Observational datasets refer to collections of information that are gathered by observing and recording data in the real world. These datasets are created by observing events, actions, or phenomena happening naturally, without any of the researcher's interference or manipulation.
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These results suggest that either different physical mechanisms control amplification processes on monthly and decadal timescales, and models fail to capture such behavior, or (more plausibly) that residual errors in several
observational datasets used here affect their representation of long - term trends.
Last week's post on Realclimate raised a couple of issues which imply that both the choice
of observational dataset and the chosen pre-industrial baseline period can influence the conclusion of how much warming the Earth has experienced to date.
I prefer to use a reanalysis product as the base rather than
gridded observational datasets because the reanalysis product provides a dynamically consistent gridded state estimation that includes assimilation of available surface and satellite observations.
The first panel shows the raw «spaghetti» projections, with
different observational datasets in black and the different emission scenarios (RCPs) shown in colours.
This study addresses the challenge by undertaking a formal detection and attribution analysis of SCE changes based on several
observational datasets with different structural characteristics, in order to account for the substantial observational uncertainty.
However, the combined evidence of the influence of natural variability on the unforced temperature estimate, the disagreement between different
observational datasets on warming level, and the uncertainty introduced by an uncertain pre-industrial temperature baseline means that we can't be confident as the Millar paper suggests on what the current level of warming is, and that the balance of evidence suggests that the Otto warming estimate may be biased cold.
... Even in the satellite era — the best observed period in Earth's climate history — there are significant uncertainties in
key observational datasets.
In short, irrespective of
what observational dataset was used — it's likely that an estimate of forced response made in 2014 would be biased cold, which on its own would translate to an overestimate of the available budget of about 40GtC.
This study explores the causes of the recent decline of Atlantic major hurricane frequency over the period 2005 - 2015, using
various observational datasets and modeling results from a 500 - year control simulation of a fully coupled earth system model, GFD's ESM2G.
For the E-OBS data set, several studies assessed the merits of the gridding technique (Hofstra et al. 2008, 2010) and the assessment of the European averaged temperature in comparison to
other observational datasets and reanalysis datasets has been conducted by van der Schrier et al. (2013).
This session examined the biogeochemical processes that are likely to affect the evolution of the Earth system over the coming decades, with a focus on the dynamics of marine and terrestrial ecosystems and the development of improved understanding through (a) fieldwork and laboratory experiments, (b) development of
new observational datasets, both modern and palaeo, and (c) simulations using numerical models.
A critical issue in such comparisons is having reliable
global observational datasets with well characterized error statistics, which is a separate challenge in itself (which will be subject of a series of posts after the new year).
Accounting for the considerable disagreement among satellite -
era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow cover over the Canadian land mass, reflecting a broader Northern Hemisphere positive bias.
and later: «With the exception of one SR case (RSS TLT) out of 18, none of the directly -
measured observational datasets is consistent with the — best estimate ‖ of the IPCC AR4 [12] model - mean.
Surely it's obvious that most meteorology is based on (and validated against) a
large observational dataset, and the day to day transitions of the past are a large part of predicting tomorrow from yesterday.
They are perhaps the largest uncertainty in our understanding of climate change, owing to disagreement among climate models and
observational datasets over what cloud changes have occurred during recent decades and will occur in response to global warming2, 3.
The State of the UK Climate report is an annual publication which provides an accessible, authoritative and up - to - date assessment of UK climate trends, variations and extremes based on the latest available climate
quality observational datasets.
Note that for the historical 1950 - 2005 period, which was used to calibrate the downscaling models, statistical properties of the downscaled outputs will, by design, tend to match those of the
gridded observational dataset.
The apparent model - observational difference for tropical upper tropospheric warming represents an important problem, but it is not clear whether the difference is a result of common biases in GCMs, biases
in observational datasets, or both.
Importantly, the findings closely correspond to
observational datasets used by the IPCC (Chapter 10 — Detection and Attribution of Climate Change) in its most recent report, which showed increasing temperatures caused by global warming.
Many readers will remember our critique of a paper by Douglass et al on tropical tropospheric temperature trends late last year, and the discussion of the ongoing revisions to
the observational datasets.
From 1984 to 2006, the trends in the two
observational datasets are 0.24 + / - 0.07 and 0.21 + / - 0.06 deg C / decade, where the error bars (2) are the derived from the linear fit.
Statistical properties and spatial patterns of the downscaled scenarios are based on this gridded
observational dataset, which represents one approximation of the actual historical climate.