Diagnosing land-atmosphere fluxes of carbon-dioxide (CO$_2$) and methane (CH$_4$), is essential for evaluating carbon-climate feedbacks. Greenhouse gas satellite missions aim to fill data gaps in regions like the humid tropics, but obtain very few valid measurements due to cloud contamination. We examined data yields from the Orbiting Carbon Observatory alongside Sentinel 2 cloud statistics. We find that the main contribution to low data yields are frequent shallow cumulus clouds. In the Amazon, the success rate in obtaining valid measurements vary from 0.1\% to 1.0\%. By far the lowest yields occur in the wet season, consistent with Sentinel 2 cloud patterns. We find that increasing the spatial resolution of observations to $\sim$200\,m would increase yields by 2-3 orders of magnitude, and allow regular measurements in the wet season. Thus, the key effective tropical greenhouse gas observations lies in regularly acquiring high-spatial resolution data, rather than more frequent low-resolution measurements.

Studies have implicated the importance of longwave (LW) cloud-radiative forcing (CRF) in facilitating or accelerating the upscale development of tropical moist convection. While different cloud types are known to have distinct CRF, their individual roles in driving upscale development through radiative feedback is largely unexplored. We hypothesize that CRF from stratiform regions will have the greatest effect on upscale tropical convection. We test this hypothesis by analyzing output from convection-permitting ensemble Weather Research and Forecasting (WRF) model simulations of tropical cyclone formation. Using a novel column-by-column cloud classification scheme introduced herein, we use this model output to identify the relative contribution of five cloud types (shallow, congestus, and deep convection; and stratiform and anvil clouds) to the direct LW radiative forcing and the upscale development of convection via LW moist static energy variance. Results indicate that stratiform and anvil regions contribute dominantly to the domain averages of these variables.

In this study, we employ the Conformal Cubic Atmospheric Model (CCAM), a variable-resolution global atmospheric model, driven by two distinct sea surface temperature (SST) datasets: the 0.25° Optimum Interpolation Sea Surface Temperature (CCAM_OISST) version 2.1 and the 2° Extended Reconstruction SSTs Version 5 (CCAM_ERSST5). Model performance is assessed using a benchmarking framework, revealing good agreement between both simulations and the climatological rainfall spatial pattern, seasonality, and annual trends obtained from the Australian Gridded Climate Data (AGCD). Notably, wet biases are identified in both simulations, with CCAM_OISST displaying a more pronounced bias. Furthermore, we have examined CCAM’s ability to capture El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) correlations with rainfall during Austral spring (SON) utilizing a novel hit rate metric. Results indicate that only CCAM_OISST successfully replicates observed SON ENSO- and IOD-rainfall correlations, achieving hit rates of 86.6% and 87.5%, respectively, compared to 52.7% and 41.8% for CCAM_ERSST5. Large SST differences are found surrounding the Australian coastline between OISST and ERSST5 (termed the “Coastal Effect”). Differences can be induced by the spatial interpolation error due to the discrepancy between model and driving SST. An additional CCAM experiment, employing OISST with SST masked by ERSST5 in 5° proximity to the Australian continent, underscores the “Coastal Effect” has a significant impact on IOD-Australian rainfall simulations. In contrast, its influence on ENSO-Australian rainfall is limited. Therefore, simulations of IOD-Australian rainfall teleconnection are sensitive to local SST representation along coastlines, probably dependent on the spatial resolution of driving SST.

Stratospheric water vapor (SWV) is a greenhouse gas that has a significant, yet uncertain, impact on the Earth’s climate through its radiative effect and feedback. As the climate changes, it is thus critical to monitor and understand changes in SWV. NASA’s Microwave Limb Sounding (MLS) aboard the Aura satellite has observed SWV since 2004 but will soon reach end of life. The Stratospheric Aerosol and Gas Experiment (SAGE) missions observe SWV as well, with the SAGE III instrument operating on the International Space Station (ISS) since 2017. We use the constituent data assimilation capabilities of NASA’s Goddard Earth Observing System (GEOS) to demonstrate that the up to 30 SAGE III/ISS profiles each day provide a useful constraint on SWV over the observed midlatitudes and tropics. We conclude that assimilating SAGE III/ISS SWV into GEOS can continue the SWV climate data record of Aura MLS.

Properly predicting the rapid transition from shallow to precipitating atmospheric convection within a diurnal cycle over land is of great importance for both weather prediction and climate projections. In this work, we consider that a cumulus cloud is formed due to the transport of water mass by multiple updrafts during its life-time. Cumulus clouds then locally create favorable conditions for the subsequent convective updrafts to reach higher altitudes, leading to deeper precipitating convection. This mechanism is amplified by the cold pools formed by the evaporation of precipitation in the sub-cloud layer. Based on this conceptual view of cloud-cloud interactions which goes beyond the one cloud equals one-plume picture, it is argued that precipitating clouds may act as predators that prey on the total cloud population, such that the rapid shallow-to-deep transition can be modeled as a simple predator-prey system. This conceptual model is validated by comparing solutions of the Lotka-Volterra system of equations to results obtained using a high-resolution large-eddy simulation model. Moreover, we argue that the complete diurnal cycle of deep convection can be seen as a predator-prey system with varying food supply for the prey. Finally, we suggest that the present model can be applied to weather and climate models, which may lead to improved representations of the transition from shallow to precipitating continental convection.

In the austral spring seasons of 2020-2022, the Antarctic stratosphere experienced three consecutive strong vortex events. In particular, the Antarctic vortex of October-December 2020 was the strongest on record in the satellite era for that season at 60°S in the mid- to lower stratosphere. However, it was poorly predicted by the Australian Bureau of Meteorology’s operational seasonal climate forecast system of that time, ACCESS-S1, even at a short lead time of a month. Using the current operational forecast system, ACCESS-S2, we have, therefore, tried to find a primary cause of the limited predictability of this event and conducted forecast sensitivity experiments to climatological versus observation-based ozone to understand the potential role of the ozone forcing in the strong vortex event and associated anomalies of the Southern Annular Mode (SAM) and south-eastern Australian rainfall. Here, we show that the 2020 strong vortex event did not follow the canonical dynamical evolution seen in previous strong vortex events in spring, whereas the ACCESS-S2 control forecasts with the climatological ozone did, which likely accounts for the inaccurate forecasts of ACCESS-S1/S2 at 1-month lead time. Forcing ACCESS-S2 with observed ozone significantly improved the skill in predicting the strong vortex in October-December 2020 and the subsequent positive SAM and related rainfall increase over south-eastern Australia in the summer of December 2020 to February 2021. These results highlight an important role of ozone variations in seasonal climate forecasting as a source of long-lead predictability, and therefore, a need for improved ozone forcing in future ACCESS-S development.

Following the Hunga Tonga–Hunga Ha’apai (HTHH) eruption in January 2022, a significant reduction in stratospheric hydrochloric acid (HCl) was observed in the Southern Hemisphere mid-latitudes during the latter half of 2022, suggesting potential chlorine activation. The objective of this study is to comprehensively understand the substantial loss of HCl in the aftermath of HTHH. Satellite measurements along with a global chemistry-climate model are employed for the analysis. We find strong agreement of 2022 anomalies between the modeled and the measured data. The observed tracer-tracer relations between N2O and HCl indicate a significant role of chemical processing in the observed HCl reduction, especially during the austral winter of 2022. Further examining the roles of chlorine gas-phase and heterogeneous chemistry, we find that heterogeneous chemistry emerges as the primary driver for the chemical loss of HCl, with the reaction between HOBr and HCl on sulfate aerosols identified as the dominant loss process.

We present a heuristic model to quantitatively explain the suppression of deep convection in convection-resolving models (CRMs) with small domains. We distinguish between “computational” smallness (few grid columns) and “physical” smallness (representing a small geographic area). Domains that are computationally small require greater instability to sustain convection because they force a large convective fraction, driving strong compensating subsidence warming. Consequently, detrainment occurs lower for undiluted convection. Both computationally and physically small domains limit the physical updraft width, increasing entrainment dilution. This enhancement of entrainment strengthens the sensitivity to domain size beyond that for undiluted deep convection. Coarsening grid spacing to expand the physical domain and physical updraft width can reduce domain size sensitivity. Simulations using the System for Atmospheric Modeling (SAM) confirm the heuristic model results. We also present simulation results for two shallow convection cases, which are less sensitive to domain size, but also exhibit sensitivities.

As more countries make net zero greenhouse gas emissions pledges, it is crucial to understand the effects on global climate after achieving net zero emissions. The climate has been found to continue to evolve even after the abrupt cessation of CO2 emissions, with some models simulating a small warming and others simulating a small cooling. In this study, we analyse if the temperature and precipitation changes post abrupt cessation of CO2 emissions are significant compared to natural climate variations. We find that the temperature changes are outside of natural variability for most models, whilst the precipitation changes are mostly non-significant. We also demonstrate that post-net zero temperature changes have implications for the remaining carbon budget. The possibility of further global warming post-net zero adds to the evidence supporting more rapid emissions reductions in the near-term.

This work presents 30-year observations of dust chemical composition by the IMPROVE network in the United States. Analysis of 1,253 large dust storms detected at the IMPROVE sites shows that dust PM2.5 (particles less than 2.5 micrometers in fresh dust plumes) crustal materials (64%), organic matter (13%), sulfate (7%), nitrate (2%), Cl, Br, and heavy metals. Dust composition stays relatively stable during near source transport. There are distinct spatial variations in dust composition, including high carbon and sulfate in Oklahoma, high Cl in Washington, and high fractions of heavy metals in Arizona. Compared to the Earth’s crust, dust PM2.5 contains less crustal elements but more OC, EC, sulfate, nitrate, and halogen elements due to influence by human activities and biogeochemical processes. This rich pool of dust composition data provides useful information to study the roles played by dust in the Earth system and its effects on human society.

A document by Daniele Visioni. Click on the document to view its contents.

Soil moisture (SM) analyses and assessments hold significance for numerous applications in the fields of hydrometeorology and agriculture. Throughout history, flux tower sites have been a primary source of data for observationally-based SM examinations and evaluations of landatmosphere interaction. However, these monitoring stations are not evenly distributed worldwide. One of the ways in which the comprehensive understanding of how land and atmosphere interact can be improved is by incorporating remotely sensed SM observations. The Soil Moisture Active Passive (SMAP) satellite is one of the satellite resources which closely aligns with in-site observations. However, the remote sensing nature of SMAP data means that it is prone to unpredictable random distortions. Since variations in SM tend to follow a fundamental Markov process, they typically display a specific "red noise" pattern of variability. On the other hand, satellite data that incorporates random fluctuations exhibits a more uniform "white noise" pattern at higher frequencies, which contrasts with the anticipated red noise pattern. Furthermore, gaps in SMAP data are not randomly distributed; due to its orbital characteristics, the satellite experiences regular instances of missing data during its 8-day orbital cycle, differing depending on the orbital pass. This introduces additional anomalies in the power spectrum, performed through examining correlations in the time series data, leading to recurring spikes at intervals of 8, 4 (half of 8), 2 and 2/3 (one-third of 8), and 2 days (one-fourth of 8). These spectral spikes become broader due to small variations in the satellite's orbit. To make the satellite data most effective for assessing land-atmosphere interactions, which tend to rely on estimates of covariability of SM with other environmental variables, it is crucial to minimize the impact of random distortions and systematic missing data. A technique for adjusting the power spectrum, and thus the time series, of SM has been developed to minimize the influence of orbital harmonic spikes in the gridded Level 3 (L3) SMAP dataset. This is achieved by fitting a catenary function to the power spectrum between the harmonic spikes and then removing their influence. The adjusted spectrum is then aligned with soil moisture data from the surface layer, collected from sites within the AmeriFlux network (in-situ flux tower data). These sites demonstrate relatively minimal distortion and exhibit SM power spectra that closely resemble those generated by offline land surface models (LSMs), which are free of random noise by nature. Using validated spectral data from gridded LSM-based datasets, an improved global L3 SMAP dataset is being generated that accounts for noise and harmonic effects. This presentation will showcase the outcomes of this technique in enhancing SMAP data and its temporal correspondence with observational data.

• Climate change and impacts will continue to accelerate until the warming influences are reduced or offset by direct cooling approaches. • Direct climate cooling approaches have the potential to reduce local to global portions of human-induced warming influences. • GHG emission reduction and removal policies alone will take at least decades to halt warming, much less restore 20 th century conditions.

• Deep depression occurrences have significantly increased over the eastern side, and decreased over the western side of the North Atlantic. • Deep depressions are linked to high surface temperature patterns in western continental Europe but have little impact on the mean warming.

The atmospheric CO2 growth rate is a fundamental measure of climate forcing. NOAA's growth rate estimates, derived from in situ observations at the marine boundary layer (MBL), serve as the benchmark in policy and science. However, NOAA's MBL-based method encounters challenges in accurately estimating the whole-atmosphere CO2 growth rate at sub-annual scales. We introduce the Growth Rate from Satellite Observations (GRESO) method as a complementary approach to estimate the whole-atmosphere CO2 growth rate utilizing satellite data. Satellite CO2 observations offer extensive atmospheric coverage that extends the capability of the current NOAA benchmark. We assess the sampling errors of the GRESO and NOAA methods using ten atmospheric transport model simulations. The simulations generate synthetic OCO-2 satellite and NOAA MBL data for calculating CO2 growth rates, which are compared against the global sum of carbon fluxes used as model inputs. We find good performance for the NOAA method (R = 0.93, RMSE = 0.12 ppm/year or 0.25 PgC/year). GRESO demonstrates lower sampling errors (R = 1.00; RMSE = 0.04 ppm/year or 0.09 PgC/year). Additionally, GRESO shows better performance at monthly scales than NOAA (R = 0.77 vs 0.47, respectively). Due to CO2's atmospheric longevity, the NOAA method accurately captures growth rates over five-year intervals. GRESO's robustness across partial coverage configurations (ocean or land data) shows that satellites can be promising tools for low-latency CO2 growth rate information, provided the systematic biases are minimized using in situ observations. Along with accurate and calibrated NOAA in situ data, satellite-derived growth rates can provide information about the global carbon cycle at sub-annual scales.

While yearly budgets of CO2 and evapotranspiration (ET) above forests can be readily obtained from eddy-covariance measurements, the quantification of their respective soil (respiration and evaporation) and canopy (photosynthesis and transpiration) components remains an elusive yet critical research objective. To this end, methods capable of reliably partitioning the measured ET and F_c fluxes into their respective soil and plant sources and sinks are highly valuable. In this work, we investigate four partitioning methods (two new, and two existing) that are based on analysis of conventional high frequency eddy-covariance (EC) data. The physical validity of the assumptions of all four methods, as well as their performance under different scenarios, are tested with the aid of large eddy simulations, which are used to replicate eddy-covariance field experiments. Our results indicate that canopies with large, exposed soil patches increase the mixing and correlation of scalars; this negatively impacts the performance of the partitioning methods, all of which require some degree of uncorrelatedness between CO2 and water vapor. In addition, best performance for all partitioning methods were found when all four flux components are non-negligible, and measurements are collected close to the canopy top. Methods relying on the water-use efficiency (W) perform better when W is known a priori, but are shown to be very sensitive to uncertainties in this input variable especially when canopy fluxes dominate. We conclude by showing how the correlation coefficient between CO2 and water vapor can be used to infer the reliability of different W parameterizations.

1 NASA Goddard Institute for Space Studies, New York, NY USA2 Dept. of Applied Physics and Applied Mathematics, Columbia Univ., New York, NY USA3 Dept. of Earth and Atmospheric Sciences, City College of New York, NY USA4 University of California, Los Angeles, Los Angeles, CA5 NASA Jet Propulsion Laboratory, Pasadena, CA USA* Corresponding author: Allegra N. LeGrande ([email protected] | [email protected] )

In recent years, efforts to assess the evolving risks of coastal compound surge and rainfall-driven flooding from tropical cyclones (TCs) and extratropical cyclones (ETCs) in a warming climate have intensified. While substantial progress has been made, the persistent challenge lies in obtaining actionable insights into the changing magnitude and spatially-varying flood risks in coastal areas. We employ a physics-based numerical hydrodynamic framework to simulate compound flooding from TCs and ETCs in both current and future warming climate conditions, focusing on the western side of Buzzard Bay in Massachusetts. Our approach leverages hydrodynamic models driven by extensive sets of synthetic TCs downscaled from CMIP6 climate models and dynamically downscaled ETC events using the WRF model forced by CMIP5 simulations. Through this methodology, we quantify the extent to which climate change can potentially reshape the risk landscape of compound flooding in the study area. Our findings reveal a significant increase in TC-induced compound flooding risk due to evolving climatology and sea level rise (SLR). Additionally, there is a heightened magnitude of compound flooding from ETCs, in coastal regions, due to SLR. Inland areas exhibit a decline in rainfall-driven flooding from high-frequency ETC events toward the end of the century compared to the current climate. Our methodology is transferable to other vulnerable coastal regions, serving as a valuable decision-making tool for adaptive measures in densely populated areas. It equips decision-makers and stakeholders with the means to effectively mitigate the destructive impacts of compound flooding arising from both current and future TCs and ETCs.

Mars once had a dense atmosphere enabling liquid water existing on its surface, however, much of that atmosphere has since escaped to space. We examine how incoming solar and solar wind energy fluxes drive escape of atomic and molecular oxygen ions (O+ and O2+) at Mars. We use MAVEN data to evaluate ion escape from February 1, 2016 through May 25, 2022. We find that Martian O+ and O2+ both have increased escape flux with increased solar wind kinetic energy flux and this relationship is generally logarithmic. Increased solar wind electromagnetic energy flux also corresponds to increased O+ and O2+ escape flux, however, increased solar wind electromagnetic energy flux seems to first dampen ion escape until a threshold level is reached, at which point ion escape increases with increasing electromagnetic energy flux. Increased solar irradiance (both total and ionizing) does not obviously increase escape of O+ and O2+. Our results suggest that the solar wind electromagnetic energy flux should be considered along with the kinetic energy flux as an important driver of ion escape, and that other parameters should be considered when evaluating solar irradiance’s impact on O+ and O2+ escape.

We study the sensitivity of South Asian Summer Monsoon (SASM) precipitation to Southern Hemisphere (SH) subtropical Absorbed Solar Radiation (ASR) changes using Community Earth System Model 2 simulations. Reducing positive ASR biases over the SH subtropics impacts SASM, and is sensitive to the ocean basin where changes are imposed. Radiation changes over the SH subtropical Indian Ocean (IO) shifts rainfall over the equatorial IO northward causing 1-2 mm/day drying south of equator, changes over the SH subtropical Pacific increases precipitation over northern continental regions by 1-2 mm/day, and changes over the SH subtropical Atlantic have little effect on SASM precipitation. Radiation changes over the subtropical Pacific impacts the SASM through zonal circulation changes, while changes over the IO modify meridional circulation to bring about changes in precipitation over northern IO. Our findings suggest that reducing SH subtropical radiation biases in climate models may also reduce SASM precipitation biases.

Volcanic aerosol plumes over south east Asia (SEAsia), and only over SEAsia, have always been the trigger and sustaining cause of: El Niño Southern Oscillation (ENSO) events which are the dominant mode of variability in the global climate; Australian and Indonesian droughts; increased global temperatures; and Indian Ocean Dipole (IOD) events. In recent decades this natural plume has been augmented by an anthropogenic plume which has intensified these events especially from September to November. Understanding the mechanism which enables aerosols over SEAsia, and only over SEAsia, to create ENSO events is crucial to understanding the global climate. I show that the SEAsian aerosol plume causes ENSO events by: reflecting/absorbing solar radiation which warms the upper troposphere; and reducing surface radiation which cools the surface under the plume. This inversion reduces convection in SEAsia thereby suppressing the Walker Circulation and the Trade Winds which causes the Sea Surface Temperature (SST) to rise in the central Pacific Ocean and creates convection there. This further weakens/reverses the Walker Circulation driving the climate into an ENSO state which is maintained until the SEAsian aerosols dissipate and the climate system relaxes into a non-ENSO state. Data from the Global Volcanism Program (151 years), the Last Millennium Ensemble (1,156 years), MERRA-2 (41 years) and NASA MODIS on Terra (21 years) demonstrates this connection with the Nino 3.4 and 1+2 SST, the Southern Oscillation Index, and three events commonly associated with ENSO: drought in south eastern Australia; the IOD and a warmer World.

The eruption of Hunga Tonga in January 2022 injected a large amount of water into the stratosphere. Satellite measurements from Aura Microwave Limb Sounder (MLS) show that this water vapor (H2O) has now spread throughout the stratosphere and into the lower mesosphere, resulting in an increase of >1 ppmv throughout most of this region. Measurements from three ground-based Water Vapor Millimeter Wave Spectrometer (WVMS) instruments and MLS are in good agreement, and show that in 2023 there was more H2O in the lower mesosphere than at any time since the WVMS measurements began in the 1990’s. At Table Mountain, California all WVMS H2O measurements at 54 km since June 2023, and all of the measurements from Mauna Loa, Hawaii, since the resumption of measurements in September 2023, show larger mixing ratios than any previous measurements. At 70 km several recent ~1 week WVMS retrievals in the last few months show the largest anomalies ever measured. The MLS measurements show that maximum H2O anomalies have occurred throughout almost all of the stratosphere and lower mesosphere since the eruption. As of November 2023, almost all of the ~140 Tg of water originally injected into the stratosphere by the Hunga Tonga eruption remains in the middle atmosphere at pressures below 83 hPa (altitudes above ~17 km). The eruption occurred during a period when stratospheric H2O was already slightly elevated above the 2004-2021 MLS average, and the November 2023 anomaly of ~160 Tg represents ~15% of the total mass of H2O in this region.

Deep convection is the primary influence on weather and climate in tropical regions. However, understanding and simulating the shallow-to-deep (STD) convective transition has long been challenging. Here, we conduct high-resolution numerical simulations to assess the environmental controls on the evolution of isolated convection in the Amazon during the wet season. Observations and large-scale forcing derived through the constrained variational analysis approach for the GoAmazon2014/5 experiments are used in the simulations and model validation. The model consistently reproduces the GOAmazon observations for precipitation, moisture, and surface fluxes of radiation, latent and sensible heat. Through sensitivity experiments, we examine the relative importance of moisture and vertical wind shear in controlling the STD convective transition. Reducing the pre-convective humidity within the lower 1.5 km significantly suppresses vertical development and lowers the ice water path. Additionally, the maximum precipitation rate decreases almost quadratically with column water vapor. Conversely, a reduction of column water vapor above 1.5 km by a factor of two or more is necessary to produce a comparable decrease in ice water path or precipitation. Moderate low-level wind shear facilitates the STD transition, leading to an earlier peak of ice water compared to stronger wind shear or its absence. Although upper-level wind shear negatively influences high cloud formation, its role in controlling the STD transition is relatively smaller than that of low-level wind shear. Our results help quantify the role of moisture and wind shear on the STD transition, but also suggest that dynamic factors may exert a more pronounced influence.

Surface and satellite observations of atmospheric methane show smooth seasonal behavior in the Southern Hemisphere driven by loss from the hydroxyl (OH) radical. However, observations in the Northern Hemisphere show a sharp mid-summer increase that is asymmetric with the Southern Hemisphere and not captured by the default configuration of the GEOS-Chem chemical transport model. Using an ensemble of 22 OH model estimates and 24 wetland emission inventories in GEOS-Chem, we show that the magnitude, latitudinal distribution, and seasonality of Northern Hemisphere wetland emissions are critical for reproducing the observed seasonality of methane in that hemisphere, with the interhemispheric OH ratio playing a lesser role. Reproducing the observed seasonality requires a wetland emission inventory with ~80 Tg a-1 poleward of 10°N including significant emissions in South Asia, and an August peak in boreal emissions persisting into autumn. In our 24-member wetland emission ensemble, only the LPJ-wsl MERRA-2 inventory has these attributes.