This points to the cloud‐free atmosphere as a main responsible for dimming and brightening in central Europe and suggests that these variations are anthropogenically forced rather than of natural origin, with aerosol pollutants as likely major drivers.

Abstract Observational data indicate increasing trends of surface ozone (O3) in China, despite emission controls that have resulted in reductions of precursor emissions. Here, we explore the cause of this contradiction, through analysis of surface observations (2014–2019) in China and historical observation record in the United States (US, 1990–2019). Our observation‐based analysis indicates that the reductions of nitrogen oxides (NOx) emissions led to increase of surface O3 in North China Plain (NCP) and Yangtze River Delta (YRD) of around 8 ppb. However, NOx controls resulted in shift of O3 chemical regimes over NCP and YRD, with turning points between NOx‐ and volatile organic compound (VOC)‐limited regimes around 2019, while model simulations suggest transitional or NOx‐limited regimes over the rest of China. The impacts of high fine particles (PM2.5) on O3 formation has declined because of the reduction of PM2.5 concentrations. Stricter NOx controls can mitigate O3 pollutions over industrialized areas in China.

Abstract Hourly precipitation extremes can intensify with temperature at higher rates than expected from thermodynamic increases explained by the Clausius‐Clapeyron (CC) relationship (∼6.5%/K), but local scaling with surface air temperature is highly variable. Here we use daily dew point temperature, a direct proxy of absolute humidity, to estimate at‐gauge local scaling across six macro‐regions for a global data set of over 7,000 hourly precipitation gauges. We find scaling rates from CC to 2 × CC at more than 60% of gauges, peaking in the tropics at a median rate of ∼1.5CC. Moreover, regional scaling rates show surprisingly universal behavior at around CC, with higher scaling in Europe. Importantly for impacts, hourly scaling is persistently higher than scaling for daily extreme precipitation. Our results indicate greater consistency in global scaling than previous work, usually at or above CC, with positive scaling in the (sub)tropics. This demonstrates the relevance of DPT scaling to understanding future changes.

Abstract Satellite nitrogen dioxide (NO2) measurements are used extensively to infer nitrogen oxide emissions and their trends, but interpretation can be complicated by background contributions to the NO2 column sensed from space. We use the step decrease of US anthropogenic emissions from the COVID‐19 shutdown to compare the responses of NO2 concentrations observed at surface network sites and from satellites (Ozone Monitoring Instrument [OMI], Tropospheric Ozone Monitoring Instrument [TROPOMI]). After correcting for differences in meteorology, surface NO2 measurements for 2020 show decreases of 20% in March–April and 10% in May–August compared to 2019. The satellites show much weaker responses in March–June and no decrease in July–August, consistent with a large background contribution to the NO2 column. Inspection of the long‐term OMI trend over remote US regions shows a rising summertime NO2 background from 2010 to 2019 potentially attributable to wildfires.

Abstract Measurements of dust aerosol size usually obtain the optical or projected area‐equivalent diameters, whereas model calculations of dust impacts use the geometric or aerodynamic diameters. Accurate conversions between the four diameter types are thus critical. However, most current conversions assume dust is spherical, even though numerous studies show that dust is highly aspherical. Here, we obtain conversions between different diameter types that account for dust asphericity. Our conversions indicate that optical particle counters have underestimated dust geometric diameter ( D geo ) at coarse sizes. We further use the diameter conversions to obtain a consistent observational constraint on the size distribution of emitted dust. This observational constraint is coarser than parameterizations used in global aerosol models, which underestimate the mass of emitted dust within 10 ≤ D geo ≤ 20 μm by a factor of ∼2 and usually do not account for the substantial dust emissions with D geo ≥ 20 μm. Our findings suggest that models substantially underestimate coarse dust emission.

Abstract The National Oceanic and Atmospheric Administration (NOAA) maintains an operational analysis for monitoring trends in global surface temperature. Because of limited polar coverage, the analysis does not fully capture the rapid warming in the Arctic over recent decades. Given the impact of coverage biases on trend assessments, we introduce a new analysis that is spatially complete for 1850–2018. The new analysis uses air temperature data in the Arctic Ocean and applies climate reanalysis fields in spatial interpolation. Both the operational analysis and the new analysis show statistically significant warming across the globe and the Arctic for all periods examined. The analyses have comparable global trends, but the new analysis exhibits significantly more warming in the Arctic since 1980 (0.598°C dec−1 vs. 0.478°C dec−1), and its trend falls outside the 95% confidence interval of its operational counterpart. Trend differences primarily result from coverage gaps in the operational analysis.

Abstract World Wide Lightning Location Network (WWLLN) data on global lightning are used to investigate the increase of total lightning strokes at Arctic latitudes. We use the summertime data from June, July, and August (JJA) which average >200,000 strokes each year above 65°N for the years 2010–2020. We minimize the possible influence of WWLLN network detection efficiency increases by normalizing our results to the total global strokes during northern summer each year. The ratio of strokes occurring above a given latitude, compared to total global strokes, increases with time, indicating that the Arctic is becoming more influenced by lightning. We compare the increasing fraction of strokes with the NOAA global temperature anomaly, and find that the fraction of strokes above 65°N to total global strokes increases linearly with the temperature anomaly and grew by a factor of 3 as the anomaly increased from 0.65°C to 0.95°C.

Abstract The Third Pole experiences accelerated glacier retreating particularly in the eastern‐Himalaya, coinciding with a decrease of monsoon‐precipitation in the early 21st century. The extent to which the vanishing abundant maritime glaciers buffer the declining precipitation‐runoff remains unclear. Here, with a state‐of‐the‐art enthalpy‐based distributed cryosphere‐hydrology model and first‐hand hydrometeorology observations at Motuo (latest accessible Chinese county), we carefully examine the Yarlung Zangbo basin along Himalayas. We find that during 1998–2019, the rising downstream runoff (lower Nuxia; +6.40 × 10 8 m 3 /yr) offsets the dropping upstream runoff (upper Nuxia; −6.89 × 10 8 m 3 /yr); however, only the marginal contribution from the vanishing eastern‐Himalaya and Nyainqêntanglha glaciers. During 1998–2019, dry upstream illustrates limited glacier melt (15.7 mm/yr) with dominated snow melt (78.8 mm/yr); while much larger at humid downstream (144.8 mm/yr for glacier melt and 219.1 mm/yr for snow melt). From 1981 to 2019, we observe glacier‐to‐snow melt transition in…

Abstract Targeted observations for Typhoon Maria (2018) using the Geostationary Interferometric Infrared Sounder (GIIRS) at a temporal resolution of 15 min provide unprecedented information for impact studies of high‐temporal‐resolution from geostationary hyperspectral IR sounders. This study investigates the impacts of different temporal resolutions for GIIRS assimilation on Maria analyses and forecasts. Results reveal that assimilating higher resolution data captures more detailed temporal and spatial structures and stronger warm anomalies. Additionally, the track forecast for Maria from assimilating higher temporal resolution GIIRS radiances is better than those from assimilating radiances with lower temporal resolution, and both are better than the control experiment. The accuracy of the different temporal resolutions GIIRS experiments (from 3 h to 15 min) is improved (from 18% to 43%), and the intensity forecast errors are also reduced (from 12% to 18%). Meanwhile, high‐temporal GIIRS assimilation also improves the coastal precipitation forecasts during typhoon landfall.