scholarly journals Internal variability of all-sky and clear-sky surface solarradiation on decadal timescales

2021 ◽  
Author(s):  
Boriana Chtirkova ◽  
Doris Folini ◽  
Lucas Ferreira Correa ◽  
Martin Wild
Keyword(s):  
Internal Variability ◽  
Clear Sky

Internal variability and unforced trends of all-sky and clear-sky SSR: quantitative estimates using CMIP6

2021 ◽  
Author(s):  
Boriana Chtirkova ◽  
Doris Folini ◽  
Lucas Ferreira Correa ◽  
Martin Wild
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Grid Cell ◽  
Internal Variability ◽  
Global Climate Models ◽  
Geographical Region ◽  
Positive Trend ◽  
Surface Solar Radiation ◽  
Anthropogenic Effect ◽  
Clear Sky

<p>Quantifying trends in surface solar radiation (SSR) of unforced simulations is of substantial importance when one tries to quantify the anthropogenic effect in forced trends, as the net effect may be dampened or amplified by the internal variability of the system. In our analysis, we consider trends on different temporal scales (10, 30, 50 and 100 years) from 58 global climate models, participating in the Coupled Model Intercomparison Project - Phase 6 (CMIP6). We calculate the trends at the grid-box level for all-sky and clear-sky SSR using annual mean data of the multi-century pre-industrial control (piControl) experiments. The trends from both variables are found to depend strongly on the geographical region, as the most pronounced trends of the all-sky variable are observed in the Tropical Pacific, while the largest clear-sky trends are found in the large desert regions. Inspecting for each grid cell the statistical distribution of occurring N-year trends  shows that they are normally distributed in the majority of grid cells for both all-sky and clear-sky SSR. The 75-th percentile taken from these distributions (i.e. a positive trend with a 25 % chance of occurrence) varies with geographical region, taking values in the ranges 0.79 - 12.03 Wm<sup>-2</sup>/decade for 10-year trends, 0.15 - 2.05 Wm<sup>-2</sup>/decade for 30-year trends, 0.07 - 0.92 Wm<sup>-2</sup>/decade for 50-year trends and 0.02 - 0.29 Wm<sup>-2</sup>/decade for 100-year trends for all-sky SSR. The unforced trends become less significant on longer timescales – the trend medians, corresponding to the above ranges, are 3.18 Wm<sup>-2</sup>/decade, 0.62 Wm<sup>-2</sup>/decade, 0.29 Wm<sup>-2</sup>/decade, 0.10 Wm<sup>-2</sup>/decade respectively. The trends for clear-sky SSR are found to differ from the all-sky SSR by a factor of 0.16 on average, independent of the trend length. The model spread becomes greater at longer trend timescales, the differences being more substantial between large model families rather than between individual models. To elucidate the dominant causes of variability in different regions, we examine the correlations of the SSR variables with ambient aerosol optical thickness at 550 nm, atmosphere mass content of water vapour, cloud area fraction and albedo.</p>

Uncertainty in Forced and Natural Arctic Solar Absorption Variations in CMIP6 Models

2021 ◽  
Vol 34 (3) ◽  
pp. 931-948
Author(s):  
Anne Sledd ◽  
Tristan L’Ecuyer
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Internal Variability ◽  
Global Climate Models ◽  
Shortwave Radiation ◽  
The Arctic ◽  
Solar Absorption ◽  
Clear Sky

AbstractThe Arctic is rapidly changing, with increasingly dramatic sea ice loss and surface warming in recent decades. Shortwave radiation plays a key role in Arctic warming during summer months, and absorbed shortwave radiation has been increasing largely because of greater sea ice loss. Clouds can influence this ice–albedo feedback by modulating the amount of shortwave radiation incident on the Arctic Ocean. In turn, clouds impact the amount of time that must elapse before forced trends in Arctic shortwave absorption emerge from internal variability. This study determines whether the forced climate response of absorbed shortwave radiation in the Arctic has emerged in the modern satellite record and global climate models. From 18 years of satellite observations from CERES-EBAF, we find that recent declines in sea ice are large enough to produce a statistically significant trend (1.7 × 106 PJ or 3.9% per decade) in observed clear-sky absorbed shortwave radiation. However, clouds preclude any forced trends in all-sky absorption from emerging within the existing satellite record. Across 18 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6), the predicted time to emergence of absorbed shortwave radiation trends varies from 8 to 39 and from 8 to 35 years for all-sky and clear-sky conditions, respectively, across two future scenarios. Furthermore, most models fail to reproduce the observed cloud delaying effect because of differences in internal variability. Contrary to observations, one-third of models suggest that clouds may reduce the time to emergence of absorbed shortwave trends relative to clear skies, an artifact that may be the result of inaccurate representations of cloud feedbacks.

Evaluation of net shortwave radiation over China with a regional climate model

2020 ◽  
Vol 80 (2) ◽  
pp. 147-163
Author(s):  
X Liu ◽  
Y Kang ◽  
Q Liu ◽  
Z Guo ◽  
Y Chen ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Radiant Energy ◽  
Energy System ◽  
Radiation Budget ◽  
Shortwave Radiation ◽  
Monsoon Region ◽  
Clear Sky ◽  
Sky Conditions

The regional climate model RegCM version 4.6, developed by the European Centre for Medium-Range Weather Forecasts Reanalysis, was used to simulate the radiation budget over China. Clouds and the Earth’s Radiant Energy System (CERES) satellite data were utilized to evaluate the simulation results based on 4 radiative components: net shortwave (NSW) radiation at the surface of the earth and top of the atmosphere (TOA) under all-sky and clear-sky conditions. The performance of the model for low-value areas of NSW was superior to that for high-value areas. NSW at the surface and TOA under all-sky conditions was significantly underestimated; the spatial distribution of the bias was negative in the north and positive in the south, bounded by 25°N for the annual and seasonal averaged difference maps. Compared with the all-sky condition, the simulation effect under clear-sky conditions was significantly better, which indicates that the cloud fraction is the key factor affecting the accuracy of the simulation. In particular, the bias of the TOA NSW under the clear-sky condition was <±10 W m-2 in the eastern areas. The performance of the model was better over the eastern monsoon region in winter and autumn for surface NSW under clear-sky conditions, which may be related to different levels of air pollution during each season. Among the 3 areas, the regional average biases overall were largest (negative) over the Qinghai-Tibet alpine region and smallest over the eastern monsoon region.

SST-Forced and Internal Variability of the Atmosphere in an Ensemble GCM Simulation

1500 ◽  
Vol 999991 (9991) ◽  
pp. 9943-9962 ◽  
Author(s):  
Shinji dummyMATSUMURA ◽  
Gang dummyHUANG ◽  
Shang-Ping dummyXIE ◽  
Koji dummyYAMAZAKI
Keyword(s):  
Internal Variability

scholarly journals Reply to “Comment on ‘The Impact of Recent Forcing and Ocean Heat Uptake Data on Estimates of Climate Sensitivity’”

2020 ◽  
Vol 33 (1) ◽  
pp. 397-404 ◽  
Author(s):  
Nicholas Lewis ◽  
Judith Curry
Keyword(s):  
Time Windows ◽  
Internal Variability ◽  
Historical Record ◽  
Cmip5 Models ◽  
Temperature Record ◽  
Ocean Heat Uptake ◽  
Transient Climate Response ◽  
Total Uncertainty ◽  
The Impact ◽  
The Relationship

AbstractCowtan and Jacobs assert that the method used by Lewis and Curry in 2018 (LC18) to estimate the climate system’s transient climate response (TCR) from changes between two time windows is less robust—in particular against sea surface temperature bias correction uncertainty—than a method that uses the entire historical record. We demonstrate that TCR estimated using all data from the temperature record is closely in line with that estimated using the LC18 windows, as is the median TCR estimate using all pairs of individual years. We also show that the median TCR estimate from all pairs of decade-plus-length windows is closely in line with that estimated using the LC18 windows and that incorporating window selection uncertainty would make little difference to total uncertainty in TCR estimation. We find that, when differences in the evolution of forcing are accounted for, the relationship over time between warming in CMIP5 models and observations is consistent with the relationship between CMIP5 TCR and LC18’s TCR estimate but fluctuates as a result of multidecadal internal variability and volcanism. We also show that various other matters raised by Cowtan and Jacobs have negligible implications for TCR estimation in LC18.

Efficient downscaling of satellite oceanographic data with convolutional neural networks

2021 ◽  
Vol 12 (3) ◽  
pp. 46-47
Author(s):  
Nikita Saxena
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Sea Surface ◽  
Clear Sky ◽  
Oceanographic Data ◽  
Computationally Expensive ◽  
Sky Conditions ◽  
Data Inference

Space-borne satellite radiometers measure Sea Surface Temperature (SST), which is pivotal to studies of air-sea interactions and ocean features. Under clear sky conditions, high resolution measurements are obtainable. But under cloudy conditions, data analysis is constrained to the available low resolution measurements. We assess the efficiency of Deep Learning (DL) architectures, particularly Convolutional Neural Networks (CNN) to downscale oceanographic data from low spatial resolution (SR) to high SR. With a focus on SST Fields of Bay of Bengal, this study proves that Very Deep Super Resolution CNN can successfully reconstruct SST observations from 15 km SR to 5km SR, and 5km SR to 1km SR. This outcome calls attention to the significance of DL models explicitly trained for the reconstruction of high SR SST fields by using low SR data. Inference on DL models can act as a substitute to the existing computationally expensive downscaling technique: Dynamical Downsampling. The complete code is available on this Github Repository.

scholarly journals Anthropogenic forcing and response yield observed positive trend in Earth’s energy imbalance

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shiv Priyam Raghuraman ◽  
David Paynter ◽  
V. Ramaswamy
Keyword(s):  
Radiative Forcing ◽  
Climate Model ◽  
Solar Spectrum ◽  
Internal Variability ◽  
Satellite Observations ◽  
Positive Trend ◽  
Energy Imbalance ◽  
Model Simulations ◽  
Heat Uptake ◽  
Climate Model Simulations

AbstractThe observed trend in Earth’s energy imbalance (TEEI), a measure of the acceleration of heat uptake by the planet, is a fundamental indicator of perturbations to climate. Satellite observations (2001–2020) reveal a significant positive globally-averaged TEEI of 0.38 ± 0.24 Wm−2decade−1, but the contributing drivers have yet to be understood. Using climate model simulations, we show that it is exceptionally unlikely (<1% probability) that this trend can be explained by internal variability. Instead, TEEI is achieved only upon accounting for the increase in anthropogenic radiative forcing and the associated climate response. TEEI is driven by a large decrease in reflected solar radiation and a small increase in emitted infrared radiation. This is because recent changes in forcing and feedbacks are additive in the solar spectrum, while being nearly offset by each other in the infrared. We conclude that the satellite record provides clear evidence of a human-influenced climate system.

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