Ship-based aerosol measurements in the Southern Ocean 

2021 ◽  
Author(s):  
Floortje van den Heuvel ◽  
Thomas Lachlan-Cope ◽  
Jonathan Witherstone ◽  
Dean Hurren ◽  
Anna Jones
Keyword(s):  
Southern Ocean ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Numerical Models ◽  
Cloud Microphysics ◽  
Wave Radiation ◽  
Atlantic Sector ◽  
Radiation Fluxes ◽  
Travel Restrictions

<p><span><span>Our limited understanding of clouds is a major source of uncertainty in climate sensitivity and climate model projections. The Southern Ocean is </span></span><span><span>the largest</span></span><span> </span><span><span>region</span></span><span><span> on Earth where climate models present large biases </span></span><span><span>in</span></span><span><span> short and long wave radiation fluxes which in turn affect the representation of sea surface temperatures, sea ice and ultimately large scale circulation in the S</span></span><span><span>outhern Hemisphere</span></span><span><span>. Evidence suggests that the poor representation of mixed phase clouds at the micro- and macro scales is responsible for the model biases in this region. The Southern Ocean Clouds (SOC) project </span></span><span><span>will be</span></span><span><span> a multi-scale, multi-platform approach with the aim of improving understanding of aerosol and cloud microphysics in this region, and their representation in numerical models. </span></span></p><p><span><span>Although this years’ first SOC measurement season has suffered greatly from travel restrictions, we have installed an Optical Particle Counter (OPC) on a ship (The James Clark Ross – JCR) and recorded aerosol measurements as it was travelling through the Atlantic sector of the Southern Ocean towards the Antarctic Peninsula, and while the ship was moored at South Georgia and Port Stanley. Over the course of one month, the OPC recorded particle sizes between 0.35 and 40 micrometers every six seconds. This study will present the data from this first, rather short Antarctic SOC season. It will present the analyses of the obtained OPC data alongside satellite observations and model reanalyses in the same region.</span></span></p>

scholarly journals Improving the Southern Ocean cloud albedo biases in a general circulation model

2020 ◽  
Vol 20 (13) ◽  
pp. 7741-7751
Author(s):  
Vidya Varma ◽  
Olaf Morgenstern ◽  
Paul Field ◽  
Kalli Furtado ◽  
Jonny Williams ◽  
...  
Keyword(s):  
Southern Ocean ◽  
General Circulation ◽  
Climate Models ◽  
Circulation Model ◽  
Short Wave ◽  
Cloud Microphysics ◽  
Global Climate Models ◽  
Ice Crystals ◽  
Wave Radiation ◽  
Short Wave Radiation

Abstract. The present generation of global climate models is characterised by insufficient reflection of short-wave radiation over the Southern Ocean due to a misrepresentation of clouds. This is a significant concern as it leads to excessive heating of the ocean surface, sea surface temperature biases and subsequent problems with atmospheric dynamics. In this study, we modify cloud microphysics in a recent version of the Met Office's Unified Model and show that choosing a more realistic value for the shape parameter of atmospheric ice crystals, in better agreement with theory and observations, benefits the simulation of short-wave radiation. In the model, for calculating the growth rate of ice crystals through deposition, the default assumption is that all ice particles are spherical in shape. We modify this assumption to effectively allow for oblique shapes or aggregates of ice crystals. Along with modified ice nucleation temperatures, we achieve a reduction in the annual-mean short-wave cloud radiative effect over the Southern Ocean by up to ∼4 W m−2 and seasonally much larger reductions compared to the control model. By slowing the growth of the ice phase, the model simulates substantially more supercooled liquid cloud.

scholarly journals Resolution dependence of CO2-induced Tropical Atlantic sector climate changes

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
W. Park ◽  
M. Latif
Keyword(s):  
Large Scale ◽  
Climate Changes ◽  
Climate Models ◽  
Climate Model ◽  
Equatorial Region ◽  
Atmospheric Response ◽  
Tropical Atlantic ◽  
Equatorial Atlantic ◽  
Atlantic Sector ◽  
Sst Bias

Abstract A long-standing problem in state-of-the-art climate models is the Tropical Atlantic (TA) warm sea surface temperature (SST) bias, which goes along with major biases in large-scale atmospheric circulation. Here we show that TA-sector climate changes forced by increasing atmospheric carbon dioxide (CO2) levels are sensitive to model resolution. Two versions of a climate model employing greatly varying atmospheric resolution and exhibiting very different warm bias strength are compared. The version with high atmospheric resolution features a small SST bias and simulates an eastward amplified SST warming over the equatorial Atlantic, in line with the observed SST trends since the mid-20th century. On the contrary, the version with coarse atmospheric resolution exhibits a large SST bias and projects relatively uniform SST changes across the equatorial Atlantic. In both model versions, the warming pattern resembles the pattern of interannual SST variability simulated under present-day conditions. Atmospheric changes also vastly differ among the two climate model versions. In the version with small SST bias, a deep atmospheric response is simulated with a major change in the Walker circulation and strongly enhanced rainfall over the equatorial region, whereas the atmospheric response is much weaker and of rather different character in the model with large SST bias. This study suggests that higher atmospheric resolution in climate models may enhance global warming projections over the TA sector.

scholarly journals Instantaneous Linkages between Clouds and Large-Scale Meteorology over the Southern Ocean in Observations and a Climate Model

2017 ◽  
Vol 30 (23) ◽  
pp. 9455-9474 ◽  
Author(s):  
Casey J. Wall ◽  
Dennis L. Hartmann ◽  
Po-Lun Ma
Keyword(s):  
Southern Ocean ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Austral Summer ◽  
Vertical Motion ◽  
Cloud Properties ◽  
Low Clouds ◽  
Community Atmosphere Model ◽  
Low Cloud

Instantaneous, coincident, footprint-level satellite observations of cloud properties and radiation taken during austral summer over the Southern Ocean are used to study relationships between clouds and large-scale meteorology. Cloud properties are very sensitive to the strength of vertical motion in the midtroposphere, and low-cloud properties are sensitive to estimated inversion strength, low-level temperature advection, and sea surface temperature. These relationships are quantified. An index for the meteorological anomalies associated with midlatitude cyclones is presented, and it is used to reveal the sensitivity of clouds to the meteorology within the warm and cold sectors of cyclones. The observed relationships between clouds and meteorology are compared to those in the Community Atmosphere Model, version 5 (CAM5), using satellite simulators. Low clouds simulated by CAM5 are too few, are too bright, and contain too much ice. In the cold sector of cyclones, the low clouds are also too sensitive to variations in the meteorology. When CAM5 is coupled with an updated boundary layer parameterization known as Cloud Layers Unified by Binormals (CLUBB), bias in the ice content of low clouds is dramatically reduced. More generally, this study demonstrates that examining the instantaneous time scale is a powerful approach to understanding the physical processes that control clouds and how they are represented in climate models. Such an evaluation goes beyond the cloud climatology and exposes model bias under various meteorological conditions.

scholarly journals Ventilation of the abyss in the Atlantic sector of the Southern Ocean

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Camille Hayatte Akhoudas ◽  
Jean-Baptiste Sallée ◽  
F. Alexander Haumann ◽  
Michael P. Meredith ◽  
Alberto Naveira Garabato ◽  
...  
Keyword(s):  
Southern Ocean ◽  
Bottom Water ◽  
Climate Models ◽  
Deep Ocean ◽  
Analytical Framework ◽  
Weddell Sea ◽  
Global Ocean ◽  
Shelf Water ◽  
Antarctic Bottom Water ◽  
Atlantic Sector

AbstractThe Atlantic sector of the Southern Ocean is the world’s main production site of Antarctic Bottom Water, a water-mass that is ventilated at the ocean surface before sinking and entraining older water-masses—ultimately replenishing the abyssal global ocean. In recent decades, numerous attempts at estimating the rates of ventilation and overturning of Antarctic Bottom Water in this region have led to a strikingly broad range of results, with water transport-based calculations (8.4–9.7 Sv) yielding larger rates than tracer-based estimates (3.7–4.9 Sv). Here, we reconcile these conflicting views by integrating transport- and tracer-based estimates within a common analytical framework, in which bottom water formation processes are explicitly quantified. We show that the layer of Antarctic Bottom Water denser than 28.36 kg m$$^{-3}$$ - 3 $$\gamma _{n}$$ γ n is exported northward at a rate of 8.4 ± 0.7 Sv, composed of 4.5 ± 0.3 Sv of well-ventilated Dense Shelf Water, and 3.9 ± 0.5 Sv of old Circumpolar Deep Water entrained into cascading plumes. The majority, but not all, of the Dense Shelf Water (3.4 ± 0.6 Sv) is generated on the continental shelves of the Weddell Sea. Only 55% of AABW exported from the region is well ventilated and thus draws down heat and carbon into the deep ocean. Our findings unify traditionally contrasting views of Antarctic Bottom Water production in the Atlantic sector, and define a baseline, process-discerning target for its realistic representation in climate models.

A new ensemble-based statistical methodology to verify changes in weather and climate models

2021 ◽  
Author(s):  
Christian Zeman ◽  
Christoph Schär
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Numerical Models ◽  
Hypothesis Test ◽  
Model Development ◽  
Atmospheric Model ◽  
Statistical Hypothesis ◽  
Model Parameters ◽  
Rounding Errors ◽  
Weather And Climate

<p>Since their first operational application in the 1950s, atmospheric numerical models have become essential tools in weather and climate prediction. As such, they are a constant subject to changes, thanks to advances in computer systems, numerical methods, and the ever increasing knowledge about the atmosphere of Earth. Many of the changes in today's models relate to seemingly unsuspicious modifications, associated with minor code rearrangements, changes in hardware infrastructure, or software upgrades. Such changes are meant to preserve the model formulation, yet the verification of such changes is challenged by the chaotic nature of our atmosphere - any small change, even rounding errors, can have a big impact on individual simulations. Overall this represents a serious challenge to a consistent model development and maintenance framework.</p><p>Here we propose a new methodology for quantifying and verifying the impacts of minor atmospheric model changes, or its underlying hardware/software system, by using ensemble simulations in combination with a statistical hypothesis test. The methodology can assess effects of model changes on almost any output variable over time, and can also be used with different hypothesis tests.</p><p>We present first applications of the methodology with the regional weather and climate model COSMO. The changes considered include a major system upgrade of the supercomputer used, the change from double to single precision floating-point representation, changes in the update frequency of the lateral boundary conditions, and tiny changes to selected model parameters. While providing very robust results, the methodology also shows a large sensitivity to more significant model changes, making it a good candidate for an automated tool to guarantee model consistency in the development cycle.</p>

Evaluating the Consistency between Statistically Downscaled and Global Dynamical Model Climate Change Projections

2008 ◽  
Vol 21 (22) ◽  
pp. 6052-6059 ◽  
Author(s):  
B. Timbal ◽  
P. Hope ◽  
S. Charles
Keyword(s):  
Global Warming ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Specific Humidity ◽  
Model Output ◽  
Winter Rainfall ◽  
Direct Model ◽  
The Impact

Abstract The consistency between rainfall projections obtained from direct climate model output and statistical downscaling is evaluated. Results are averaged across an area large enough to overcome the difference in spatial scale between these two types of projections and thus make the comparison meaningful. Undertaking the comparison using a suite of state-of-the-art coupled climate models for two forcing scenarios presents a unique opportunity to test whether statistical linkages established between large-scale predictors and local rainfall under current climate remain valid in future climatic conditions. The study focuses on the southwest corner of Western Australia, a region that has experienced recent winter rainfall declines and for which climate models project, with great consistency, further winter rainfall reductions due to global warming. Results show that as a first approximation the magnitude of the modeled rainfall decline in this region is linearly related to the model global warming (a reduction of about 9% per degree), thus linking future rainfall declines to future emission paths. Two statistical downscaling techniques are used to investigate the influence of the choice of technique on projection consistency. In addition, one of the techniques was assessed using different large-scale forcings, to investigate the impact of large-scale predictor selection. Downscaled and direct model projections are consistent across the large number of models and two scenarios considered; that is, there is no tendency for either to be biased; and only a small hint that large rainfall declines are reduced in downscaled projections. Among the two techniques, a nonhomogeneous hidden Markov model provides greater consistency with climate models than an analog approach. Differences were due to the choice of the optimal combination of predictors. Thus statistically downscaled projections require careful choice of large-scale predictors in order to be consistent with physically based rainfall projections. In particular it was noted that a relative humidity moisture predictor, rather than specific humidity, was needed for downscaled projections to be consistent with direct model output projections.

scholarly journals Influence of the representation of convection on the mid-Holocene West African Monsoon

2021 ◽  
Vol 17 (4) ◽  
pp. 1665-1684
Author(s):  
Leonore Jungandreas ◽  
Cathy Hohenegger ◽  
Martin Claussen
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Monsoon Season ◽  
West African Monsoon ◽  
West African ◽  
Global Climate Models ◽  
African Monsoon ◽  
Monsoonal Precipitation

Abstract. Global climate models experience difficulties in simulating the northward extension of the monsoonal precipitation over north Africa during the mid-Holocene as revealed by proxy data. A common feature of these models is that they usually operate on grids that are too coarse to explicitly resolve convection, but convection is the most essential mechanism leading to precipitation in the West African Monsoon region. Here, we investigate how the representation of tropical deep convection in the ICOsahedral Nonhydrostatic (ICON) climate model affects the meridional distribution of monsoonal precipitation during the mid-Holocene by comparing regional simulations of the summer monsoon season (July to September; JAS) with parameterized and explicitly resolved convection. In the explicitly resolved convection simulation, the more localized nature of precipitation and the absence of permanent light precipitation as compared to the parameterized convection simulation is closer to expectations. However, in the JAS mean, the parameterized convection simulation produces more precipitation and extends further north than the explicitly resolved convection simulation, especially between 12 and 17∘ N. The higher precipitation rates in the parameterized convection simulation are consistent with a stronger monsoonal circulation over land. Furthermore, the atmosphere in the parameterized convection simulation is less stably stratified and notably moister. The differences in atmospheric water vapor are the result of substantial differences in the probability distribution function of precipitation and its resulting interactions with the land surface. The parametrization of convection produces light and large-scale precipitation, keeping the soils moist and supporting the development of convection. In contrast, less frequent but locally intense precipitation events lead to high amounts of runoff in the explicitly resolved convection simulations. The stronger runoff inhibits the moistening of the soil during the monsoon season and limits the amount of water available to evaporation in the explicitly resolved convection simulation.

scholarly journals Improving climate model coupling through a complete mesh representation: a case study with E3SM (v1) and MOAB (v5.x)

2020 ◽  
Vol 13 (5) ◽  
pp. 2355-2377
Author(s):  
Vijay S. Mahadevan ◽  
Iulian Grindeanu ◽  
Robert Jacob ◽  
Jason Sarich
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Climate Modeling ◽  
System Modeling ◽  
Spectral Element ◽  
Scientific Workflow ◽  
Model Coupling ◽  
Earth System ◽  
Modeling Framework

Abstract. One of the fundamental factors contributing to the spatiotemporal inaccuracy in climate modeling is the mapping of solution field data between different discretizations and numerical grids used in the coupled component models. The typical climate computational workflow involves evaluation and serialization of the remapping weights during the preprocessing step, which is then consumed by the coupled driver infrastructure during simulation to compute field projections. Tools like Earth System Modeling Framework (ESMF) (Hill et al., 2004) and TempestRemap (Ullrich et al., 2013) offer capability to generate conservative remapping weights, while the Model Coupling Toolkit (MCT) (Larson et al., 2001) that is utilized in many production climate models exposes functionality to make use of the operators to solve the coupled problem. However, such multistep processes present several hurdles in terms of the scientific workflow and impede research productivity. In order to overcome these limitations, we present a fully integrated infrastructure based on the Mesh Oriented datABase (MOAB) (Tautges et al., 2004; Mahadevan et al., 2015) library, which allows for a complete description of the numerical grids and solution data used in each submodel. Through a scalable advancing-front intersection algorithm, the supermesh of the source and target grids are computed, which is then used to assemble the high-order, conservative, and monotonicity-preserving remapping weights between discretization specifications. The Fortran-compatible interfaces in MOAB are utilized to directly link the submodels in the Energy Exascale Earth System Model (E3SM) to enable online remapping strategies in order to simplify the coupled workflow process. We demonstrate the superior computational efficiency of the remapping algorithms in comparison with other state-of-the-science tools and present strong scaling results on large-scale machines for computing remapping weights between the spectral element atmosphere and finite volume discretizations on the polygonal ocean grids.

scholarly journals Regional Climate Model Emulator based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach

2021 ◽  
Author(s):  
Antoine Doury ◽  
Samuel Somot ◽  
Sébastien Gadat ◽  
Aurélien Ribes ◽  
Lola Corre
Keyword(s):  
Climate Change ◽  
Network Architecture ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
Near Surface ◽  
The Way

Abstract Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). The aim of this tool is to enlarge the size of high-resolution RCM simulation ensembles at low cost.We build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. Furthermore, the emulator relies on a neural network architecture, which grants computational efficiency. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM and in particular the way the RCM refines locally the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a huge computational benefit in running the emulator rather than the RCM, since training the emulator takes about 2 hours on GPU, and the prediction is nearly instantaneous. However, further work is needed to improve the way the RCM-emulator reproduces some of the temperature extremes, the intensity of climate change, and to extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

scholarly journals Southern Ocean Heat Uptake, Redistribution, and Storage in a Warming Climate: The Role of Meridional Overturning Circulation

2018 ◽  
Vol 31 (12) ◽  
pp. 4727-4743 ◽  
Author(s):  
Wei Liu ◽  
Jian Lu ◽  
Shang-Ping Xie ◽  
Alexey Fedorov
Keyword(s):  
Southern Ocean ◽  
Heat Transport ◽  
Climate Models ◽  
Climate Model ◽  
Meridional Overturning Circulation ◽  
The Other ◽  
Anthropogenic Heat ◽  
Overturning Circulation ◽  
Ocean Heat Uptake ◽  
Meridional Overturning

Climate models show that most of the anthropogenic heat resulting from increased atmospheric CO2 enters the Southern Ocean near 60°S and is stored around 45°S. This heat is transported to the ocean interior by the meridional overturning circulation (MOC) with wind changes playing an important role in the process. To isolate and quantify the latter effect, we apply an overriding technique to a climate model and decompose the total ocean response to CO2 increase into two major components: one due to wind changes and the other due to direct CO2 effect. We find that the poleward-intensified zonal surface winds tend to shift and strengthen the ocean Deacon cell and hence the residual MOC, leading to anomalous divergence of ocean meridional heat transport around 60°S coupled to a surface heat flux increase. In contrast, at 45°S we see anomalous convergence of ocean heat transport and heat loss at the surface. As a result, the wind-induced ocean heat storage (OHS) peaks at 46°S at a rate of 0.07 ZJ yr−1 (° lat)−1 (1 ZJ = 1021 J), contributing 20% to the total OHS maximum. The direct CO2 effect, on the other hand, very slightly alters the residual MOC but primarily warms the ocean. It induces a small but nonnegligible change in eddy heat transport and causes OHS to peak at 42°S at a rate of 0.30 ZJ yr−1 (° lat)−1, accounting for 80% of the OHS maximum. We also find that the eddy-induced MOC weakens, primarily caused by a buoyancy flux change as a result of the direct CO2 effect, and does not compensate the intensified Deacon cell.

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