Measuring Winds From Space to Reduce the Uncertainty in the Southern Ocean Carbon Fluxes: Science Requirements and Proposed Mission

Strong winds in Southern Ocean storms drive air-sea carbon and heat fluxes. These fluxes are integral to the global climate system and the wind speeds that drive them are increasing. The current scatterometer constellation measuring vector winds remotely undersamples these storms and the higher winds within them, leading to potentially large biases in Southern Ocean wind reanalyses and the fluxes that derive from them. This observing system design study addresses these issues in two ways. First, we describe an addition to the scatterometer constellation, called Southern Ocean Storms -- Zephyr, to increase the frequency of independent observations, better constraining high winds. Second, we show that potential reanalysis wind biases over the Southern Ocean lead to uncertainty over the sign of the net winter carbon flux. More frequent independent observations per day will capture these higher winds and reduce the uncertainty in estimates of the global carbon and heat budgets

Contrasting responses of mean and extreme snowfall to climate change

Snowfall is an important element of the climate system, and one that is expected to change in a warming climate. Both mean snowfall and the intensity distribution of snowfall are important, with heavy snowfall events having particularly large economic and human impacts. Simulations with climate models indicate that annual mean snowfall declines with warming in most regions but increases in regions with very low surface temperatures. The response of heavy snowfall events to a changing climate, however, is unclear. Here I show that in simulations with climate models under a scenario of high emissions of greenhouse gases, by the late twenty-first century there are smaller fractional changes in the intensities of daily snowfall extremes than in mean snowfall over many Northern Hemisphere land regions. For example, for monthly climatological temperatures just below freezing and surface elevations below 1,000 metres, the 99.99th percentile of daily snowfall decreases by 8% in the multimodel median, compared to a 65% reduction in mean snowfall. Both mean and extreme snowfall must decrease for a sufficiently large warming, but the climatological temperature above which snowfall extremes decrease with warming in the simulations is as high as −9 °C, compared to −14 °C for mean snowfall. These results are supported by a physically based theory that is consistent with the observed rain–snow transition. According to the theory, snowfall extremes occur near an optimal temperature that is insensitive to climate warming, and this results in smaller fractional changes for higher percentiles of daily snowfall. The simulated changes in snowfall that I find would influence surface snow and its hazards; these changes also suggest that it may be difficult to detect a regional climate-change signal in snowfall extremes.National Science Foundation (U.S.) (Grant AGS-1148594)United States. National Aeronautics and Space Administration (ROSES Grant 09-IDS09-0049

Simulations of ocean deoxygenation in the historical era: insights from forced and coupled models

Ocean deoxygenation due to anthropogenic warming represents a major threat to marine ecosystems and fisheries. Challenges remain in simulating the modern observed changes in the dissolved oxygen (O2). Here, we present an analysis of upper ocean (0-700m) deoxygenation in recent decades from a suite of the Coupled Model Intercomparison Project phase 6 (CMIP6) ocean biogeochemical simulations. The physics and biogeochemical simulations include both ocean-only (the Ocean Model Intercomparison Project Phase 1 and 2, OMIP1 and OMIP2) and coupled Earth system (CMIP6 Historical) configurations. We examine simulated changes in the O2 inventory and ocean heat content (OHC) over the past 5 decades across models. The models simulate spatially divergent evolution of O2 trends over the past 5 decades. The trend (multi-model mean and spread) for upper ocean global O2 inventory for each of the MIP simulations over the past 5 decades is 0.03 ± 0.39×1014 [mol/decade] for OMIP1, −0.37 ± 0.15×1014 [mol/decade] for OMIP2, and −1.06 ± 0.68×1014 [mol/decade] for CMIP6 Historical, respectively. The trend in the upper ocean global O2 inventory for the latest observations based on the World Ocean Database 2018 is −0.98×1014 [mol/decade], in line with the CMIP6 Historical multi-model mean, though this recent observations-based trend estimate is weaker than previously reported trends. A comparison across ocean-only simulations from OMIP1 and OMIP2 suggests that differences in atmospheric forcing such as surface wind explain the simulated divergence across configurations in O2 inventory changes. Additionally, a comparison of coupled model simulations from the CMIP6 Historical configuration indicates that differences in background mean states due to differences in spin-up duration and equilibrium states result in substantial differences in the climate change response of O2. Finally, we discuss gaps and uncertainties in both ocean biogeochemical simulations and observations and explore possible future coordinated ocean biogeochemistry simulations to fill in gaps and unravel the mechanisms controlling the O2 changes

An integrated approach to quantifying uncertainties in the remaining carbon budget

The remaining carbon budget quantifies the future CO2 emissions to limit global warming below a desired level. Carbon budgets are subject to uncertainty in the Transient Climate Response to Cumulative CO2 Emissions (TCRE), as well as to non-CO2 climate influences. Here we estimate the TCRE using observational constraints, and integrate the geophysical and socioeconomic uncertainties affecting the distribution of the remaining carbon budget. We estimate a median TCRE of 0.44 °C and 5–95% range of 0.32–0.62 °C per 1000 GtCO2 emitted. Considering only geophysical uncertainties, our median estimate of the 1.5 °C remaining carbon budget is 440 GtCO2 from 2020 onwards, with a range of 230–670 GtCO2, (for a 67–33% chance of not exceeding the target). Additional socioeconomic uncertainty related to human decisions regarding future non-CO2 emissions scenarios can further shift the median 1.5 °C remaining carbon budget by ±170 GtCO2

Regionally aggregated, stitched and de‐drifted CMIP‐climate data, processed with netCDF‐SCM v2.0.0

The world's most complex climate models are currently running a range of experiments as part of the Sixth Coupled Model Intercomparison Project (CMIP6). Added to the output from the Fifth Coupled Model Intercomparison Project (CMIP5), the total data volume will be in the order of 20PB. Here, we present a dataset of annual, monthly, global, hemispheric and land/ocean means derived from a selection of experiments of key interest to climate data analysts and reduced complexity climate modellers. The derived dataset is a key part of validating, calibrating and developing reduced complexity climate models against the behaviour of more physically complete models. In addition to its use for reduced complexity climate modellers, we aim to make our data accessible to other research communities. We facilitate this in a number of ways. Firstly, given the focus on annual, monthly, global, hemispheric and land/ocean mean quantities, our dataset is orders of magnitude smaller than the source data and hence does not require specialized ‘big data’ expertise. Secondly, again because of its smaller size, we are able to offer our dataset in a text-based format, greatly reducing the computational expertise required to work with CMIP output. Thirdly, we enable data provenance and integrity control by tracking all source metadata and providing tools which check whether a dataset has been retracted, that is identified as erroneous. The resulting dataset is updated as new CMIP6 results become available and we provide a stable access point to allow automated downloads. Along with our accompanying website (cmip6.science.unimelb.edu.au), we believe this dataset provides a unique community resource, as well as allowing non-specialists to access CMIP data in a new, user-friendly way

Supporting GFDL data for Southern Ocean Freshwater release model experiments Initiative (SOFIA)

Note: This data collection is hosted at the Geophysical Fluid Dynamics Laboratory. Data DOI capability is provided by PUL. Please refer to the README for a detailed description of the dataset. For questions, please contact [email protected], with the subject line including the title of the dataset.See "how_to_access_data.txt" to access data files from GDFL servers.This output was produced in coordination with the Southern Ocean Freshwater release model experiments Initiative (SOFIA) and is the Tier 1 experiment where freshwater is delivered in a spatially and temporally uniform pattern at the surface of the ocean at sea surface temperature in a 1-degree latitude band extending from Antarctica’s coastline. The total additional freshwater flux imposed as a monthly freshwater flux entering the ocean is 0.1 Sv. Users are referred to the methods section of Beadling et al. (2022) for additional details on the meltwater implementation in CM4 and ESM4. The datasets in this collection contain model output from the coupled global climate model, CM4, and Earth System Model, ESM4, both developed at the Geophysical Fluid Dynamics Laboratory (GFDL) of the National Oceanic and Atmospheric Administration (NOAA). The ocean_monthly_z and ocean_annual_z output are provided as z depth levels in meters as opposed to the models native hybrid vertical ocean coordinate which consists of z* (quasi-geopotential) coordinates in the upper ocean through the mixed layer, transitioning to isopycnal (referenced to 2000 dbar) in the ocean interior. Please see README for further details.File list: doc/README data/ cm4_tier1_antwater.agessc.ocean_annual_z.tar.gz cm4_tier1_antwater.bsnk.ice_monthly.tar.gz cm4_tier1_antwater.cld_amt.atmos_level_monthly.tar.gz cm4_tier1_antwater.evap.atmos_level_monthly.tar.gz cm4_tier1_antwater.evs.ocean_monthly.tar.gz cm4_tier1_antwater.ficeberg.ocean_monthly.tar.gz cm4_tier1_antwater.frazil.ice_monthly.tar.gz cm4_tier1_antwater.friver.ocean_monthly.tar.gz cm4_tier1_antwater.heat_content_surfwater.ocean_monthly.tar.gz cm4_tier1_antwater.hfds.ocean_monthly.tar.gz cm4_tier1_antwater.hflso.ocean_monthly.tar.gz cm4_tier1_antwater.hfsifrazil.ocean_monthly.tar.gz cm4_tier1_antwater.hfsso.ocean_monthly.tar.gz cm4_tier1_antwater.lsrc.ice_monthly.tar.gz cm4_tier1_antwater.mlotst.ocean_monthly.tar.gz cm4_tier1_antwater.precip.atmos_level_monthly.tar.gz cm4_tier1_antwater.prlq.ocean_monthly.tar.gz cm4_tier1_antwater.prsn.ocean_monthly.tar.gz cm4_tier1_antwater.rlntds.ocean_monthly.tar.gz cm4_tier1_antwater.rsntds.ocean_monthly.tar.gz cm4_tier1_antwater.sfdsi.ocean_monthly.tar.gz cm4_tier1_antwater.siconc.ice_monthly.tar.gz cm4_tier1_antwater.sithick.ice_monthly.tar.gz cm4_tier1_antwater.siu.ice_monthly.tar.gz cm4_tier1_antwater.siv.ice_monthly.tar.gz cm4_tier1_antwater.slp.atmos_level_monthly.tar.gz cm4_tier1_antwater.snowfl.ice_monthly.tar.gz cm4_tier1_antwater.so.ocean_annual_z.tar.gz cm4_tier1_antwater.so.ocean_monthly_z_complete.tar.gz cm4_tier1_antwater.static_fields.tar.gz cm4_tier1_antwater.tauuo.ocean_monthly.tar.gz cm4_tier1_antwater.tauvo.ocean_monthly.tar.gz cm4_tier1_antwater.temp.atmos_level_monthly.tar.gz cm4_tier1_antwater.thetao.ocean_annual_z.tar.gz cm4_tier1_antwater.thetao.ocean_monthly_z.tar.gz cm4_tier1_antwater.t_ref.atmos_level_monthly.tar.gz cm4_tier1_antwater.ucomp.atmos_level_monthly.tar.gz cm4_tier1_antwater.umo.ocean_annual_z.tar.gz cm4_tier1_antwater.umo.ocean_monthly_z.tar.gz cm4_tier1_antwater.uo.ocean_annual_z.tar.gz cm4_tier1_antwater.uo.ocean_monthly_z.tar.gz cm4_tier1_antwater.u_ref.atmos_level_monthly.tar.gz cm4_tier1_antwater.vcomp.atmos_level_monthly.tar.gz cm4_tier1_antwater.vmo.ocean_annual_z.tar.gz cm4_tier1_antwater.vmo.ocean_monthly_z.tar.gz cm4_tier1_antwater.volcello.ocean_annual_z.tar.gz cm4_tier1_antwater.volcello.ocean_monthly_z.tar.gz cm4_tier1_antwater.vo.ocean_annual_z.tar.gz cm4_tier1_antwater.vo.ocean_monthly_z.tar.gz cm4_tier1_antwater.v_ref.atmos_level_monthly.tar.gz cm4_tier1_antwater.wfo.ocean_monthly.tar.gz cm4_tier1_antwater.zos.ocean_monthly.tar.gz esm4_tier1_antwater.agessc.ocean_annual_z.tar.gz esm4_tier1_antwater.bsnk.ice_monthly.tar.gz esm4_tier1_antwater.cld_amt.atmos_level_monthly.tar.gz esm4_tier1_antwater.evap.atmos_level_monthly.tar.gz esm4_tier1_antwater.evs.ocean_monthly.tar.gz esm4_tier1_antwater.ficeberg.ocean_monthly.tar.gz esm4_tier1_antwater.frazil.ice_monthly.tar.gz esm4_tier1_antwater.friver.ocean_monthly.tar.gz esm4_tier1_antwater.heat_content_surfwater.ocean_monthly.tar.gz esm4_tier1_antwater.hfds.ocean_monthly.tar.gz esm4_tier1_antwater.hflso.ocean_monthly.tar.gz esm4_tier1_antwater.hfsifrazil.ocean_monthly.tar.gz esm4_tier1_antwater.hfsso.ocean_monthly.tar.gz esm4_tier1_antwater.lsrc.ice_monthly.tar.gz esm4_tier1_antwater.mlotst.ocean_monthly.tar.gz esm4_tier1_antwater.precip.atmos_level_monthly.tar.gz esm4_tier1_antwater.prlq.ocean_monthly.tar.gz esm4_tier1_antwater.prsn.ocean_monthly.tar.gz esm4_tier1_antwater.rlntds.ocean_monthly.tar.gz esm4_tier1_antwater.rsntds.ocean_monthly.tar.gz esm4_tier1_antwater.sfdsi.ocean_monthly.tar.gz esm4_tier1_antwater.siconc.ice_monthly.tar.gz esm4_tier1_antwater.sithick.ice_monthly.tar.gz esm4_tier1_antwater.siu.ice_monthly.tar.gz esm4_tier1_antwater.siv.ice_monthly.tar.gz esm4_tier1_antwater.sivol.ice_monthly.tar.gz esm4_tier1_antwater.slp.atmos_level_monthly.tar.gz esm4_tier1_antwater.snowfl.ice_monthly.tar.gz esm4_tier1_antwater.so.ocean_monthly_z.tar.gz esm4_tier1_antwater.static_fields.tar.gz esm4_tier1_antwater.tauuo.ocean_monthly.tar.gz esm4_tier1_antwater.tauvo.ocean_monthly.tar.gz esm4_tier1_antwater.temp.atmos_level_monthly.tar.gz esm4_tier1_antwater.thetao.ocean_monthly_z.tar.gz esm4_tier1_antwater.t_ref.atmos_level_monthly.tar.gz esm4_tier1_antwater.ucomp.atmos_level_monthly.tar.gz esm4_tier1_antwater.umo.ocean_monthly_z.tar.gz esm4_tier1_antwater.uo.ocean_monthly_z.tar.gz esm4_tier1_antwater.u_ref.atmos_level_monthly.tar.gz esm4_tier1_antwater.vcomp.atmos_level_monthly.tar.gz esm4_tier1_antwater.vmo.ocean_monthly_z.tar.gz esm4_tier1_antwater.volcello.ocean_monthly_z.tar.gz esm4_tier1_antwater.vo.ocean_monthly_z.tar.gz esm4_tier1_antwater.v_ref.atmos_level_monthly.tar.gz esm4_tier1_antwater.wfo.ocean_monthly_complete.tar.gz esm4_tier1_antwater.zos.ocean_monthly.tar.g

Dominance of the Southern Ocean in Anthropogenic Carbon and Heat Uptake in CMIP5 Models

The authors assess the uptake, transport, and storage of oceanic anthropogenic carbon and heat over the period 1861-2005 in a new set of coupled carbon-climate Earth system models conducted for the fifth phase of the Coupled Model Intercomparison Project (CMIP5), with a particular focus on the Southern Ocean. Simulations show that the Southern Ocean south of 30 degrees S, occupying 30% of global surface ocean area, accounts for 43% 63% (42 + 5 PgC) of anthropogenic CO2 and 75% 622% (23 + 9 x 10(22) J) of heat uptake by the ocean over the historical period. Northward transport out of the Southern Ocean is vigorous, reducing the storage to 33 +/- 6 Pg anthropogenic carbon and 12 +/- 7 x 10(22) J heat in the region. The CMIP5 models, as a class, tend to underestimate the observation-based global anthropogenic carbon storage but simulate trends in global ocean heat storage over the last 50 years within uncertainties of observation-based estimates. CMIP5 models suggest global and Southern Ocean CO2 uptake have been largely unaffected by recent climate variability and change. Anthropogenic carbon and heat storage show a common broad-scale pattern of change, but ocean heat storage is more structured than ocean carbon storage. The results highlight the significance of the Southern Ocean for the global climate and as the region where models differ the most in representation of anthropogenic CO2 and, in particular, heat uptake

The Role of Continental Topography in the Present-Day Ocean’s Mean Climate

Climate models of varying complexity have been used for decades to investigate the impact of mountains on the atmosphere and surface climate. Here, the impact of removing the continental topography on the present-day ocean climate is investigated using three different climate models spanning multiple generations. An idealized study is performed where all present-day land surface topography is removed and the equilibrium change in the oceanic mean state with and without the mountains is studied. When the mountains are removed, changes found in all three models include a weakening of the Atlantic meridional overturning circulation and associated SST cooling in the subpolar North Atlantic. The SSTs also warm in all the models in the western North Pacific Ocean associated with a northward shift of the atmospheric jet and the Kuroshio. In the ocean interior, the magnitude of the temperature and salinity response to removing the mountains is relatively small and the sign and magnitude of the changes generally vary among the models. These different interior ocean responses are likely related to differences in the mean state of the control integrations due to differences in resolution and associated subgrid-scale mixing parameterizations. Compared to the results from 4xCO2 simulations, the interior ocean temperature changes caused by mountain removal are relatively small; however, the oceanic circulation response and Northern Hemisphere near-surface temperature changes are of a similar magnitude to the response to such radiative forcing changes.National Science Foundation6 month embargo; published online: 02 February 2022This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Simple Global Ocean Biogeochemistry With Light, Iron, Nutrients and Gas Version 2 (BLINGv2): Model Description and Simulation Characteristics in GFDL's CM4.0

Simulation of coupled carbon-climate requires representation of ocean carbon cycling, but the computational burden of simulating the dozens of prognostic tracers in state-of-the-art biogeochemistry ecosystem models can be prohibitive. We describe a six-tracer biogeochemistry module of steady-state phytoplankton and zooplankton dynamics in Biogeochemistry with Light, Iron, Nutrients and Gas (BLING version 2) with particular emphasis on enhancements relative to the previous version and evaluate its implementation in Geophysical Fluid Dynamics Laboratory's (GFDL) fourth-generation climate model (CM4.0) with 1/4 degrees ocean. Major geographical and vertical patterns in chlorophyll, phosphorus, alkalinity, inorganic and organic carbon, and oxygen are well represented. Major biases in BLINGv2 include overly intensified production in high-productivity regions at the expense of productivity in the oligotrophic oceans, overly zonal structure in tropical phosphorus, and intensified hypoxia in the eastern ocean basins as is typical in climate models. Overall, while BLINGv2 structural limitations prevent sophisticated application to plankton physiology, ecology, or biodiversity, its ability to represent major organic, inorganic, and solubility pumps makes it suitable for many coupled carbon-climate and biogeochemistry studies including eddy interactions in the ocean interior. We further overview the biogeochemistry and circulation mechanisms that shape carbon uptake over the historical period. As an initial analysis of model historical and idealized response, we show that CM4.0 takes up slightly more anthropogenic carbon than previous models in part due to enhanced ventilation in the absence of an eddy parameterization. The CM4.0 biogeochemistry response to CO2 doubling highlights a mix of large declines and moderate increases consistent with previous models.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]