scholarly journals Large Contribution of Supercooled Liquid Clouds to the Solar Radiation Budget of the Southern Ocean

2016 ◽  
Vol 29 (11) ◽  
pp. 4213-4228 ◽  
Author(s):  
A. Bodas-Salcedo ◽  
P. G. Hill ◽  
K. Furtado ◽  
K. D. Williams ◽  
P. R. Field ◽  
...  

Abstract The Southern Ocean is a critical region for global climate, yet large cloud and solar radiation biases over the Southern Ocean are a long-standing problem in climate models and are poorly understood, leading to biases in simulated sea surface temperatures. This study shows that supercooled liquid clouds are central to understanding and simulating the Southern Ocean environment. A combination of satellite observational data and detailed radiative transfer calculations is used to quantify the impact of cloud phase and cloud vertical structure on the reflected solar radiation in the Southern Hemisphere summer. It is found that clouds with supercooled liquid tops dominate the population of liquid clouds. The observations show that clouds with supercooled liquid tops contribute between 27% and 38% to the total reflected solar radiation between 40° and 70°S, and climate models are found to poorly simulate these clouds. The results quantify the importance of supercooled liquid clouds in the Southern Ocean environment and highlight the need to improve understanding of the physical processes that control these clouds in order to improve their simulation in numerical models. This is not only important for improving the simulation of present-day climate and climate variability, but also relevant for increasing confidence in climate feedback processes and future climate projections.

2014 ◽  
Vol 27 (1) ◽  
pp. 41-56 ◽  
Author(s):  
A. Bodas-Salcedo ◽  
K. D. Williams ◽  
M. A. Ringer ◽  
I. Beau ◽  
J. N. S. Cole ◽  
...  

Abstract Current climate models generally reflect too little solar radiation over the Southern Ocean, which may be the leading cause of the prevalent sea surface temperature biases in climate models. The authors study the role of clouds on the radiation biases in atmosphere-only simulations of the Cloud Feedback Model Intercomparison Project phase 2 (CFMIP2), as clouds have a leading role in controlling the solar radiation absorbed at those latitudes. The authors composite daily data around cyclone centers in the latitude band between 40° and 70°S during the summer. They use cloud property estimates from satellite to classify clouds into different regimes, which allow them to relate the cloud regimes and their associated radiative biases to the meteorological conditions in which they occur. The cloud regimes are defined using cloud properties retrieved using passive sensors and may suffer from the errors associated with this type of retrievals. The authors use information from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar to investigate in more detail the properties of the “midlevel” cloud regime. Most of the model biases occur in the cold-air side of the cyclone composite, and the cyclone composite accounts for most of the climatological error in that latitudinal band. The midlevel regime is the main contributor to reflected shortwave radiation biases. CALIPSO data show that the midlevel cloud regime is dominated by two main cloud types: cloud with tops actually at midlevel and low-level cloud. Improving the simulation of these cloud types should help reduce the biases in the simulation of the solar radiation budget in the Southern Ocean in climate models.


2021 ◽  
Vol 12 (2) ◽  
pp. 709-723
Author(s):  
Philip Goodwin ◽  
B. B. Cael

Abstract. Future climate change projections, impacts, and mitigation targets are directly affected by how sensitive Earth's global mean surface temperature is to anthropogenic forcing, expressed via the climate sensitivity (S) and transient climate response (TCR). However, the S and TCR are poorly constrained, in part because historic observations and future climate projections consider the climate system under different response timescales with potentially different climate feedback strengths. Here, we evaluate S and TCR by using historic observations of surface warming, available since the mid-19th century, and ocean heat uptake, available since the mid-20th century, to constrain a model with independent climate feedback components acting over multiple response timescales. Adopting a Bayesian approach, our prior uses a constrained distribution for the instantaneous Planck feedback combined with wide-ranging uniform distributions of the strengths of the fast feedbacks (acting over several days) and multi-decadal feedbacks. We extract posterior distributions by applying likelihood functions derived from different combinations of observational datasets. The resulting TCR distributions when using two preferred combinations of historic datasets both find a TCR of 1.5 (1.3 to 1.8 at 5–95 % range) ∘C. We find the posterior probability distribution for S for our preferred dataset combination evolves from S of 2.0 (1.6 to 2.5) ∘C on a 20-year response timescale to S of 2.3 (1.4 to 6.4) ∘C on a 140-year response timescale, due to the impact of multi-decadal feedbacks. Our results demonstrate how multi-decadal feedbacks allow a significantly higher upper bound on S than historic observations are otherwise consistent with.


2020 ◽  
Vol 14 (8) ◽  
pp. 2673-2686 ◽  
Author(s):  
Ramdane Alkama ◽  
Patrick C. Taylor ◽  
Lorea Garcia-San Martin ◽  
Herve Douville ◽  
Gregory Duveiller ◽  
...  

Abstract. Clouds play an important role in the climate system: (1) cooling Earth by reflecting incoming sunlight to space and (2) warming Earth by reducing thermal energy loss to space. Cloud radiative effects are especially important in polar regions and have the potential to significantly alter the impact of sea ice decline on the surface radiation budget. Using CERES (Clouds and the Earth's Radiant Energy System) data and 32 CMIP5 (Coupled Model Intercomparison Project) climate models, we quantify the influence of polar clouds on the radiative impact of polar sea ice variability. Our results show that the cloud short-wave cooling effect strongly influences the impact of sea ice variability on the surface radiation budget and does so in a counter-intuitive manner over the polar seas: years with less sea ice and a larger net surface radiative flux show a more negative cloud radiative effect. Our results indicate that 66±2% of this change in the net cloud radiative effect is due to the reduction in surface albedo and that the remaining 34±1 % is due to an increase in cloud cover and optical thickness. The overall cloud radiative damping effect is 56±2 % over the Antarctic and 47±3 % over the Arctic. Thus, present-day cloud properties significantly reduce the net radiative impact of sea ice loss on the Arctic and Antarctic surface radiation budgets. As a result, climate models must accurately represent present-day polar cloud properties in order to capture the surface radiation budget impact of polar sea ice loss and thus the surface albedo feedback.


2021 ◽  
Vol 34 (2) ◽  
pp. 755-772
Author(s):  
Patrik L. Pfister ◽  
Thomas F. Stocker

AbstractThe global-mean climate feedback quantifies how much the climate system will warm in response to a forcing such as increased CO2 concentration. Under a constant forcing, this feedback becomes less negative (increasing) over time in comprehensive climate models, which has been attributed to increases in cloud and lapse-rate feedbacks. However, out of eight Earth system models of intermediate complexity (EMICs) not featuring interactive clouds, two also simulate such a feedback increase: Bern3D-LPX and LOVECLIM. Using these two models, we investigate the causes of the global-mean feedback increase in the absence of cloud feedbacks. In both models, the increase is predominantly driven by processes in the Southern Ocean region. In LOVECLIM, the global-mean increase is mainly due to a local longwave feedback increase in that region, which can be attributed to lapse-rate changes. It is enhanced by the slow atmospheric warming above the Southern Ocean, which is delayed due to regional ocean heat uptake. In Bern3D-LPX, this delayed regional warming is the main driver of the global-mean feedback increase. It acts on a near-constant local feedback pattern mainly determined by the sea ice–albedo feedback. The global-mean feedback increase is limited by the availability of sea ice: faster Southern Ocean sea ice melting due to either stronger forcing or higher equilibrium climate sensitivity (ECS) reduces the increase of the global mean feedback in Bern3D-LPX. In the highest-ECS simulation with 4 × CO2 forcing, the feedback even becomes more negative (decreasing) over time. This reduced ice–albedo feedback due to sea ice depletion is a plausible mechanism for a decreasing feedback also in high-forcing simulations of other models.


2021 ◽  
Author(s):  
Blanka Bartok

<p>As solar energy share is showing a significant growth in the European electricity generation system, assessments regarding long-term variation of this variable related to climate change are becoming more and more relevant for this sector. Several studies analysed the impact of climate change on the solar energy sector in Europe (Jerez et al, 2015) finding light impact (-14%; +2%) in terms of mean surface solar radiation. The present study focuses on extreme values, namely on the distribution of low surface solar radiation (overcast situation) and high surface solar radiation (clear sky situation), since the frequencies of these situations have high impact on electricity generation.</p><p>The study considers 11 high-resolution (0.11 deg) bias-corrected climate projections from the EURO-CORDEX ensemble with 5 Global Climate Models (GCMs) downscaled by 6 Regional Climate Models (RCMs).</p><p>Changes in extreme surface solar radiation frequencies show different regional patterns over Europe.</p><p>The study also includes a case study determining the changes in solar power generation induced by the extreme situations.</p><p> </p><p> </p><p>Jerez et al (2015): The impact of climate change on photovoltaic power generation in Europe, Nature Communications 6(1):10014, 10.1038/ncomms10014</p><p> </p>


Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2266 ◽  
Author(s):  
Enrique Soriano ◽  
Luis Mediero ◽  
Carlos Garijo

Climate projections provided by EURO-CORDEX predict changes in annual maximum series of daily rainfall in the future in some areas of Spain because of climate change. Precipitation and temperature projections supplied by climate models do not usually fit exactly the statistical properties of the observed time series in the control period. Bias correction methods are used to reduce such errors. This paper seeks to find the most adequate bias correction techniques for temperature and precipitation projections that minimizes the errors between observations and climate model simulations in the control period. Errors in flood quantiles are considered to identify the best bias correction techniques, as flood quantiles are used for hydraulic infrastructure design and safety assessment. In addition, this study aims to understand how the expected changes in precipitation extremes and temperature will affect the catchment response in flood events in the future. Hydrological modelling is required to characterize rainfall-runoff processes adequately in a changing climate, in order to estimate flood changes expected in the future. Four catchments located in the central-western part of Spain have been selected as case studies. The HBV hydrological model has been calibrated in the four catchments by using the observed precipitation, temperature and streamflow data available on a daily scale. Rainfall has been identified as the most significant input to the model, in terms of its influence on flood response. The quantile mapping polynomial correction has been found to be the best bias correction method for precipitation. A general reduction in flood quantiles is expected in the future, smoothing the increases identified in precipitation quantiles by the reduction of soil moisture content in catchments, due to the expected increase in temperature and decrease in mean annual precipitations.


2005 ◽  
Vol 5 (3) ◽  
pp. 755-765 ◽  
Author(s):  
S. Ghosh ◽  
S. Osborne ◽  
M. H. Smith

Abstract. Owing to their extensive spatial coverage, stratocumulus clouds play a crucial role in the radiation budget of the earth. Climate models need an accurate characterisation of stratocumulus in order to provide an accurate forecast. However, remote sensing as well as in-situ observations reveal that on several occasions, cumulus clouds present below the stratocumulus, often have a significant impact on the main stratocumulus microphysical properties. This was observed during the ACE-2 (Aerosol Characterisation Experiment-2) campaign designed to study the impact of polluted continental air on stratocumulus formation. In this paper we used a detailed micro-physical chemical parcel model to quantify the extent of this cumulus-stratocumuls coupling. In addition, we made extensive use of microphysical observations from the C-130 aircraft that was operated during ACE-2. For the ACE-2 case studies considered in this paper, our analysis revealed that the chemical, microphysical and optical characteristics of the main stratocumulus cloud deck had significant contributions from cumulus clouds that often penetrated the stratocumulus deck. The amount of fine mode ionic species, the average droplet number concentrations, the effective radii and the optical depths during the flight A562 (when cumulus clouds interacted with the main stratocumulus) were estimated and model runs that included this effect yielded microphysical and optical properties which compared more favourably with the observations than the runs which did not. This study highlights the importance of including these cumulus effects in stratocumulus related modelling studies.


2016 ◽  
Vol 8 (1) ◽  
pp. 142-164 ◽  
Author(s):  
Philbert Luhunga ◽  
Ladslaus Chang'a ◽  
George Djolov

The IPCC (Intergovernmental Panel on Climate Change) assessment reports confirm that climate change will hit developing countries the hardest. Adaption is on the agenda of many countries around the world. However, before devising adaption strategies, it is crucial to assess and understand the impacts of climate change at regional and local scales. In this study, the impact of climate change on rain-fed maize (Zea mays) production in the Wami-Ruvu basin of Tanzania was evaluated using the Decision Support System for Agro-technological Transfer. The model was fed with daily minimum and maximum temperatures, rainfall and solar radiation for current climate conditions (1971–2000) as well as future climate projections (2010–2099) for two Representative Concentration Pathways: RCP 4.5 and RCP 8.5. These data were derived from three high-resolution regional climate models, used in the Coordinated Regional Climate Downscaling Experiment program. Results showed that due to climate change future maize yields over the Wami-Ruvu basin will slightly increase relative to the baseline during the current century under RCP 4.5 and RCP 8.5. However, maize yields will decline in the mid and end centuries. The spatial distribution showed that high decline in maize yields are projected over lower altitude regions due to projected increase in temperatures in those areas.


2015 ◽  
Vol 28 (23) ◽  
pp. 9298-9312 ◽  
Author(s):  
Kevin M. Grise ◽  
Lorenzo M. Polvani ◽  
John T. Fasullo

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.


2017 ◽  
Vol 9 (1) ◽  
pp. 207-222 ◽  
Author(s):  
Philbert Luhunga

AbstractIn this study, the impact of inter-seasonal climate variability on rainfed maize (Zea mays) production over the Wami-Ruvu basin of Tanzania is evaluated. Daily high-resolution climate simulations from the Coordinated Regional Climate Downscaling Experiment_Regional Climate Models (CORDEX_RCMs) are used to drive the Decision Support System for Agro-technological Transfer (DSSAT) to simulate maize yields. Climate simulations for the base period of 35 years (1971–2005) are used to drive DSSAT to simulate maize yields during the historical climate. On the other hand, climate projections for the period 2010–2039 (current), 2040–2069 (mid), and 2070–2099 centuries for two Representative Concentration Pathway (RCP45 and 85) emission scenarios are used to drive DSSAT to simulate maize yields in respective centuries. Statistical approaches based on Pearson correlation coefficient and the coefficients of determination are used in the analysis. Results show that rainfall, maximum temperature, and solar radiation are the most important climate variables that determine variation in rainfed maize yields over the Wami-Ruvu basin of Tanzania. They explain the variability in maize yields in historical climate condition (1971–2005), present century under RCP 4.5, and mid and end centuries under both RCP 4.5 and RCP 8.5.


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