Many Earth system models (ESMs) approximate surface emissivity as a constant. This broadband approximation reduces computational burden, yet biases longwave (LW) atmospheric fluxes and heating by neglecting the spectral structure of surface emissivity and atmospheric absorption. These biases are largest over surfaces with strongly varying emissivity and minimal atmospheric opacity (e.g., due to water vapor and clouds). Our study focuses on liquid water, ice, and snow surfaces. We use LW spectral emissivity ε(λ) calculated via the Fresnel equations and validated against a dataset of spectral surface emissivity. We flux-weight and bin ε(λ) into 16 spectral bands accepted by an offline single-column atmospheric radiative transfer model (RRTMG_LW) commonly used in ESMs (including E3SM and CESM). We quantify flux and heating biases introduced by broadband emissivity assumptions in comparison with the 16-band spectrally resolved case for three different surface types, three standard atmospheric profiles, and for the key drivers surface temperature, cloud water path, and atmospheric water vapor. In addition, we devise and test novel greybody and semi-spectral methods of representing ε(λ) with the goal of reducing biases while preserving computational efficiency. We find that typical broadband assumptions artificially cool Earth’s surface, thereby stabilizing the lower troposphere. LW upwelling flux is overestimated by 4.5 W/m2 (~1.4%) at the bottom of a mid-latitude winter atmosphere over an ice surface, and by 3.3 W/m2 (~1.4%) at the top of atmosphere. Lastly, we find that a semi-spectral approach (five bands instead of 16) reduces biases by up to 99% relative to the broadband approximation.

This material is based upon work supported by DOE E3SM (DE-SC0019278), NASA (OSTFL 80NSSC22K1743), and NSF (OAC-2004993). Work performed at UC Irvine, located on the ancestral, unceded homelands of the Acjachemen and Tongva peoples.

This paper describes the first implementation of the d x=3.25 km version of the Energy Exascale Earth System Model (E3SM) global atmosphere model and its behavior in a 40 day prescribed-sea-surface-temperature simulation (Jan 20-Feb 28, 2020). This simulation was performed as part of the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) phase 2 model intercomparison. Effective resolution is found to be $\sim 6x the horizontal grid resolution despite using a coarser grid for physical parameterizations. Despite this new model being in an immature and untuned state, moving to 3.25 km grid spacing solves several long-standing problems with the E3SM model. In particular, Amazon precipitation is much more realistic, the frequency of light and heavy precipitation is improved, agreement between the simulated and observed diurnal cycle of tropical precipitation is excellent, and the vertical structure of tropical convection and coastal stratocumulus look good. In addition, the new model is able to capture the frequency and structure of important weather events (e.g. hurricanes, midlatitude storms including atmospheric rivers, and cold air outbreaks). Interestingly, this model does not get rid of the erroneous southern branch of the intertropical convergence zone nor the tendency for strongest convection to occur over the Maritime Continent rather than the West Pacific, both of which are classic climate model biases. Several other problems with the simulation are identified, underscoring the fact that this model is a work in progress.

This work documents version two of the Department of Energy’s Energy Exascale Earth System Model (E3SM). E3SM version 2 (E3SMv2) is a significant evolution from its predecessor E3SMv1, resulting in a model that is nearly twice as fast and with a simulated climate that is improved in many metrics. We describe the physical climate model in its lower horizontal resolution configuration consisting of 110 km atmosphere, 165 km land, 0.5° river routing model, and an ocean and sea ice with mesh spacing varying between 60 km in the mid-latitudes and 30 km at the equator and poles. The model performance is evaluated by means of a standard set of Coupled Model Intercomparison Project Phase 6 (CMIP6) Diagnosis, Evaluation, and Characterization of Klima (DECK) simulations augmented with historical simulations as well as simulations to evaluate impacts of different forcing agents. The simulated climate is generally realistic, with notable improvements in clouds and precipitation compared to E3SMv1. E3SMv1 suffered from an excessively high equilibrium climate sensitivity (ECS) of 5.3 K. In E3SMv2, ECS is reduced to 4.0 K which is now within the plausible range based on a recent World Climate Research Programme (WCRP) assessment. However, E3SMv2 significantly underestimates the global mean surface temperature in the second half of the historical record. An analysis of single-forcing simulations indicates that correcting the historical temperature bias would require a substantial reduction in the magnitude of the aerosol-related forcing.

Warm and dry föhn winds on the lee side of the Antarctic Peninsula (AP) mountain range cause surface melt that can destabilize vulnerable ice shelves. Topographic funneling of these winds through mountain passes and canyons leads to localized wind-induced melt which is difficult to identify without direct measurements. Our Föhn Detection Algorithm (FonDA) identifies the surface Föhn signature using data from twelve Automatic Weather Stations on the AP and uses machine learning to detect föhn in 5km Regional Atmospheric Climate Model 2 (RACMO2.3p2) output and ERA5 reanalysis data. We estimate and compare the climatology and impact of föhns on the AP surface energy budget, surface melt pattern, and melt quantity from 1979-2018. We show that föhn-induced melt is strongest at the eastern base of the AP and the northern portion of the Larsen C ice shelf. We identify previously unknown wind-induced melt possibly katabatic in nature on the Wilkins and George VI ice shelves. Neither RACMO2 nor ERA5 datasets show a significant increase in föhn melt thus far despite a more positive Southern Annular Mode and increasing surface temperatures. The warming climate and associated southward shift of westerly winds on the AP suggest a likely increase in the wind-induced melt that can densify firn, form melt ponds, and weaken ice shelf stability, however that trend remains insignificant for the past 40 years.

Vegetation is a major control on dust emission because it extracts momentum from the wind and shelters the soil surface, protecting dry and loose material from erosion by winds. Many traditional dust emission models (TEMs) assume that the Earth’s land surface is devoid of vegetation, adjust dust emission using a vegetation cover complement, and calibrate the magnitude of modelled emissions to atmospheric dust. We compare this approach with a novel albedo-based dust emission model (AEM) which calibrates Earth’s land surface normalised shadow (1-albedo) to shelter depending on wind speed, to represent aerodynamic roughness spatio-temporal variation. Existing datasets of satellite observed dust emission from point sources (DPS) and dust optical depth (DOD) show little spatial relation and DOD frequency exceeds DPS frequency by up to two orders of magnitude. Relative to DPS frequency, both dust emission models showed strong relations, but over-estimate dust emission frequency, suitable for calibration to observed dust emission. Our results show that TEMs over-estimate large dust emission over vast vegetated areas and produce considerable false change in dust emission, relative to the AEM. It is difficult to avoid the conclusion, raised by other literature, that calibrating dust cycle models to atmospheric dust has hidden for more than two decades, these TEM modelling weaknesses and its poor performance. The AEM overcomes these weaknesses and improves performance without masks or vegetation cover. Considerable potential exists for Earth System Models driven by prognostic albedo, to reveal new insights of aerosol effects on, and responses to, contemporary and environmental change projections.

JavaScript Object Notation (JSON) is an increasingly popular text format for data exchange. netCDF encapsulates the Common Data Model (CDM) and a binary format for machine-independent and network-transparent storage of scientific data and metadata. Previous netCDF-to-JSON translators have been custom solutions with incomplete features, or based on more complex formats than the CDM. Here we describe a flexible JSON format that describes any classic or extended format netCDF dataset. This format, called NCO-JSON, expresses the richness of the CDM and increases interoperability between web services and netCDF data. NCO-JSON requires no reserved keywords and so is completely compatible with all netCDF datasets. It allows for selectable levels of fidelity to the original data and metadata. The most concise and human-legible form of NCO-JSON is also lossy. By design it distinguishes only the three atomic JSON datatypes (float, string, and int). This suffices for many purposes yet cannot guarantee bit-for-bit reproducibility of many netCDF datatypes, especially in round-trip translations. NCO-JSON uses a more complex object notation to encode the additional type information required to reproduce netCDF datasets with full fidelity. We present the rules and design of the NCO-JSON format, show results with real-world datasests, quantify the space advantages vs. alternate formats (both JSON and XML), and discuss corner cases and possible extensions.

The twin pressures to achieve mind-share and to harness available computing power drive the evolution of geoscientific data analysis tools. Such tools have enabled a remarkable progression in the atomic or fundamental unit of data they can easily analyze. In the mid-1980s we analyzed one or a few naked arrays at at time, and now researchers routinely intercompare climatological ensembles each comprising thousands of files of heterogeneous variables richly dressed in metadata. Two complementary semantic trends have empowered this analytical revolution: more intuitive and concise analysis commands that can exploit more standardized and brokered self-describing data stores. This talk highlights how tool developers can leverage these trends to successfully imagine and build the analysis tools of tomorrow by understanding the needs of domain researchers and the power of domain specific languages today. This talk will also highlight recent improvements in compression speed and interoperability that geoscientists can exploit to reduce our carbon footprint. Observations and simulations to advance Earth system sciences generate exabytes of archived data per year. Storage accounts for about 40% of datacenter power consumption, with its attendant consequences for greenhouse gas emissions and environmental sustainability. Precision-preserving lossy compression can further reduce the size of losslessly compressed data by 10-25% without compromising its scientific content. Modern lossless codecs (e.g., Zstandard or Zlib-ng) accelerate compression and decompression, relative to the traditional Zlib, by factors of 2-5x with no penalty in compression ratio. These proven modern compression technologies can help geoscientific datacenters become significantly greener.

Larsen A and B ice shelves were affected by surface melt which preconditioned them for rapid disintegration due to hydrofracture and densification. Recently, warm and dry foehn winds have been discovered to melt the vulnerable Larsen C Ice Shelf (LCIS) surface via sensible heat transfer during polar night. The climatological extent and intensity of polar night surface melt and their effects on the ice surface energy budget are unknown. Here we quantify the spatial pattern and temporal variability of foehn winds and associated melt events during polar night to understand the ice shelf surface mass balance and indirect implications for ice shelf vulnerability. Our Foehn Detection Algorithm (FonDA) uses events identified from in situ Automated Weather Stations (AWS) to calibrate foehn detection from reanalysis data covering all of Antarctica and Greenland. We present a climatology of foehn-driven surface melt days, melt water equivalent, fraction of melt that occurs during polar night, and the surface energy budget. Preliminary results show that foehns perturb sensible heat fluxes by up to 300 Wm-2 and surface air temperatures by up to 13 °C in the absence of shortwave radiation.

Lossless compression can reduce climate data storage by 30-40%. In general, further reductions require lossy compression that also reduces precision. Fortunately, geoscientific models and measurements generate false precision (scientifically meaningless data bits) that can be eliminated without sacrificing scientifically meaningful data. We introduce Bit Grooming, a lossy compression algorithm that removes the bloat due to false- precision, those bits and bytes beyond the meaningful precision of the data. We evaluated Bit Grooming against competitors Linear Packing, Layer Packing, and GRIB2/JPEG2000.