Sentences with phrase «temperature observation data»

Not exact matches

But CRU has struggled to respond to numerous requests filed under Britain's Freedom of Information Act that seek raw temperature data from weather stations, including observations obtained from other countries under promise of confidentiality.

This observation is «the only significant one» in 2014 temperature data, writes climate scientist James Hansen of Columbia University in an e-mail to ScienceInsider.

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.

This involves a combination of satellite observations (when different satellites captured temperatures in both morning and evening), the use of climate models to estimate how temperatures change in the atmosphere over the course of the day, and using reanalysis data that incorporates readings from surface observations, weather balloons and other instruments.

Huang, X., B.J. Soden, and D.L. Jackson, 2005: Interannual co-variability of tropical temperature and humidity: A comparison of model, reanalysis data and satellite observation.

However, comparison of the global, annual mean time series of near - surface temperature (approximately 0 to 5 m depth) from this analysis and the corresponding SST series based on a subset of the International Comprehensive Ocean - Atmosphere Data Set (ICOADS) database (approximately 134 million SST observations; Smith and Reynolds, 2003 and additional data) shows a high correlation (r = 0.96) for the period 1955 to 2Data Set (ICOADS) database (approximately 134 million SST observations; Smith and Reynolds, 2003 and additional data) shows a high correlation (r = 0.96) for the period 1955 to 2data) shows a high correlation (r = 0.96) for the period 1955 to 2005.

Forster and Gregory (2006) estimate ECS based on radiation budget data from the ERBE combined with surface temperature observations based on a regression approach, using the observation that there was little change in aerosol forcing over that time.

All data recorded during animal experiment will be supplied in a final report, i.e. clinical observations and monitored parameters like body temperature, weight, haematology or biochemistry analysis, animals» behaviour or any other information required by users.

As Bromwich explains on his website, he blended model data and observations «to reconstruct a record of Antarctic near - surface temperature back to 1960»:

The red line shows the observations (HadCRU3 data), the black line a standard IPCC - type scenario (driven by observed forcing up to the year 2000, and by the A1B emission scenario thereafter), and the green dots with bars show individual forecasts with initialised sea surface temperatures.

And since we don't have good ocean heat content data, nor any satellite observations, or any measurements of stratospheric temperatures to help distinguish potential errors in the forcing from internal variability, it is inevitable that there will be more uncertainty in the attribution for that period than for more recently.

In fact, the paper has rather little data of any sort, but they do cite a certain number of observations which raise the interesting question of whether temperature rise can account for the melting.

The magnitude it actually had actually risen, how different these temperatures were from the 1940s, the conflict between model prediction / theory and observation, etc, were the issues the satellite data raised.

In models run with the GISS forcing data, the «natural + anthropogenic» temperature evolution matches observations very well for a climate sensitivity of 0.75 °C / W / m ², which agrees with the value derived from palaeoclimate data.

And of course the new paper by Hausfather et al, that made quite a bit of news recently, documents how meticulously scientists work to eliminate bias in sea surface temperature data, in this case arising from a changing proportion of ship versus buoy observations.

Although some earlier work along similar lines had been done by other paleoclimate researchers (Ed Cook, Phil Jones, Keith Briffa, Ray Bradley, Malcolm Hughes, and Henry Diaz being just a few examples), before Mike, no one had seriously attempted to use all the available paleoclimate data together, to try to reconstruct the global patterns of climate back in time before the start of direct instrumental observations of climate, or to estimate the underlying statistical uncertainties in reconstructing past temperature changes.

While land surface observations go back hundreds of years in a few places, data of sufficient coverage for estimating global temperature have been available only since the end of the 19th century.

However, it is generally not possible to «tune» the models to fit very specific bits of the surface data and the evidence for that is the remaining (significant) offsets in average surface temperatures in the observations and the models.

Even putting aside the OHC data and fingerprinting, there is absolutely no evidence in model simulations (or in prevailing reconstructions of the Holocene), that an unforced climate would exhibit half - century timescale global temperature swings of order ~ 1 C. I don't see a good theoretical reason why this should be the case, but since Judith lives on «planet observations» it should be a pause for thought.

This is the same thing that became evident when RealClimate used that broad range of outputs to explain why there are «no» clear model - data inconsistencies regarding the tropical troposphere temperature observations.

The reanalyses are closely tied to the measurements at most locations where observations — such as 2 - meter temperature, T (2m), or surface pressure — are provided and used in the data assimilation.

As has been noted by others, this is comparing model temperatures after 2020 to an observation - based temperature in 2015, and of course the latter is lower — partly because it is based on HadCRUT4 data as discussed above, but equally so because of comparing different points in time.

Only an amateur with no concept of the material (Stokes) derivative and time - series aliasing would conclude that lack of serial observations, such as provided by land - station data, of diurnally varying temperature at fixed oceanic locations is «not a problem.»

The RF time series are linked to the observations of ocean heat content and temperature change through an energy balance model and a stochastic model, using a Bayesian approach to estimate the ECS from the data.

Regardless of whether such adjustments are applied to the climate model output, the observations, using the latest data, fall inside the modeled range when we consider the numerous sources of uncertainty for surface temperature data (c.f. Fig 4).

To conduct its analysis, GISS uses publicly available data from 6,300 meteorological stations around the world; ship - and buoy - based observations of sea surface temperature; and Antarctic research station measurements.

Zhang and Lindsay, 4.3 ± 0.8, Model The forecasting system is based on a synthesis of a model, the NCEP / NCAR reanalysis data, and satellite observations of ice concentration and sea surface temperature.

Maybe Trenberth thought our observations lacked a few data points, for example at the time he wrote that email, the observational evidence for seawater temperatures below 700 meters was, shall we say scant.

Climate science is the only science of which I'm aware (and my graduate training is in atmospheric science) where the observed data are consistently altered to conform to the theory, rather than the theory revised to conform to actual temperature observations and data.

However, models would need to underestimate variability by factors of over two in their standard deviation to nullify detection of greenhouse gases in near - surface temperature data (Tett et al., 2002), which appears unlikely given the quality of agreement between models and observations at global and continental scales (Figures 9.7 and 9.8) and agreement with inferences on temperature variability from NH temperature reconstructions of the last millennium.

NASA GISS obtain much of their temperature data from the NOAA who adjust the data to filter out primarily time - of - observation bias (although their corrections also include inhomogeneities and urban warming - more on NOAA adjustments).

To conduct its analysis, GISS uses publicly available data from three sources: weather data from more than a thousand meteorological stations around the world; satellite observations of sea surface temperature; and Antarctic research station measurements.

Using data from 9,000 observations collected over the course of the 11 trials and 30 years, the researchers developed a model to simulate how rises in temperature could affect sorghum yields.

Incidentally, if one looks at rural (non coastal) data, and tree ring data (from rural non coastal sites), a similar observation is told, namely that temperatures today are about the same as those observed in the late 1930s / early 1940s.

However, comparison of the global, annual mean time series of near - surface temperature (approximately 0 to 5 m depth) from this analysis and the corresponding SST series based on a subset of the International Comprehensive Ocean - Atmosphere Data Set (ICOADS) database (approximately 134 million SST observations; Smith and Reynolds, 2003 and additional data) shows a high correlation (r = 0.96) for the period 1955 to 2Data Set (ICOADS) database (approximately 134 million SST observations; Smith and Reynolds, 2003 and additional data) shows a high correlation (r = 0.96) for the period 1955 to 2data) shows a high correlation (r = 0.96) for the period 1955 to 2005.

Surface warming / ocean warming: «A reassessment of temperature variations and trends from global reanalyses and monthly surface climatological datasets» «Estimating changes in global temperature since the pre-industrial period» «Possible artifacts of data biases in the recent global surface warming hiatus» «Assessing the impact of satellite - based observations in sea surface temperature trends»

The data used in estimating the Levitus et al. (2005a) ocean temperature fields (for the above heat content estimates) do not include sea surface temperature (SST) observations, which are discussed in Chapter 3.

These facts were enough for an NAS panel, including Christy, to publish a report Reconciling Observations of Global Temperature Change which concluded that «Despite differences in temperature data, strong evidence exists to show that the warming of the Earth's surface is undoubtedly real, and surface temperatures in the past two decades have risen at a rate substantially greater than average for the past 100 yTemperature Change which concluded that «Despite differences in temperature data, strong evidence exists to show that the warming of the Earth's surface is undoubtedly real, and surface temperatures in the past two decades have risen at a rate substantially greater than average for the past 100 ytemperature data, strong evidence exists to show that the warming of the Earth's surface is undoubtedly real, and surface temperatures in the past two decades have risen at a rate substantially greater than average for the past 100 years»

This agreement is accomplished through each modeling group selecting the forcing data set that produces the best agreement with observations, along with model kludges that include adjusting the aerosol forcing to produce good agreement with the surface temperature observations.

MM04 failed to acknowledge other independent data supporting the instrumental thermometer - based land surface temperature observations, such as satellite - derived temperature trend estimates over land areas in the Northern Hemisphere (Intergovernmental Intergovernmental Panel on Climate Change, Third Assessment Report, Chapter 2, Box 2.1, p. 106) that can not conceivably be subject to the non-climatic sources of bias considered by them.

The BC Station Data page provides access to observations of weather and climate variables (such as temperature and rainfall amounts) for British Columbia from the Provincial Climate Data Set (PCDS).

As reported in Roy's post, these plots by John are based upon data from the KNMI Climate Explorer with a comparison of 44 climate models versus the UAH and RSS satellite observations for global lower tropospheric temperature variations, for the period 1979 - 2012 from the satellites, and for 1975 — 2025 for the models.

«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.»

The temperature analysis produced at GISS is compiled from weather data from more than 1,000 meteorological stations around the world, satellite observations of sea - surface temperature, and Antarctic research station measurements.

Using a combination of observed and forecast data, scientists from the team at KNMI computed the annual maximum of 3 - day maximum temperature (observations up to July 1, forecasts up to July 5).

Stegehuis A., Vautard R., Teuling R., Ciais P. Jung M. Yiou, P. (2012) Summer temperatures in Europe and land heat fluxes in observation - based data and regional climate model simulations, in press by Climate Dynamics; doi 10.1007 / s00382 -012-1559-x

There has been a systematic tendency over time for American stations to shift from evening to morning observations, resulting in an artificial cooling of temperature data at the stations affected, as noted by Karl et al. 1986.

The Goddard Institute of Space Science (GISS) global surface temperature anomaly time series is based on observations from publicly available observational data sets rather than models.

Compare the SAR and the TAR for example, and since then we have many more proxy reconstructions to consider, the satellite analyses corrected, new data about energy imbalances, better observations of ocean currents and temperature, ice sheet behaviour in Greenland and Antarctica and much much more.

Using data collected by Environment Canada, several BC ministries, RioTinto Alcan, and BC Hydro, PCIC scientists have recently constructed monthly maps for departures in precipitation and temperature observations at weather stations throughout BC, for the period of 1972 to the present.