Midlatitude Cloud Radiative Effect Sensitivity to Cloud Controlling Factors in Observations and Models: Relationship with Southern Hemisphere Jet Shifts and Climate Sensitivity

2021 ◽  
pp. 1-59
Author(s):  
Kevin M. Grise ◽  
Mitchell K. Kelleher
Keyword(s):  
Surface Temperature ◽  
Southern Hemisphere ◽  
Climate Sensitivity ◽  
Vertical Velocity ◽  
Large Scale ◽  
Climate Models ◽  
Controlling Factors ◽  
Cmip5 Models ◽  
Near Surface ◽  
Temperature Advection

AbstractAn effective method to understand cloud processes and to assess the fidelity with which they are represented in climate models is the cloud controlling factor framework, in which cloud properties are linked with variations in large-scale dynamical and thermodynamical variables. This study examines how midlatitude cloud radiative effects (CRE) over oceans co-vary with four cloud controlling factors: mid-tropospheric vertical velocity, estimated inversion strength (EIS), near-surface temperature advection, and sea surface temperature (SST), and assesses their representation in CMIP6 models with respect to observations and CMIP5 models.CMIP5 and CMIP6 models overestimate the sensitivity of midlatitude CRE to perturbations in vertical velocity, and underestimate the sensitivity of midlatitude shortwave CRE to perturbations in EIS and temperature advection. The largest improvement in CMIP6 models is a reduced sensitivity of CRE to vertical velocity perturbations. As in CMIP5 models, many CMIP6 models simulate a shortwave cloud radiative warming effect associated with a poleward shift in the Southern Hemisphere (SH) midlatitude jet stream, an effect not present in observations. This bias arises because most models’ shortwave CRE are too sensitive to vertical velocity perturbations and not sensitive enough to EIS perturbations, and because most models overestimate the SST anomalies associated with SH jet shifts. The presence of this bias directly impacts the transient surface temperature response to increasing greenhouse gases over the Southern Ocean, but not the global-mean surface temperature. Instead, the models’ climate sensitivity is correlated with their shortwave CRE sensitivity to surface temperature advection perturbations near 40°S, with models with more realistic values of temperature advection sensitivity generally having higher climate sensitivity.

Reexamining the Relationship between Climate Sensitivity and the Southern Hemisphere Radiation Budget in CMIP Models

2015 ◽  
Vol 28 (23) ◽  
pp. 9298-9312 ◽  
Author(s):  
Kevin M. Grise ◽  
Lorenzo M. Polvani ◽  
John T. Fasullo
Keyword(s):  
Southern Ocean ◽  
Southern Hemisphere ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Radiation Budget ◽  
Cmip5 Models ◽  
Net Radiation ◽  
Subtropical Clouds

Abstract Recent efforts to narrow the spread in equilibrium climate sensitivity (ECS) across global climate models have focused on identifying observationally based constraints, which are rooted in empirical correlations between ECS and biases in the models’ present-day climate. This study reexamines one such constraint identified from CMIP3 models: the linkage between ECS and net top-of-the-atmosphere radiation biases in the Southern Hemisphere (SH). As previously documented, the intermodel spread in the ECS of CMIP3 models is linked to present-day cloud and net radiation biases over the midlatitude Southern Ocean, where higher cloud fraction in the present-day climate is associated with larger values of ECS. However, in this study, no physical explanation is found to support this relationship. Furthermore, it is shown here that this relationship disappears in CMIP5 models and is unique to a subset of CMIP models characterized by unrealistically bright present-day clouds in the SH subtropics. In view of this evidence, Southern Ocean cloud and net radiation biases appear inappropriate for providing observationally based constraints on ECS. Instead of the Southern Ocean, this study points to the stratocumulus-to-cumulus transition regions of the SH subtropical oceans as key to explaining the intermodel spread in the ECS of both CMIP3 and CMIP5 models. In these regions, ECS is linked to present-day cloud and net radiation biases with a plausible physical mechanism: models with brighter subtropical clouds in the present-day climate show greater ECS because 1) subtropical clouds dissipate with increasing CO2 concentrations in many models and 2) the dissipation of brighter clouds contributes to greater solar warming of the surface.

Revisiting the relationship between dynamical sensitivity and climate sensitivity in the Southern hemisphere

2020 ◽  
Author(s):  
Thomas Wood ◽  
Amanda Maycock ◽  
Christine McKenna ◽  
Andreas Chrysanthou ◽  
John Fyfe ◽  
...  
Keyword(s):  
Southern Hemisphere ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Stratospheric Ozone ◽  
Storm Track ◽  
Temperature Gradients ◽  
Cmip5 Models ◽  
Positive Trend ◽  
Equilibrium Climate Sensitivity ◽  
The Relationship

<p>The Southern Annular Mode (SAM) is the dominant mode of midlatitude atmospheric circulation variability in the Southern hemisphere. In the future, the SAM trend is expected to be the net result of opposing effects from increasing greenhouse gases (GHG) and ozone recovery. Different greenhouse gas scenarios, which induce different rates of surface and atmospheric temperature change, are therefore associated with different future SAM trends (Barnes et al., 2014). Since the magnitude of warming due to GHGs is an important component of this response, one might expect to find a relationship between equilibrium climate sensitivity (ECS) and future Southern hemisphere circulation trends. In CMIP5, the relationship between the SAM and the level of tropospheric warming across models was found to be strongest in the summer and autumn and could explain around 20% of the intermodel spread (Grise and Polvani, 2014). The spread is more strongly correlated with differences in meridional temperature gradients (Harvey et al., 2014).</p><p>Many of the latest CMIP6 models show a larger equilibrium climate sensitivity (ECS) of up to ~5.5 K (Forster et al., 2019) compared to a maximum of ~4.7 K in CMIP5. This raises the important question of how a higher level of warming affects projections of the SH midlatitude circulation. In this study, we examine the response of the SAM in CMIP6 models and quantify its relationship to ECS and temperature gradients. Our starting hypothesis is that stronger surface warming will induce a larger increase in tropical free tropospheric temperatures, and hence all being equal, a larger tropics-to-pole temperature gradient and a more positive SAM trend. However, results show that despite the higher level of warming in the CMIP6 models, there is a smaller positive trend in SAM index than in CMIP5 indicating a different relationship between warming and midlatitude circulation trends in CMIP6. We attempt to explain potential reasons for these differences.</p><p><strong>References:</strong></p><p>Barnes, E.A., N.W. Barnes, and L.M. Polvani, 2014: Delayed Southern Hemisphere Climate Change Induced by Stratospheric Ozone Recovery, as Projected by the CMIP5 Models. J. Climate, 27, 852–867, https://doi.org/10.1175/JCLI-D-13-00246.1</p><p>Forster, P.M., Maycock, A.C., McKenna, C.M. et al. (2019), Latest climate models confirm need for urgent mitigation. Nat. Clim. Chang. (2019) doi:10.1038/s41558-019-0660-0</p><p>Grise, K. M., and Polvani, L. M. (2014), Is climate sensitivity related to dynamical sensitivity? A Southern Hemisphere perspective, Geophys. Res. Lett., 41, 534– 540, doi:10.1002/2013GL058466.</p><p>Harvey, B.J., Shaffrey, L.C. & Woollings, T.J. (2014) Equator­-to-­pole temperature differences and the extra­tropical storm track responses of the CMIP5 climate models, Clim Dyn, 43: 1171. https://doi.org/10.1007/s00382-013-1883-9</p>

Sixty Years of Widespread Warming in the Southern Middle and High Latitudes (1957–2016)

2019 ◽  
Vol 32 (20) ◽  
pp. 6875-6898 ◽  
Author(s):  
Megan E. Jones ◽  
David H. Bromwich ◽  
Julien P. Nicolas ◽  
Jorge Carrasco ◽  
Eva Plavcová ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Southern Hemisphere ◽  
Climate Models ◽  
Coupled Model ◽  
Southern Annular Mode ◽  
Cmip5 Models ◽  
High Latitudes ◽  
West Antarctica ◽  
Temporal Perspective ◽  
Temperature Trends

Abstract Temperature trends across Antarctica over the last few decades reveal strong and statistically significant warming in West Antarctica and the Antarctic Peninsula (AP) contrasting with no significant change overall in East Antarctica. However, recent studies have documented cooling in the AP since the late 1990s. This study aims to place temperature changes in the AP and West Antarctica into a larger spatial and temporal perspective by analyzing monthly station-based surface temperature observations since 1957 across the extratropical Southern Hemisphere, along with sea surface temperature (SST) data and mean sea level pressure reanalysis data. The results confirm statistically significant cooling in station observations and SST trends throughout the AP region since 1999. However, the full 60-yr period shows statistically significant, widespread warming across most of the Southern Hemisphere middle and high latitudes. Positive SST trends broadly reflect these warming trends, especially in the midlatitudes. After confirming the importance of the southern annular mode (SAM) on southern high-latitude climate variability, the influence is removed from the station temperature records, revealing statistically significant background warming across all of the extratropical Southern Hemisphere. Antarctic temperature trends in a suite of climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are then investigated. Consistent with previous work the CMIP5 models warm Antarctica at the background temperature rate that is 2 times faster than that observed. However, removing the SAM influence from both CMIP5 and observed temperatures results in Antarctic trends that differ only modestly, perhaps due to natural multidecadal variability remaining in the observations.

scholarly journals Different response of surface temperature and air temperature to deforestation in climate models

2019 ◽  
Vol 10 (3) ◽  
pp. 473-484 ◽  
Author(s):  
Johannes Winckler ◽  
Christian H. Reick ◽  
Sebastiaan Luyssaert ◽  
Alessandro Cescatti ◽  
Paul C. Stoy ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Large Scale ◽  
Model Comparison ◽  
Climate Models ◽  
Surface Air Temperature ◽  
Nonlocal Effects ◽  
Local Effects ◽  
Near Surface ◽  
Temperature Changes

Abstract. When quantifying temperature changes induced by deforestation (e.g., cooling in high latitudes, warming in low latitudes), satellite data, in situ observations, and climate models differ concerning the height at which the temperature is typically measured/simulated. In this study the effects of deforestation on surface temperature, near-surface air temperature, and lower atmospheric temperature are compared by analyzing the biogeophysical temperature effects of large-scale deforestation in the Max Planck Institute Earth System Model (MPI-ESM) separately for local effects (which are only apparent at the location of deforestation) and nonlocal effects (which are also apparent elsewhere). While the nonlocal effects (cooling in most regions) influence the temperature of the surface and lowest atmospheric layer equally, the local effects (warming in the tropics but a cooling in the higher latitudes) mainly affect the temperature of the surface. In agreement with observation-based studies, the local effects on surface and near-surface air temperature respond differently in the MPI-ESM, both concerning the magnitude of local temperature changes and the latitude at which the local deforestation effects turn from a cooling to a warming (at 45–55∘ N for surface temperature and around 35∘ N for near-surface air temperature). Subsequently, our single-model results are compared to model data from multiple climate models from the Climate Model Intercomparison Project (CMIP5). This inter-model comparison shows that in the northern midlatitudes, both concerning the summer warming and winter cooling, near-surface air temperature is affected by the local effects only about half as strongly as surface temperature. This study shows that the choice of temperature variable has a considerable effect on the observed and simulated temperature change. Studies about the biogeophysical effects of deforestation must carefully choose which temperature to consider.

scholarly journals Cities Exacerbate Climate Warming

Urban Science ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 27
Author(s):  
Lahouari Bounoua ◽  
Kurtis Thome ◽  
Joseph Nigro
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Warming Trend ◽  
Climate Mitigation ◽  
Temperature Changes ◽  
Surface Warming ◽  
The Us

Urbanization is a complex land transformation not explicitly resolved within large-scale climate models. Long-term timeseries of high-resolution satellite data are essential to characterize urbanization within land surface models and to assess its contribution to surface temperature changes. The potential for additional surface warming from urbanization-induced land use change is investigated and decoupled from that due to change in climate over the continental US using a decadal timescale. We show that, aggregated over the US, the summer mean urban-induced surface temperature increased by 0.15 °C, with a warming of 0.24 °C in cities built in vegetated areas and a cooling of 0.25 °C in cities built in non-vegetated arid areas. This temperature change is comparable in magnitude to the 0.13 °C/decade global warming trend observed over the last 50 years caused by increased CO2. We also show that the effect of urban-induced change on surface temperature is felt above and beyond that of the CO2 effect. Our results suggest that climate mitigation policies must consider urbanization feedback to put a limit on the worldwide mean temperature increase.

scholarly journals Relationship between Shortwave Cloud Radiative Forcing and Local Meteorological Variables Compared in Observations and Several Global Climate Models

2006 ◽  
Vol 19 (17) ◽  
pp. 4344-4359 ◽  
Author(s):  
Markus Stowasser ◽  
Kevin Hamilton
Keyword(s):  
Relative Humidity ◽  
Vertical Velocity ◽  
Radiative Forcing ◽  
Large Scale ◽  
Climate Models ◽  
Grid Point ◽  
Global Climate Models ◽  
Coupled Models ◽  
Cloud Radiative Forcing ◽  
Cloud Forcing

Abstract The relations between local monthly mean shortwave cloud radiative forcing and aspects of the resolved-scale meteorological fields are investigated in hindcast simulations performed with 12 of the global coupled models included in the model intercomparison conducted as part of the preparation for Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). In particular, the connection of the cloud forcing over tropical and subtropical ocean areas with resolved midtropospheric vertical velocity and with lower-level relative humidity are investigated and compared among the models. The model results are also compared with observational determinations of the same relationships using satellite data for the cloud forcing and global reanalysis products for the vertical velocity and humidity fields. In the analysis the geographical variability in the long-term mean among all grid points and the interannual variability of the monthly mean at each grid point are considered separately. The shortwave cloud radiative feedback (SWCRF) plays a crucial role in determining the predicted response to large-scale climate forcing (such as from increased greenhouse gas concentrations), and it is thus important to test how the cloud representations in current climate models respond to unforced variability. Overall there is considerable variation among the results for the various models, and all models show some substantial differences from the comparable observed results. The most notable deficiency is a weak representation of the cloud radiative response to variations in vertical velocity in cases of strong ascending or strong descending motions. While the models generally perform better in regimes with only modest upward or downward motions, even in these regimes there is considerable variation among the models in the dependence of SWCRF on vertical velocity. The largest differences between models and observations when SWCRF values are stratified by relative humidity are found in either very moist or very dry regimes. Thus, the largest errors in the model simulations of cloud forcing are prone to be in the western Pacific warm pool area, which is characterized by very moist strong upward currents, and in the rather dry regions where the flow is dominated by descending mean motions.

Evaluating temperature extremes in CMIP6 simulations using statistically proper evaluation methods

2021 ◽  
Author(s):  
Thordis Thorarinsdottir ◽  
Jana Sillmann ◽  
Marion Haugen ◽  
Nadine Gissibl ◽  
Marit Sandstad
Keyword(s):  
Air Temperature ◽  
Model Evaluation ◽  
Climate Models ◽  
Climate Model ◽  
Mean Squared Error ◽  
Surface Air Temperature ◽  
Cmip5 Models ◽  
Model Output ◽  
Intergovernmental Panel ◽  
Near Surface

<p>Reliable projections of extremes in near-surface air temperature (SAT) by climate models become more and more important as global warming is leading to significant increases in the hottest days and decreases in coldest nights around the world with considerable impacts on various sectors, such as agriculture, health and tourism.</p><p>Climate model evaluation has traditionally been performed by comparing summary statistics that are derived from simulated model output and corresponding observed quantities using, for instance, the root mean squared error (RMSE) or mean bias as also used in the model evaluation chapter of the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Both RMSE and mean bias compare averages over time and/or space, ignoring the variability, or the uncertainty, in the underlying values. Particularly when interested in the evaluation of climate extremes, climate models should be evaluated by comparing the probability distribution of model output to the corresponding distribution of observed data.</p><p>To address this shortcoming, we use the integrated quadratic distance (IQD) to compare distributions of simulated indices to the corresponding distributions from a data product. The IQD is the proper divergence associated with the proper continuous ranked probability score (CRPS) as it fulfills essential decision-theoretic properties for ranking competing models and testing equality in performance, while also assessing the full distribution.</p><p>The IQD is applied to evaluate CMIP5 and CMIP6 simulations of monthly maximum (TXx) and minimum near-surface air temperature (TNn) over the data-dense regions Europe and North America against both observational and reanalysis datasets. There is not a notable difference between the model generations CMIP5 and CMIP6 when the model simulations are compared against the observational dataset HadEX2. However, the CMIP6 models show a better agreement with the reanalysis ERA5 than CMIP5 models, with a few exceptions. Overall, the climate models show higher skill when compared against ERA5 than when compared against HadEX2. While the model rankings vary with region, season and index, the model evaluation is robust against changes in the grid resolution considered in the analysis.</p>

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 Atmospheric Dynamics Feedback: Concept, Simulations, and Climate Implications

2018 ◽  
Vol 31 (8) ◽  
pp. 3249-3264 ◽  
Author(s):  
Michael P. Byrne ◽  
Tapio Schneider
Keyword(s):  
Climate Change ◽  
Atmospheric Circulation ◽  
Radiative Forcing ◽  
Large Scale ◽  
Climate Models ◽  
Atmospheric Dynamics ◽  
Future Climate Change ◽  
Coupled Models ◽  
Near Surface ◽  
Circulation Changes

AbstractThe regional climate response to radiative forcing is largely controlled by changes in the atmospheric circulation. It has been suggested that global climate sensitivity also depends on the circulation response, an effect called the “atmospheric dynamics feedback.” Using a technique to isolate the influence of changes in atmospheric circulation on top-of-the-atmosphere radiation, the authors calculate the atmospheric dynamics feedback in coupled climate models. Large-scale circulation changes contribute substantially to all-sky and cloud feedbacks in the tropics but are relatively less important at higher latitudes. Globally averaged, the atmospheric dynamics feedback is positive and amplifies the near-surface temperature response to climate change by an average of 8% in simulations with coupled models. A constraint related to the atmospheric mass budget results in the dynamics feedback being small on large scales relative to feedbacks associated with thermodynamic processes. Idealized-forcing simulations suggest that circulation changes at high latitudes are potentially more effective at influencing global temperature than circulation changes at low latitudes, and the implications for past and future climate change are discussed.

Southern Hemisphere Continental Temperature Responses to Major Volcanic Eruptions Since 1883 in CMIP5 Models

2021 ◽  
Author(s):  
Pamela J Harvey ◽  
Stefan W Grab
Keyword(s):  
Southern Hemisphere ◽  
Temperature Response ◽  
Volcanic Eruptions ◽  
Lag Time ◽  
Cmip5 Models ◽  
Seasonal Temperature ◽  
Near Surface ◽  
Santa Maria ◽  
Temperature Responses ◽  
Austral Autumn

Abstract Although global and Northern Hemisphere (NH) temperature responses to volcanic forcing have been extensively investigated, knowledge of such responses over Southern Hemisphere (SH) continental regions is still limited. Here we use an ensemble of CMIP5 models to explore SH temperature responses to four major volcanic eruptions: Krakatau (1883), Santa Maria (1902), Agung (1963) and Pinatubo (1991). Focus is on near-surface temperature responses over southern continental landmasses including southern South America (SSA), southern Africa (SAF) and Australia and their seasonal differences. Findings indicate that for all continents, temperature responses were strongest and lasted longest following the Krakatau eruption. Responses in Australia had the shortest lag time, strongest maximum seasonal response, as well as the most significant monthly anomalies. In contrast, SSA records the longest lag time, weakest maximum seasonal temperature response, and lowest number of monthly negative anomalies following these eruptions. In most cases, the strongest single-season response occurred in austral autumn or winter, and the weakest in summer or spring. We tentatively propose that cooler temperature responses are likely caused, at least in part, by the intensification of the westerlies and associated mid-latitude cyclones and anti-cyclones.

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