The Arctic Ocean's Wandel Sea is the easternmost sector of the Last Ice Area, where thick, old sea ice is expected to endure longer than elsewhere. Nevertheless, in August 2020 the area experienced record-low sea ice concentration. Here we use satellite data and sea ice model experiments to determine what caused this record sea ice minimum. In our simulations there was a multi-year sea-ice thinning trend due to climate change. Natural climate variability expressed as wind-forced ice advection and subsequent melt added to this trend. In spring 2020, the Wandel Sea had a mixture of both thin and---unusual for recent years---thick ice, but this thick ice was not sufficiently widespread to prevent the summer sea ice concentration minimum. With continued thinning, more frequent low summer sea ice events are expected. We suggest that the Last Ice Area, an important refuge for ice-dependent species, is less resilient to warming than previously thought.

Statistical studies of the properties of different plasma regions, such as the magnetosheath and outer magnetosphere found near the boundaries of planetary magnetospheres, require knowledge of boundary (bow shock and magnetopause) crossings for purposes of classification. These are commonly detected by visual inspection of the magnetic field and / or particle data sampled by the relevant spacecraft. Automation of this type of activity would thus improve the efficiency of boundary and region studies, which benefit from large crossing datasets, and could also have implications for future development of onboard data-processing protocols in the pre-downlink stage. The Cassini mission at Saturn (2004-2017) provided an invaluable dataset for testing the viability of automated boundary classification. The training dataset consists of BS and MP crossings for the time period 2004 to 2016 (Jackman et al. (2019)). We have employed a series of techniques which involve pre-processing the calibrated magnetometer data, unsupervised training of a LSTM recurrent neural network on magnetometer data to filter magnetosheath regions where crossings are most likely to be found, isolating large rotations in magnetic field using minimum variance analysis (MVA), feature engineering such as magnetic field strength ratio either side of the field rotation to form a ‘feature vector’ for each candidate, and finally applying a gradient-boosting decision-tree-based algorithm to predict the probability that a given interval of data contains the signature of a bow shock (BS), a magnetopause (MP), or None (not a boundary crossing). The resulting model performs better on bow shock events, with a precision (fraction of true events in the retrieved sample) and recall (fraction of the total true events which were retrieved) of ~86% and ~90% respectively, as compared to ~50% and ~68% for the MP. The ongoing work focuses on augmenting the feature space for improved classification of MP, based on a magnetic pressure model of MP crossings derived using a local pressure balance condition (e.g. Pilkington et al. 2015) and using the distinct energetic particle flux changes across the MP in MIMI data (e.g. Liou et al. 2021). We expect that these promising new features will help us to better constrain the retrieval of candidate events which are true MP crossings.

Scalar parameters of various aspects of magnetic reconnection offer easily comparable measures of the characteristics of the diffusion regions surrounding X-lines in a way arguably more general than other attributes which may depend on the coordinate system used. We performed a principal component analysis (PCA) of several scalar parameters including measures of relative magnetic field line (MFL) curvature, ion agyrotropy, energy conversion rate, electric field, and current density in the time spans surrounding and including independently identified ion diffusion regions (IDRs) observed by MMS in the geomagnetic tail. We find that relative MFL curvature is the most bold indicator of an IDR encounter while some more traditional measures vary comparatively little between IDRs and the regions adjacent to them when averaged across events. More granular statistics are also presented and discussed.

We present a methodology that allows real-time simulation of the geoelectric field (GEF) spatiotemporal evolution in a given 3-D conductivity model of the Earth based on continuously augmented inducing source data. The presented concept is validated using Fennoscandia as a test region. The choice of Fennoscandia is motivated by several reasons. First, it is a high latitude region, where the GEF is expected to be particularly large. Second, there exists a 3-D ground electrical conductivity model of the region. Third, the regional magnetometer network, IMAGE, allows us to build a realistic model of the source for a given geomagnetic disturbance. Taking the 7-8 September 2017 geomagnetic storm as a space weather event, we show that real-time high-resolution 3-D modeling of the GEF is feasible and requires only a few tens of seconds.

Several studies show anthropogenic aerosols (AAs) can perturb large-scale atmospheric circulation, as well as precipitation associated with the tropical rain belt and monsoons of the Northern Hemisphere, including the South and East Asian monsoons, as well as the West African monsoon. In the Northern Hemisphere mid-latitudes, however, the impact of AAs on regional climate and precipitation remains uncertain. This work investigates the influence of AAs on wintertime precipitation along the west coast of North America using the newest climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6). Over much of the 20th century, particularly from 1930-1975 when U.S. and European AA emissions rapidly increased, models show a robust wintertime precipitation dipole pattern along the Pacific Coast, with wetting southward of 40°N and drying to the north. This dipole pattern is consistent with a deepening of the Aleutian Low and a southeastward shift of the east Pacific jet stream, which in turn is associated with a southeastward displacement of the region of maximum baroclinicity. These changes in baroclinicity are related to strong aerosol-induced tropospheric cooling that maximizes at 40°N over the east Pacific. This strong cooling is consistent with aerosol-induced decreases in top-of-the-atmosphere radiation and changes in clouds, including decreases in cloud top effective radius and increases in cloud cover. Consistently, in response to 21st-century AA reductions an opposite hydro-dynamic dipole pattern occurs, including drying southward of 40°N and wetting to the north. Although uncertainties remain, including possible overestimation of aerosol forcing, our results suggest aerosols can perturb precipitation along the west coast of North America. However, the dipole pattern under AA forcing is partially offset by GHGs, resulting in a muted dipole response in all-forcing simulations.

Wildfire emissions can vary substantially between different inventories due to the assumptions made in the emission creation process, including the defined vegetation type, fire detection, fuel loading, fraction of vegetation burned and emissions factors. Here, we focus on the uncertainty in emission factors and the resulting impact on modeled composition. We use the Community Atmosphere Model with chemistry (CAM-chem) to simulate 2014 atmospheric composition and focus on carbon monoxide (CO), a trace gas emitted from incomplete combustion and also produced from secondary oxidation of volatile organic compounds (VOCs). Fire is a major source of atmospheric CO and VOCs. Multiple simulations are compared, from an ensemble using four fire emission inventories (CMIP6/GFED4s, QFED2.5, GFAS1.2 and FINN1.5) and a range of sensitivity tests based on CO and VOC emission factor uncertainties. We compare model output and evaluate against CO observations from the Measurements of Pollution in the Troposphere (MOPITT) satellite-based instrument. For some regions, emission factor uncertainty spans the results found by using different inventories. Finally, we use modeled ozone (O3) to investigate how emission factor uncertainty influences the atmospheric oxidative environment. Overall, accounting for emission factor uncertainty lends a range of uncertainty to simulated results.

Drought is probably the most complex among natural hazards to assess its effects. While most drought indicators and risk assessments are developed around agricultural or water shortages effects of drought, its effect on human health is highly understudied because of its unclear and complicated path towards physical and mental health effects. This study assesses the health risk of the latest decadal drought over the US counties by spatially superimposing several proxy variables of counties’ health vulnerabilities over their drought levels. We have used different variations of Local Moran’s I statistics to assess the spatial distribution of drought-vulnerability in two five-year study periods (2010-2014 and 2015-2019) and their differences. Our results show large clusters of significant risk increase in the west due to increases in both vulnerability and hazard indicators in the second study period. Since the used vulnerability variables include indicators of agriculture, drinking water, and socioeconomic prosperities, the results of this study can help researchers and policymakers in these areas to distinguish areas in need of higher attention for interdisciplinary study and planning in national or regional scales.

In this study, we used the potential vorticity (PV) diagnosis technique to investigate the key factors that affect the track forecasts of Hurricane Maria (2017) in the NCEP GFS v14, ECMWF IFS and GFDL SHiELD models. In Chen et al. (2019), it showed that a slow bias of Maria’s translation speed in the IFS 5-day forecasts was significantly improved by GFDL SHiELD with IFS initial conditions (SHiELD_IFS). Our results found that the slow moving bias in the IFS is mainly due to a strong, westerly steering flow contribution from a cutoff low from the northeast quadrant and another low system from the southwest quadrant of Maria. On the other hand, the SHiELD_IFS improves on the IFS by better simulating the strength of the Bermuda High, and low systems in the southwest, northwest, and northeast quadrants allowing for better track alignment with observations. We also found that the northward track bias of Maria in the legacy GFS and SHiELD with the GFS initial conditions (SHiELD_GFS) was associated with a weaker Continental High which contributed less northerly steering flow compared to that in the IFS and SHiELD_IFS. Furthermore, the Bermuda High was relatively weak in the SHiELD_GFS, while the two low systems in the northwest and northeast quadrants contributed steering flow opposing Maria’s moving direction, causing a slowdown of translation speed of Maria in the SHiELD_GFS. By performing this piecewise potential vorticity diagnosis on all of the storms in the 2017 North Atlantic Hurricane Season, we could possibly identify the key elements that generate the biases in TC track forecasts in these models.

Cratons are stable parts of the Earth’s continental lithosphere that have remained largely undeformed for several billion years. These consist of crustal granite-greenstone terrains coupled to roots of strong, buoyant cratonic lithospheric mantle that extend up to several hundreds of kms depth. Due to their stability, cratons preserve a record of the tectonics and the thermal evolution of the mantle in the early Earth. These observations suggest that the highly viscous (strong) character of cratons hampered the viability of early Earth tectonics, thereby affecting mantle convection patterns and cooling. In this study, we investigate the controls of stiff cratons on the initiation of subduction and mantle thermal evolution on the early Earth. Using numerical models, we simulate the effects of strong and buoyant cratons on mantle convection. We vary a set of parameters including (i) width and thickness of cratons, and (ii) viscosity ratio between cratonic lithosphere and cratonic crust. We test initial conditions varying the number of cratons, which is unconstrained for early Earth and associated it to mantle cooling rates. Our preliminary results show that the mantle cooling rate decreases with increasing number of cratons. Because mantle cooling rates affect the early Earth transition from a basaltic drip regime to initiation of subduction, we show that the craton coverage on the early Earth controls the time of onset of plate tectonics. Furthermore, we observe that cratons will remain separate or combine depending on the convective cell size, which is function of mantle cooling.

A recent study categorized the Madden-Julian Oscillation (MJO) during the boreal winter season into four types including stand, jump, slow eastward propagating, and fast eastward propagating MJO. This study focuses on the stand and jump MJO events. Based on whether their convection penetrates the Maritime Continent (MC), stand and jump MJO are combined as non-penetrating (NP) MJO, while the rest two types are combined as eastward-penetrating (EP) MJO. Results reveal the relative roles of the westward-propagating wave (WPW), as well as the QBO and ENSO, in limiting MJO propagation. Lack of the pre-moistening over the southern sea surface of MC is responsible for NP MJO’s failure to penetrate MC. The active convection of WPW hinders the NP MJO descending branch over the Pacific and therfore leads to the insufficient meridional advective moistening over the southern sea surface of the MC. The overall drying over the MC for jump MJO also comes from the intraseasonal dry amonalies induced by WPW over the western Pacific, which are then advected to MC by the superposed seasonal mean northern easterlies. The independent convection over the Pacific for jump MJO is influenced by a combined effect of the QBO and ENSO. Under the influence of the preferred QBO westerly (QBOW) phase, the tropopause instability of NP MJO is decoupled with its convection. The decoupled tropopause instability propagates eastward into the Pacific, and amplifies the local WPW convection there for jump MJO. For stand MJO, however, the La Nina-like seasonal mean cool sea surface temperature (SST) anmalies confine WPW within the western Pacific. Therefore, the decoupled tropopause instability of stand MJO is out phase of WPW over the central Pacific, and fails to induce an independent convection as a result. The similar diagnosis is further applied to the CESM2 and E3SM CMIP6 historical simulations to investigate the MJO diversity and propagation in climate models. Four MJO types as in the observation are successfully categorized in these two models. The accompanied westward propagating waves over the central-western Pacific for NP MJO events are also evident. However, models suffer from some biases in MJO propagation such as too many NP MJO events than the observation.

There is a need to better characterize aerosol absorption due to its remarkable radiative effects on climate. In particular, we need to understand the separation of total absorption between the two main components, the black carbon (BC) and brown carbon (BrC), especially in places where the anthropic influence is little, such as in pristine regions like Central Amazonia. The mechanisms that control the formation and evolution of BC and BrC in tropical forests remain unclear. In this study, we have performed detailed measurements at the Amazon Tall Tower Observatory (ATTO) tower on aerosols collected in Nuclepore filters and analyzed them with high-resolution optical spectrometers with a wide spectral range (300 to 2500 nm). Thus, we determined the absorption characteristics of BrC as a function of wavelength. The results show that BrC absorption is spectrally significant below 660 nm and is maximum at wavelengths close to 370 nm. Combining the measured spectral dependency with MIE modeling of the BC contribution, we determined that the BrC accounts for 14.8% of the total absorption. A similar fraction of BrC to total absorption was obtained through a similar analysis of in-situ measurements. Elemental chemical analysis of the filters, cluster, and factor analysis shows that the BrC is associated with airborne dust. The different methods to quantify BC and BrC are consistent and show similar results. This study will allow the quantification of the role of BrC and BC in aerosol absorption of radiation in Amazonia.

Earth System Models (ESMs) traditionally operate at large horizontal resolutions, on the order of 100 km, which can obscure the effects of smaller scale heterogeneity. When examining land surface states and fluxes in ESMs, one common approach to mitigate this issue is to divide the sub-grid land surface into distinct homogeneous clusters and then resolve the water, energy, and biogeochemical processes on each cluster or tile. The literature, as well as work in the Coupling of Land and Atmospheric Subgrid Parameterizations (CLASP) project, indicates that surface heterogeneity has important implications for atmospheric processes as well. Previous work using large-eddy simulation (LES) shows that spatial variability in surface heating can produce significant secondary circulations closely related to the type and scale of heterogeneity that are not captured by single column models. This presentation aims to address this persistent weakness by using a clustering or tiling approach, similar to that used with land surface processes, for the atmosphere. To accomplish this task, we run the Cloud Layers Unified By Binomials (CLUBB) single column model, a sub-grid turbulence and cloud parameterization scheme, over a 100 km box centered at the Southern Great Plains site in Oklahoma for a variety of surface and atmospheric conditions. The model is run independently over multiple surface clusters defined by surface sensible heat fluxes in the given domain. Results indicate that significant differences exist for some cases between the single column and multicolumn cases for liquid water path (LWP) as well as the turbulent kinetic energy (TKE) budget, and that results converge on consistent results with a fairly low number of clusters (i.e., atmospheric columns). We follow this up with a connected multicolumn setup where each column is dynamically connected with the other columns throughout the run to qualitatively capture the circulations observed in the LES output. The existing results show promise for capturing the effects of subgrid scale surface flux heterogeneity on the lower atmosphere in ESMs with the application of a multicolumn CLUBB setup.

Regional crop production estimates are important in both public and private sectors to ensure the adequacy of a food supply and aid policymakers and farmers in managing harvest, storage, import/export, transportation, and anticipate market fluctuations. Food security will be progressively challenged by population growth and climate change. Thus, the prediction of accurate regional crop yield is essential for national food security and the sustainable development of the Indian agriculture sector. In this study, we have selected Punjab, the highest wheat yielding state in India. The district-wise wheat yield data were available for the year 2000 – 2019. We have used several covariates for crop health viz. normalized difference vegetation index (NDVI), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR); meteorological indicators viz. land surface temperature (LST), and evapotranspiration (ET); and surface characteristics viz. protrusion coefficient (PC). These indicators were generated at 250 m spatial resolution from the MODIS data using Google Earth Engine. The whole data was divided into two groups for training (2000 – 2009, 2011, 2013, 2014, 2016 - 2019) and testing (2010, 2012, 2015), which were randomly selected. This study uses the random forest (RF) regression method to create a wheat yield prediction model. We created several combinations of covariates and found that fAPAR and ET are highly correlated with NDVI and do not have much influence on the model’s prediction accuracy. Hence, only four out of six covariates were selected for final training. The coefficient of determination between district-level yield vs. (NDVI/LAI/PC/LST) was 0.37/0.31/0.15/0.13 respectively. We used randomized search cross-validation as well as grid search cross-validation for hyper-parameter tuning. Furthermore, we used mean absolute error (MAE) and accuracy as quality metrics. The MAE for training was 0.1870 t/Ha with 95.81% accuracy, whereas the MAE on test data was obtained as 0.4293 t/Ha with 90.02% accuracy. The results of this study are within acceptable error limits of the published research articles. Overall, this study demonstrates that covariates derived from coarse resolution satellite data can predict district-level crop yield with reasonable accuracy.

This study investigates the characteristics and climate impacts of the quasi-biweekly oscillation (QBWO) over the western North Pacific (WNP) in boreal winter based on observational and reanalysis data and numerical experiments with a simplified model. The wintertime convection over the WNP is dominated by significant biweekly variability with a 10-20-day period, which explains about 66% of the intraseasonal variability. Its leading mode on the biweekly timescale is a northwestward-propagating convection dipole over the WNP, which oscillates over a period of about 12 days. When the convection-active center of this QBWO is located to the east of the Philippines, it can generate an anticyclonic vorticity source to the south of Japan via inducing upper-tropospheric divergence and excite a Rossby wave train propagating towards North America along the Pacific rim. The resultant lower-tropospheric circulation facilitates cold advection and leads to cold anomalies over central North America in the following week. This result highlights a cause-effect relationship between the WNP convection and the North American climate on the quasi-biweekly timescale and may provide some prediction potential for the North American climate.

Satellite-based land surface phenology information (e.g. time-series spectral metrics and seasonal composites) can be used to model tropical tree canopy cover (TCC) but is limited by missing observations caused by clouds and cloud shadows and the sensor failure. Gap-filling (i.e. reconstruction of missing data in images) provides a key to solving the limitation, but its effectiveness and necessity for a given application, e.g. TCC modelling have been rarely explained. The main goal of this study was to examine the effectiveness and necessity of gap-filling, applied to the TCC modelling using Landsat time series. We chose Missing Observation Prediction Based on Spectral-Temporal Metrics (MOPSTM) method for its simple tuning, fast computation, and good performance in Landsat missing data reconstruction. MOPSTM models the relationship between valid observations in time series and spectral-temporal metrics in the k-Nearest Neighbor regression to predict the missing observations. We provided a quantitative comparison of TCC modelling using predictor variables (e.g. seasonal composite, spectral-temporal metrics, and harmonic regression coefficients) derived from Landsat time series that included gap-filling versus those that did not include gap-filling. With 1-year Landsat 8 Surface Reflectance time series acquired from a tropical area in Taita Hills, Kenya throughout 2015 and a reference TCC map scaled in 0 – 100 derived from the Airborne Laser Scanning (ALS) data between 2014 and 2015, we applied random forest regression to model TCC using the predictor variables. The results indicated that TCC modelling using gap-filled predictors yielded smaller root mean square error than that using the non-gap-filled predictors, which proved the effectiveness of gap-filling in tropical TCC modelling. The effects of gap-filling might be reduced when the predictor variables were of high quality, e.g. median composite derived from the time series where sufficient observations exist. We concluded that gap-filling has a positive effect on the accuracy of TCC modelling.

Providing environmental flows is challenging in the middle portion of the Rio Grande/Bravo Basin (between Elephant Butte Reservoir in New Mexico and Presidio, Texas) where water demand has continued to increase over time despite limited river water and dropping groundwater levels. Riparian ecosystems in this agriculture-dominated desert environment will likely become more vulnerable as competition over scarce water increases in the face of growing demand and dwindling supply. Little to no water is allocated to riparian ecosystems unless water or water rights are purchased or transferred to sustain those systems. Ongoing debates about providing environmental flows for riparian forest galleries in this water-scarce region have not been backed by quantitative modeling results of potential impacts on surface water and groundwater availability. We quantify water requirements to provide a menu of options for environmental flow allocation to establish cottonwood forest galleries. We apply hydrologic modeling under a projected warm-dry future to determine the frequency of river water availability for providing minimum environmental flows, and to evaluate water budget tradeoffs associated with environmental flow allocations in this region. Results inform water resources management decisions that support riparian habitats.

Climatic changes with warmer temperatures in mid-latitudes require the need to improve the simplified vegetation scheme of the regional climate model COSMO-CLM, which is not capable of modelling complex processes depending on temperature, water availability and day length. Thus, we have implemented the physically based Ball-Berry approach coupled with photosynthesis processes based on Farquhar and Collatz models for C3 and C4 plants in COSMO-CLM (v 5.16). The implementation of the new algorithms includes the replacement of the “one-big leaf” by a “two-big leaf” approach. We performed single column simulations with COSMO-CLM over three observational sites with C3 grass plants in Germany for the period from 1999 to 2015 (Parc, Linden and Lindenberg domain, Fig.1). Hereby, we tested three alternative formulations of the new algorithms. The first formulation (COSMO_v3.5) is based on the Community Land Model (CLM v3.5) algorithms for stomatal resistance, which depend on leaf photosynthesis, CO2 partial and vapor pressure and minimum stomatal conductance. The second one is COSMO_v4.5, which is based on the phenology algorithms of CLM v4.5 including the soil water stress function. The third one is similar to COSMO_v4.5 but with additional equations for dry leaf calculations (COSMO_v4.5e). The results revealed major differences in the annual cycle of stomatal resistance compared to the control simulation (COSMO_orig) with the original algorithm (Fig. 1). The biggest changes are from October to April when stomata are closed. The summer values of experiments are closer to measured values, than COSMO_orig. Further, changes in the stomatal resistance algorithms improve the accuracy of calculated transpiration rate and total evapotranspiration. The results indicate that changes in stomatal resistance and photosynthesis algorithms can improve the accuracy of other parameters of the COSMO-CLM model by comparing them with FLUXNET data and meteorological observations at the sites, and GLEAM datasets. Figure 1: The stomatal resistance based on COSMO-CLM experiments (a - annual cycle; b - daily values from 01.06.2011 to 15.09.2011) for: I – Parc domain, II – Linden domain, III – Lindenberg domain. This research was funded by the German Research Foundation (DFG) through grant number 401857120

Tropical peatlands are estimated to hold carbon stocks of 70 Pg C or more as partly-decomposed organic matter, or peat. Peat may accumulate over thousands of years into gently mounded deposits called peat domes with a relief of several meters over distances of kilometers. The curved shape of peat domes accounts for much of the carbon storage in these landscapes, but their subtle topographic signal is difficult to measure. As many of the world’s tropical peatlands are remote and inaccessible, spaceborne laser altimetry data from missions such as NASA’s Global Ecosystem Dynamics Investigation (GEDI) and the Advanced Topographic Laser Altimeter System (ATLAS) instrument on the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory could help to describe these deposits. However, for better and for worse, tropical peatlands may also support forests with high above-ground biomass—averages of over 200 Mg C / ha have been reported—which increases their carbon stocks but further complicates determination of their surface topography using laser altimetry. In this work, we evaluate retrieval of ground elevations and canopy metrics derived from GEDI waveform data, as well as single-photon data from ATLAS, with reference to an airborne laser scanning dataset covering an area of over 100 km^2 in the Belait District of Brunei Darussalam. We find that despite infrequent ground retrievals, with regularization these spaceborne platforms can provide useful data for tropical peatland surface altimetry.