scholarly journals Polyphony of Short-Term Climatic Variations

Atmosphere ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1145
Author(s):  
Dmitry M. Sonechkin ◽  
Nadezda V. Vakulenko
Keyword(s):  
Surface Air Temperature ◽  
Warming Trend ◽  
Chandler Wobble ◽  
Climate Data ◽  
Near Surface ◽  
Global Mean Temperature ◽  
Climate Evolution ◽  
Meteorological Observations ◽  
External Forces ◽  
Global Mean

It is widely accepted to believe that humanity is mainly responsible for the worldwide temperature growth during the period of instrumental meteorological observations. This paper aims to demonstrate that it is not so simple. Using a wavelet analysis on the example of the time series of the global mean near-surface air temperature created at the American National Climate Data Center (NCDC), some complex structures of inter-annual to multidecadal global mean temperature variations were discovered. The origin of which seems to be better attributable to the Chandler wobble in the Earth’s Pole motion, the Luni-Solar nutation, and the solar activity cycles. Each of these external forces is individually known to climatologists. However, it is demonstrated for the first time that responses of the climate system to these external forces in their integrity form a kind of polyphony superimposed on a general warming trend. Certainly, the general warming trend as such remains to be unconsidered. However, its role is not very essential in the timescale of a few decades. Therefore, it is this polyphony that will determine climate evolution in the nearest future, i.e., during the time most important for humanity currently.

scholarly journals Dynamical Downscaling of Surface Air Temperature and Wind Field Variabilities over the Southeastern Levantine Basin, Mediterranean Sea

Climate ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 150
Author(s):  
Mohamed ElBessa ◽  
Saad Mesbah Abdelrahman ◽  
Kareem Tonbol ◽  
Mohamed Shaltout
Keyword(s):  
Wind Speed ◽  
Air Temperature ◽  
Wind Field ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Surface Wind ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Climate Data ◽  
Near Surface

The characteristics of near surface air temperature and wind field over the Southeastern Levantine (SEL) sub-basin during the period 1979–2018 were simulated. The simulation was carried out using a dynamical downscaling approach, which requires running a regional climate model system (RegCM-SVN6994) on the study domain, using lower-resolution climate data (i.e., the fifth generation of ECMWF atmospheric reanalysis of the global climate ERA5 datasets) as boundary conditions. The quality of the RegCM-SVN simulation was first verified by comparing its simulations with ERA5 for the studied region from 1979 to 2018, and then with the available five WMO weather stations from 2007 to 2018. The dynamical downscaling results proved that RegCM-SVN in its current configuration successfully simulated the observed surface air temperature and wind field. Moreover, RegCM-SVN was proved to provide similar or even better accuracy (during extreme events) than ERA5 in simulating both surface air temperature and wind speed. The simulated annual mean T2m by RegCM-SVN (from 1979 to 2018) was 20.9 °C, with a positive warming trend of 0.44 °C/decade over the study area. Moreover, the annual mean wind speed by RegCM-SVN was 4.17 m/s, demonstrating an annual negative trend of wind speed over 92% of the study area. Surface air temperatures over SEL mostly occurred within the range of 4–31 °C; however, surface wind speed rarely exceeded 10 m/s. During the study period, the seasonal features of T2m showed a general warming trend along the four seasons and showed a wind speed decreasing trend during spring and summer. The results of the RegCM-SVN simulation constitute useful information that could be utilized to fully describe the study area in terms of other atmospheric parameters.

scholarly journals Geoengineering by stratospheric SO<sub>2</sub> injection: results from the Met Office HadGEM2 climate model and comparison with the Goddard Institute for Space Studies ModelE

2010 ◽  
Vol 10 (3) ◽  
pp. 7421-7434 ◽  
Author(s):  
A. Jones ◽  
J. Haywood ◽  
O. Boucher ◽  
B. Kravitz ◽  
A. Robock
Keyword(s):  
Climate Model ◽  
Regional Climate ◽  
Surface Air Temperature ◽  
Injection Rate ◽  
Intergovernmental Panel ◽  
Near Surface ◽  
Global Mean Temperature ◽  
Goddard Institute ◽  
Continuous Injection ◽  
Global Mean

Abstract. We examine the response of the Met Office Hadley Centre's HadGEM2-AO climate model to simulated geoengineering by continuous injection of SO2 into the lower stratosphere, and compare the results with those from the Goddard Institute for Space Studies ModelE. The HadGEM2 simulations suggest that the SO2 injection rate considered here (5 Tg[SO2] yr−1) could defer the amount of global warming predicted under the Intergovernmental Panel on Climate Change's A1B scenario by approximately 30–35 years, although both models indicate rapid warming if geoengineering is not sustained. We find a broadly similar geographic distribution of the response to geoengineering in both models in terms of near-surface air temperature and mean June–August precipitation. The simulations also suggest that significant changes in regional climate would be experienced even if geoengineering was successful in maintaining global-mean temperature near current values.

scholarly journals Geoengineering by stratospheric SO<sub>2</sub> injection: results from the Met Office HadGEM2 climate model and comparison with the Goddard Institute for Space Studies ModelE

2010 ◽  
Vol 10 (13) ◽  
pp. 5999-6006 ◽  
Author(s):  
A. Jones ◽  
J. Haywood ◽  
O. Boucher ◽  
B. Kravitz ◽  
A. Robock
Keyword(s):  
Air Temperature ◽  
Lower Stratosphere ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Global Mean Temperature ◽  
Goddard Institute ◽  
Continuous Injection ◽  
Global Mean

Abstract. We examine the response of the Met Office Hadley Centre's HadGEM2-AO climate model to simulated geoengineering by continuous injection of SO2 into the lower stratosphere, and compare the results with those from the Goddard Institute for Space Studies ModelE. Despite the differences between the models, we find a broadly similar geographic distribution of the response to geoengineering in both models in terms of near-surface air temperature and mean June–August precipitation. The simulations also suggest that significant changes in regional climate would be experienced even if geoengineering was successful in maintaining global-mean temperature near current values, and both models indicate rapid warming if geoengineering is not sustained.

scholarly journals Detecting sulphate aerosol geoengineering with different methods

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Y. T. Eunice Lo ◽  
Andrew J. Charlton-Perez ◽  
Fraser C. Lott ◽  
Eleanor J. Highwood
Keyword(s):  
Climate Models ◽  
Surface Air Temperature ◽  
High Sensitivity ◽  
Internal Variability ◽  
Detection Methods ◽  
Sulphate Aerosol ◽  
Near Surface ◽  
Future Scenario ◽  
External Forcings ◽  
Global Mean

Abstract Sulphate aerosol injection has been widely discussed as a possible way to engineer future climate. Monitoring it would require detecting its effects amidst internal variability and in the presence of other external forcings. We investigate how the use of different detection methods and filtering techniques affects the detectability of sulphate aerosol geoengineering in annual-mean global-mean near-surface air temperature. This is done by assuming a future scenario that injects 5 Tg yr−1 of sulphur dioxide into the stratosphere and cross-comparing simulations from 5 climate models. 64% of the studied comparisons would require 25 years or more for detection when no filter and the multi-variate method that has been extensively used for attributing climate change are used, while 66% of the same comparisons would require fewer than 10 years for detection using a trend-based filter. This highlights the high sensitivity of sulphate aerosol geoengineering detectability to the choice of filter. With the same trend-based filter but a non-stationary method, 80% of the comparisons would require fewer than 10 years for detection. This does not imply sulphate aerosol geoengineering should be deployed, but suggests that both detection methods could be used for monitoring geoengineering in global, annual mean temperature should it be needed.

scholarly journals An analysis of present and future seasonal Northern Hemisphere land snow cover simulated by CMIP5 coupled climate models

2012 ◽  
Vol 6 (4) ◽  
pp. 3317-3348 ◽  
Author(s):  
C. Brutel-Vuilmet ◽  
M. Ménégoz ◽  
G. Krinner
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Linear Correlation ◽  
Large Scale ◽  
Climate Models ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Snow Cover Extent ◽  
Global Mean

Abstract. The 20th century seasonal Northern Hemisphere land snow cover as simulated by available CMIP5 model output is compared to observations. On average, the models reproduce the observed snow cover extent very well, but the significant trend towards a~reduced spring snow cover extent over the 1979–2005 is underestimated. We show that this is linked to the simulated Northern Hemisphere extratropical land warming trend over the same period, which is underestimated, although the models, on average, correctly capture the observed global warming trend. There is a good linear correlation between hemispheric seasonal spring snow cover extent and boreal large-scale annual mean surface air temperature in the models, supported by available observations. This relationship also persists in the future and is independent of the particular anthropogenic climate forcing scenario. Similarly, the simulated linear correlation between the hemispheric seasonal spring snow cover extent and global mean annual mean surface air temperature is stable in time. However, the sensitivity of the Northern Hemisphere spring snow cover to global mean surface air temperature changes is underestimated at present because of the underestimate of the boreal land temperature change amplification.

scholarly journals The climate effects of increasing ocean albedo: An idealized representation of solar geoengineering

2018 ◽  
Author(s):  
Ben Kravitz ◽  
Philip J. Rasch ◽  
Hailong Wang ◽  
Alan Robock ◽  
Corey Gabriel ◽  
...  
Keyword(s):  
Land Surface ◽  
Hydrological Cycle ◽  
Heat Content ◽  
Surface Air Temperature ◽  
Co2 Concentration ◽  
Radiative Flux ◽  
Near Surface ◽  
Global Mean Temperature ◽  
Model Average ◽  
General Slowing

Abstract. Marine cloud brightening has been proposed as a means of geoengineering/climate intervention, or deliberately altering the climate system to offset anthropogenic climate change. As an idealized representation of marine cloud brightening, this paper discusses experiment G1ocean-albedo of the Geoengineering Model Intercomparison Project (GeoMIP), involving an abrupt quadrupling of the CO2 concentration and an instantaneous increase in ocean albedo to maintain approximate net top-of-atmosphere radiative flux balance. Eleven Earth System Models are relatively consistent in their temperature, radiative flux, and hydrological cycle responses to this experiment. Due to the imposed forcing, air over the land surface warms by a model average of 1.14 K, while air over most of the ocean cools. Some parts of the near-surface air temperature over ocean warm due to heat transport from land to ocean. These changes generally resolve within a few years, indicating that changes in ocean heat content play at most a small role in the warming over the oceans. The hydrological cycle response is a general slowing down, with high heterogeneity in the response, particularly in the tropics. While idealized, these results have important implications for marine cloud brightening, or other methods of geoengineering involving spatially heterogeneous forcing, or other general forcings with a strong land/ocean contrast. It also reinforces previous findings that keeping top-of-atmosphere net radiative flux constant is not sufficient for preventing changes in global mean temperature.

scholarly journals Seasonal-to-Interannual Prediction Skills of Near-Surface Air Temperature in the CMIP5 Decadal Hindcast Experiments

2016 ◽  
Vol 29 (4) ◽  
pp. 1511-1527 ◽  
Author(s):  
Jung Choi ◽  
Seok-Woo Son ◽  
Yoo-Geun Ham ◽  
June-Yi Lee ◽  
Hye-Mi Kim
Keyword(s):  
Air Temperature ◽  
Climate Models ◽  
Southern Oscillation ◽  
Surface Air Temperature ◽  
Equatorial Pacific ◽  
Prediction Skill ◽  
Good Opportunity ◽  
Near Surface ◽  
Global Mean

Abstract This study explores the seasonal-to-interannual near-surface air temperature (TAS) prediction skills of state-of-the-art climate models that were involved in phase 5 of the Coupled Model Intercomparison Project (CMIP5) decadal hindcast/forecast experiments. The experiments are initialized in either November or January of each year and integrated for up to 10 years, providing a good opportunity for filling the gap between seasonal and decadal climate predictions. The long-lead multimodel ensemble (MME) prediction is evaluated for 1981–2007 in terms of the anomaly correlation coefficient (ACC) and mean-squared skill score (MSSS), which combines ACC and conditional bias, with respect to observations and reanalysis data, paying particular attention to the seasonal dependency of the global-mean and equatorial Pacific TAS predictions. The MME shows statistically significant ACCs and MSSSs for the annual global-mean TAS for up to two years, mainly because of long-term global warming trends. When the long-term trends are removed, the prediction skill is reduced. The prediction skills are generally lower in boreal winters than in other seasons regardless of lead times. This lack of winter prediction skill is attributed to the failure of capturing the long-term trend and interannual variability of TAS over high-latitude continents in the Northern Hemisphere. In contrast to global-mean TAS, regional TAS over the equatorial Pacific is predicted well in winter. This is mainly due to a successful prediction of the El Niño–Southern Oscillation (ENSO). In most models, the wintertime ENSO index is reasonably well predicted for at least one year in advance. The sensitivity of the prediction skill to the initialized month and method is also discussed.

Probing the Fast and Slow Components of Global Warming by Returning Abruptly to Preindustrial Forcing

2010 ◽  
Vol 23 (9) ◽  
pp. 2418-2427 ◽  
Author(s):  
Isaac M. Held ◽  
Michael Winton ◽  
Ken Takahashi ◽  
Thomas Delworth ◽  
Fanrong Zeng ◽  
...  
Keyword(s):  
Global Warming ◽  
Radiative Forcing ◽  
Climate Model ◽  
Slow Component ◽  
Surface Air Temperature ◽  
Special Report ◽  
Near Surface ◽  
Ensemble Mean ◽  
Emissions Scenarios ◽  
Global Mean

Abstract The fast and slow components of global warming in a comprehensive climate model are isolated by examining the response to an instantaneous return to preindustrial forcing. The response is characterized by an initial fast exponential decay with an e-folding time smaller than 5 yr, leaving behind a remnant that evolves more slowly. The slow component is estimated to be small at present, as measured by the global mean near-surface air temperature, and, in the model examined, grows to 0.4°C by 2100 in the A1B scenario from the Special Report on Emissions Scenarios (SRES), and then to 1.4°C by 2300 if one holds radiative forcing fixed after 2100. The dominance of the fast component at present is supported by examining the response to an instantaneous doubling of CO2 and by the excellent fit to the model’s ensemble mean twentieth-century evolution with a simple one-box model with no long times scales.

scholarly journals ATTRICI 1.0 – counterfactual climate for impact attribution

2020 ◽  
Author(s):  
Matthias Mengel ◽  
Simon Treu ◽  
Stefan Lange ◽  
Katja Frieler
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Climate Data ◽  
Gas Emissions ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Impact Events ◽  
Global Mean

Abstract. Climate has changed over the past century due to anthropogenic greenhouse gas emissions. In parallel, societies and their environment have evolved rapidly. To identify the impacts of historical climate change on human or natural systems, it is therefore necessary to separate the effect of different drivers. By definition this is done by comparing the observed situation to a counterfactual one in which climate change is absent and other drivers change according to observations. As such a counterfactual baseline cannot be observed it has to be estimated by process-based or empirical models. We here present ATTRICI (ATTRIbuting Climate Impacts), an approach to remove the signal of global warming from observational climate data to generate forcing data for the simulation of a counterfactual baseline of impact indicators. Our method identifies the interannual and annual cycle shifts that are correlated to global mean temperature change. We use quantile mapping to a baseline distribution that removes the global mean temperature related shifts to find counterfactual values for the observed daily climate data. Applied to each variable of two climate datasets, we produce two counterfactual datasets that are made available through the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) along with the original datasets. Our method preserves the internal variability of the observed data in the sense that observed (factual) and counterfactual data for a given day remain in the same quantile in their respective statistical distribution. That makes it possible to compare observed impact events and counterfactual impact events. Our approach adjusts for the long-term trends associated with global warming but does not address the attribution of climate change to anthropogenic greenhouse gas emissions.

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