TCC Training Seminar on Primary Modes of Global Climate Variability and Regional Climate Tokyo Climate Center Japan Meteorological Agency (original) (raw)
Climate variability and relationships between top-of-atmosphere radiation and temperatures on Earth
Journal of Geophysical Research: Atmospheres, 2015
The monthly global and regional variability in Earth's radiation balance is examined using correlations and regressions between atmospheric temperatures and water vapor with top-of-atmosphere outgoing longwave (OLR), absorbed shortwave (ASR) and net radiation (RT=ASR-OLR). Anomalous global mean monthly variability in the net radiation is surprisingly large, often more than ±1 W m -2 , and arises mainly from clouds and transient weather systems. Relationships are strongest and positive between OLR and temperatures, especially over land for tropospheric temperatures, except in the deep tropics where high sea surface temperatures are associated with deep convection, high cold cloud-tops and thus less OLR but also less ASR. Tropospheric verticallyaveraged temperatures (surface-150 hPa) are thus negatively correlated globally with net radiation (-0.57), implying 2.18±0.10 W m -2 extra net radiation to space for 1°C increase in temperature. Water vapor is positively correlated with tropospheric temperatures and thus also negatively correlated with net radiation, however when the temperature dependency of water vapor is statistically removed, a significant positive feedback between water vapor and net radiation is revealed globally with 0.87 W m -2 less OLR to space per mm of total-column water vapor. The regression coefficient between global RT and tropospheric temperature becomes -2.98 W m -2 K -1 if water vapor effects are removed, slightly less than expected from black-body radiation (-3.2 W m -2 K -1 ), suggesting a positive feedback from clouds and other processes. Robust regional structures provide additional physical insights. The observational record is too short, weather noise too great and forcing too small to make reliable estimates of climate sensitivity.
Empirical analysis of the solar contribution to global mean air surface temperature change
Journal of Atmospheric and Solar-Terrestrial Physics, 2009
The solar contribution to global mean air surface temperature change is analyzed by using an empirical bi-scale climate model characterized by both fast and slow characteristic time responses to solar forcing: t 1 ¼ 0:470:1 yr and t 2 ¼ 872 yr or t 2 ¼ 1273 yr. Since 1980 the solar contribution to climate change is uncertain because of the severe uncertainty of the total solar irradiance satellite composites. The sun may have caused from a slight cooling, if PMOD TSI composite is used, to a significant warming (up to 65% of the total observed warming) if ACRIM, or other TSI composites are used. The model is calibrated only on the empirical 11-year solar cycle signature on the instrumental global surface temperature since 1980. The model reconstructs the major temperature patterns covering 400 years of solar induced temperature changes, as shown in recent paleoclimate global temperature records.
The annual variation of surface temperatures over the world
1992
Examples from individual stations are chosen to illustrate some of the various regional characteristics of the annual cycle of temperature. iii This analysis procedure is affected by the density and distribution of observing stations around a grid point. Results would be most reliable, if a large number of stations were isotropically distributed around each grid point. Unfortunately, this is generally not the case, especially in the tropical regions and parts of the Southern Hemisphere (Fig. 1). Also, since values interpolated to grid points represent averages over areas, intense gradients may not be presented quite exact in the spatial patterns. 5.1 Monthly Statistics Monthly statistics are presented for January, April, July, and October here, but monthly statistics for all calendar months are contained in the enclosed microfiche set, which is described in Appendix 1. 5.5 Fourier Results (Daily Temperature Range) 101 CONTINUE READ (ITAPEI,'(A100)') CHGRIDS READ (CHGRIDS,'(2514)') (IGRID(I),I=IFST,KLON*KLAT)
Quantifying and specifying the solar influence on terrestrial surface temperature
Journal of Atmospheric and Solar-Terrestrial Physics, 2010
This investigation is a follow-up of a paper in which we showed that both major magnetic components of the solar dynamo, viz. the toroidal and the poloidal ones, are correlated with average terrestrial surface temperatures. Here, we quantify, improve and specify that result and search for their causes.
Climate Dynamics, 2005
The sensitivity of the global climate is essentially determined by the radiative damping of the global mean surface temperature anomaly through the outgoing radiation from the top of the atmosphere (TOA). Using the TOA fluxes of terrestrial and reflected solar radiation obtained from the Earth radiation budget experiment (ERBE), this study estimates the magnitude of the overall feedback, which modifies the radiative damping of the annual variation of the global mean surface temperature, and compare it with model simulations. Although the pattern of the annually varying anomaly is quite different from that of the global warming, the analysis conducted here may be used for assessing the systematic bias of the feedback that operates on the CO 2 -induced warming of the surface temperature. In the absence of feedback effect, the outgoing terrestrial radiation at the TOA is approximately follows the Stefan-Boltzmann's fourth power of the planetary emission temperature. However, it deviates significantly from the blackbody radiation due to various feedbacks involving water vapor and cloud cover. In addition, the reflected solar radiation is altered by the feedbacks involving sea ice, snow and cloud, thereby affecting the radiative damping of surface temperature. The analysis of ERBE reveals that the radiative damping is weakened by as much as 70% due to the overall effect of feedbacks, and is only 30% of what is expected for the blackbody with the planetary emission temperature. Similar feed-back analysis is conducted for three general circulation models of the atmosphere, which was used for the study of cloud feedback in the preceding study. The sign and magnitude of the overall feedback in the three models are similar to those of the observed. However, when it is subdivided into solar and terrestrial components, they are quite different from the observation mainly due to the failure of the models to simulate individually the solar and terrestrial components of the cloud feedback. It is therefore desirable to make the similar comparison not only for the overall feedback but also for its individual components such as albedo-and cloud-feedbacks. Although the pattern of the annually-varying anomaly is quite different from that of global warming, the methodology of the comparative analysis presented here may be used for the identification of the systematic bias of the overall feedback in a model. A proposal is made for the estimation of the best guess value of climate sensitivity using the outputs from many climate models submitted to the Intergovernmental Panel on Climate Change.
International Journal of Remote Sensing, 1999
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Atmosphere of Planets and Greenhouse Effect Theory
2021
Comparison of the atmospheres of Venus, Earth and Mars in terms of carbon dioxide content is considered as evidence of the existence of the greenhouse effect (IPCC report, 1990). However, the applicability of the ideal gas equation to the description of the properties of the atmosphere of Venus and the Earth shows that the temperature of the gas is determined by the total atmospheric pressure, and not by the ability of molecules to absorb or not absorb infrared radiation.
Doklady Earth Sciences, 2006
The key problem of climatic research is related to the diagnostics of the relative role of natural and anthropogenic factors in modern climate changes. In this case, the necessary tools are 3D numerical models of the climate with interacting atmosphere, ocean, active layer of soil, cryosphere, and biosphere. Solar and volcanic activities are among the significant factors influencing climatic variations. In this work, we analyzed the interrelation between variations in the global surface temperature and solar radiation based on the annual observation and reconstruction data for the 17-20th centuries and the results of numerical experiments with our 3D global climatic model IAP RAS CM.