Sentences with phrase «air temperature observations»

Average global surface air temperature observations versus IPCC climate model simulations.

The fools or frauds who misuse, abuse, and confuse admittedly sparse and imprecise air temperature observations to bolster a nonsensical speculation unverifiable by experiment.

Here we apply such a method using near surface air temperature observations over the 1851 — 2010 period, historical simulations of the response to changing greenhouse gases, aerosols and natural forcings, and simulations of future climate change under the Representative Concentration Pathways from the second generation Canadian Earth System Model (CanESM2).

Earth Surface: (A) Land - based air temperature observation.

Not exact matches

Change of temperature may therefore serve as a warning, and frequent observations, both of the temperature of the air and the sea, should be taken and considered.

Air pressure changes linked to the phases of the moon were first detected in 1847, and temperature in 1932, in ground - based observations.

Rutgers University scientist Georgiy Stenchikov worked with Lioy and others to create the most up - to - date air contaminant model, using data about the region's wind, temperature, and humidity to supplement surface and space - based observations.

Smith, T.M. and R.W. Reynolds, 2005: A global merged land air and sea surface temperature reconstruction based on historical observations (1880 - 1997), J. Clim., 18, 2021 - 2036.

http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-11-00148.1 Global satellite observations show the sea surface temperature (SST) increasing since the 1970s in all ocean basins, while the net air — sea heat flux Q decreases.

The Fourth Assessment Report finds that «Warming of the climate system is unequivocal, as is now evident from observations of increases in global average air and ocean temperatures, widespread melting of snow and ice, and rising mean sea level.

The Australian Climate Observations Reference Network — Surface Air Temperature (ACORN - SAT) dataset

This was one of the motivations for our study out this week in Nature Climate Change (England et al., 2014) With the global - average surface air temperature (SAT) more - or-less steady since 2001, scientists have been seeking to explain the climate mechanics of the slowdown in warming seen in the observations during 2001 - 2013.

Air temperature increases similar to those observed aloft since 1960, amplified by associated increases in humidity, account for a significant portion of the enhanced ablation leading to this strongly negative mass balance, but the exact proportion is highly uncertain because of the short span of energy and mass balance observations.

The model variables that are evaluated against all sorts of observations and measurements range from solar radiation and precipitation rates, air and sea surface temperatures, cloud properties and distributions, winds, river runoff, ocean currents, ice cover, albedos, even the maximum soil depth reached by plant roots (seriously!).

Warming of the climate system is unequivocal, as is now evident from observations of increases in global average air and ocean temperatures, widespread melting of snow and ice, and rising global average sea level.

Elsewhere, the background forecast model plays a stronger role, helping values of surface air temperature to be derived from other types of observation, such as sea - surface temperatures and winds.

Hansen et al. (1995) used a simplified GCM to investigate the impacts of various climate forcings on the diurnal cycle of surface air temperature and compared them with observations.

Forest et al. 2006 compares observations of multiple surface, upper air and deep - ocean temperature changes with simulations thereof by the MIT 2D climate model run at many climate parameter settings.

Advocates of global warming remain must explain their science in the form of a paper that is accepted, quantitative, confirmed by observation and that gives a useful mathematical relation between air temperatures and the concentrations of GHG in them.

Arbetter, 4.7, Statistical A statistical model using regional observations of sea ice area and global NCEP air temperature, sea level pressure, and freezing degree day estimates continues the trend of projecting below - average summer sea ice conditions for the Arctic.

Therefore, the best temperature observation for comparison with climate models probably falls between the meteorological station surface air analysis and the land — ocean temperature index.

Snowfall varies across the region, comprising less than 10 % of total precipitation in the south, to more than half in the north, with as much as two inches of water available in the snowpack at the beginning of spring melt in the northern reaches of the river basins.81 When this amount of snowmelt is combined with heavy rainfall, the resulting flooding can be widespread and catastrophic (see «Cedar Rapids: A Tale of Vulnerability and Response»).82 Historical observations indicate declines in the frequency of high magnitude snowfall years over much of the Midwest, 83 but an increase in lake effect snowfall.61 These divergent trends and their inverse relationships with air temperatures make overall projections of regional impacts of the associated snowmelt extremely difficult.

Air Temperature 01 / JAN / 1897 INSTALL Thermometer, Mercury, Dry Bulb (Type Unknown S / N - Unknown) Surface Observations 01 / NOV / 1953 REMOVE Thermometer, Mercury, Dry Bulb (Type Unknown S / N - Unknown) Surface Observations Maximum Temperature 01 / JAN / 1880 INSTALL Thermometer, Mercury, Max (Type Unknown S / N - Unknown) Surface Observations 01 / NOV / 1953 REMOVE Thermometer, Mercury, Max (Type Unknown S / N - Unknown) Surface Observations Minimum Temperature 01 / JAN / 1880 INSTALL Thermometer, Alcohol, Min (Type Unknown S / N - Unknown) Surface Observations 01 / NOV / 1953 REMOVE Thermometer, Alcohol, Min (Type Unknown S / N - Unknown) Surface Observations

Air Temperature 04 / MAY / 2009 INSTALL Humidity Probe (Type Rotronics MP101A - T4 - W4W S / N - 49513 - 011) Surface Observations 04 / MAY / 2009 INSTALL Humidity Probe (Type Rotronics MP101A - T4 - W4W S / N - 49513 - 011) Upper Air 08 / OCT / 2009 REMOVE Humidity Probe (Type Rotronics MP101A - T4 - W4W S / N - 49513 - 011) Surface Observations 08 / OCT / 2009 REMOVE Humidity Probe (Type Rotronics MP101A - T4 - W4W S / N - 49513 - 011) Upper Air 29 / APR / 1998 INSTALL Temperature Probe - Dry Bulb (Type Rosemount S / N - 275) Upper Air 24 / NOV / 1994 INSTALL Temperature Probe - Dry Bulb (Type Unknown S / N - NONE) Surface Observations 07 / JAN / 2008 REMOVE Temperature Probe - Dry Bulb (Type Rosemount S / N - 275) Upper Air 11 / SEP / 2000 REPLACE Temperature Probe - Dry Bulb (Now Rosemount S / N - 0607) Surface Observations 11 / SEP / 2000 REPLACE Temperature Probe - Dry Bulb (Now Rosemount S / N - 0607) Upper Air 07 / JAN / 2008 SHARE Temperature Probe - Dry Bulb (Type Unknown S / N - NONE) Upper Air 17 / JUL / 1942 INSTALL Thermometer, Mercury, Dry Bulb (Type Dobbie S / N - S1536) Surface Observations Maximum Temperature 17 / JUL / 1942 INSTALL Thermometer, Mercury, Max (Type Dobbie S / N - M2392) Surface Observations 02 / DEC / 2004 REPLACE Thermometer, Mercury, Max (Now Dobbie S / N - 20867) Surface Observations Minimum Temperature 17 / JUL / 1942 INSTALL Thermometer, Alcohol, Min (Type Dobbie S / N - M1564) Surface Observations

Air Temperature 01 / JAN / 1965 INSTALL Thermometer, Mercury, Dry Bulb (Type Dobbie S / N - CBM3926) Surface Observations 07 / FEB / 2006 REPLACE Thermometer, Mercury, Dry Bulb (Now Dobbie S / N - 18760) Surface Observations Maximum Temperature 01 / JAN / 1965 INSTALL Thermometer, Mercury, Max (Type Dobbie S / N - 2369) Surface Observations 20 / MAR / 2001 REPLACE Thermometer, Mercury, Max (Now Dobbie S / N - 20554) Surface Observations 07 / NOV / 2006 REPLACE Thermometer, Mercury, Max (Now Dobbie S / N - 23853) Surface Observations Minimum Temperature 01 / JAN / 1965 INSTALL Thermometer, Alcohol, Min (Type Dobbie S / N - Unknown) Surface Observations 06 / DEC / 2000 REPLACE Thermometer, Alcohol, Min (Now Dobbie S / N - 18977) Surface Observations 29 / JAN / 2001 REPLACE Thermometer, Alcohol, Min (Now Dobbie S / N - 20694) Surface Observations 25 / JUL / 2003 REPLACE Thermometer, Alcohol, Min (Now Dobbie S / N - 23216) Surface Observations 22 / JUL / 2003 REPLACE Thermometer, Alcohol, Min (Now Dobbie S / N - 23245) Surface Observations 15 / MAY / 2002 REPLACE Thermometer, Alcohol, Min (Now Dobbie S / N - CBM4158) Surface Observations 07 / FEB / 2006 REPLACE Thermometer, Alcohol, Min (Now Dobbie S / N - CBM4158) Surface Observations

On balance the evidence shows that solar and oceanic variations are more likely the cause of recent observations of warming in the air than increasing CO2 in the air but the issue can soon be resolved by observing the global air temperature changes that occur during and after the extended cycle 23 and the probable weak cycle 24.

Figure 2: Gillett et al. time series of global mean near - surface air temperature anomalies in observations and simulations of CanESM2.

But observations from recent years support the idea that the melting ice is a key factor in shaping the persistent pattern of warm temperatures over the Arctic that displaces bitter cold air toward North America and especially Eurasia, says conference co-chair Judah Cohen, a climate scientist at the Massachusetts Institute of Technology.

Since then there are a number of papers published on why the warming was statistically insignificant including a recent one by Richardson et al. 2016 which tries to explain that the models were projecting a global tas (temperature air surface) but the actual observations are a combination of tas (land) and SST oceans, meaning projected warming shouldn't be as much as projected.

«Major improvements include updated and substantially more complete input data from the ICOADS Release 2.5, revised Empirical Orthogonal Teleconnections (EOTs) and EOT acceptance criterion, updated sea surface temperature (SST) quality control procedures, revised SST anomaly (SSTA) evaluation methods, revised low - frequency data filing in data sparse regions using nearby available observations, updated bias adjustments of ship SSTs using Hadley Nighttime Marine Air Temperature version 2 (HadNMAT2), and buoy SST bias adjustments not previously made in temperature (SST) quality control procedures, revised SST anomaly (SSTA) evaluation methods, revised low - frequency data filing in data sparse regions using nearby available observations, updated bias adjustments of ship SSTs using Hadley Nighttime Marine Air Temperature version 2 (HadNMAT2), and buoy SST bias adjustments not previously made in Temperature version 2 (HadNMAT2), and buoy SST bias adjustments not previously made in v3b.»

Further details are available from Australian Climate Observations Reference Network — Surface Air Temperature.

They looked at the way that permafrost changes across the landscape, and how this is related to the air temperature, and then considered possible future increases in air temperature before converting these to a permafrost distribution map, using their observation - based relationship.

Working with a total of 2,196 globally - distributed databases containing observations of NPP, as well as the five environmental variables thought to most impact NPP trends (precipitation, air temperature, leaf area index, fraction of photosynthetically active radiation, and atmospheric CO2 concentration), Li et al. analyzed the spatiotemporal patterns of global NPP over the past half century (1961 — 2010).

The Fourth Assessment Report finds that «Warming of the climate system is unequivocal, as is now evident from observations of increases in global average air and ocean temperatures, widespread melting of snow and ice, and rising mean sea level.

Global mean temperatures from climate model simulations are typically calculated using surface air temperatures, while the corresponding observations are based on a blend of air and sea surface temperatures.

«Causes of differences in model and satellite tropospheric warming rates» «Comparing tropospheric warming in climate models and satellite data» «Robust comparison of climate models with observations using blended land air and ocean sea surface temperatures» «Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends» «Reconciling warming trends» «Natural variability, radiative forcing and climate response in the recent hiatus reconciled» «Reconciling controversies about the «global warming hiatus»»

Now in its 10th year, the Arctic Report Card provides the latest Arctic observations from an international team of more than 70 scientists in 10 countries about changes in Arctic air and sea temperatures, snow,...

For instance, if the SST and air temperature support the formation of ice and ice isn't forming then other forces like wind, salinity, or observation thresholds are in play.

(1) there is established scientific concern over warming of the climate system based upon evidence from observations of increases in global average air and ocean temperatures, widespread melting of snow and ice, and rising global average sea level;

Moreover the recent decline of the yearly increments d (CO2) / dt acknowledged by Francey et al (2013)(figure 17 - F) and even by James Hansen who say that the Chinese coal emissions have been immensely beneficial to the plants that are now bigger grow faster and eat more CO2 due to the fertilisation of the air (references in note 19) cast some doubts on those compartment models with many adjustable parameters, models proved to be blatantly wrong by observations as said very politely by Wang et al.: (Xuhui Wang et al: A two-fold increase of carbon cycle sensitivity to tropical temperature variations, Nature, 2014) «Thus, the problems present models have in reproducing the observed response of the carbon cycle to climate variability on interannual timescales may call into question their ability to predict the future evolution of the carbon cycle and its feedbacks to climate»

The observations show that in the last decades as in geological times the CO2 content of the air is a consequence of the temperatures and can not be their cause.

We also have concordant observations from night - time maritime near surface air temperatures, which trend in the same direction.

References: Smith, T. M., and R. W. Reynolds (2005), A global merged land air and sea surface temperature reconstruction based on historical observations (1880 - 1997), J. Climate, 18, 2021 - 2036.

Also of note is that the satellite network is limited to clear - sky observations and the surface anomalies may not be the same as the 2 - m air temperature anomalies.

The following are more of interest: — «Winter Sampling of Shallow Firn Air at the South Pole to Understand Processes Affecting Firn Atmospheric Histories and Ice Core Gas Records» by Severinghaus (2000), — «Thermal fractionation of air in polar firn by seasonal temperature gradients» by Severinghaus, Grachev & Battle (2001), — «Severinghaus et al. «Fractionation of gases in polar ice during bubble close - off: New constraints from firn air Ne, Kr and Xe observations» by Severinghaus & Battle (2006), but all follow the same line of reasoniAir at the South Pole to Understand Processes Affecting Firn Atmospheric Histories and Ice Core Gas Records» by Severinghaus (2000), — «Thermal fractionation of air in polar firn by seasonal temperature gradients» by Severinghaus, Grachev & Battle (2001), — «Severinghaus et al. «Fractionation of gases in polar ice during bubble close - off: New constraints from firn air Ne, Kr and Xe observations» by Severinghaus & Battle (2006), but all follow the same line of reasoniair in polar firn by seasonal temperature gradients» by Severinghaus, Grachev & Battle (2001), — «Severinghaus et al. «Fractionation of gases in polar ice during bubble close - off: New constraints from firn air Ne, Kr and Xe observations» by Severinghaus & Battle (2006), but all follow the same line of reasoniair Ne, Kr and Xe observations» by Severinghaus & Battle (2006), but all follow the same line of reasoning.

In multi-year ensemble simulations driven by reanalyses of atmospheric observations, Vidale et al. (2003) show that RCMs have skill in reproducing interannual variability in precipitation and surface air temperature.

see IPCC, «3.2.2.3 Sea Surface Temperature and Marine Air Temperature — AR4 WGI Chapter 3: Observations: Surface and Atmospheric Climate Change.»

Since the scaling factor used is based purely on simulations by CMIP5 models, rather than on observations, the estimate is only valid if those simulations realistically reproduce the spatiotemporal pattern of actual warming for both SST and near - surface air temperature (tas), and changes in sea - ice cover.

In no way is this comparable to the manufacture of data where no measurements have been taken or the substitution of one measured variable (daily mean land air temperature) with another (instantaneous SST observations) whose sampling method varies, is exceedingly uneven geographically, and no credible, alias - free time - series can be obtained.

Air temperature is relatively well monitored, although observations in remote polar areas are not dense, and that lack of data density can be problematic.