The NSIDC DAAC provides data and information on snow, sea ice, glaciers, ice sheets, ice shelves, frozen ground, soil moisture, cryosphere, and climate interactions, in support of research in
global change detection, model validation, and water resource management.
Not exact matches
There are also
global datasets of indices representing the more extreme aspects of climate called CLIMDEX, providing a list of 27 core climate extremes indices (so - called the «ETCCDI» indices, referring to the «CCl / CLIVAR / JCOMM Expert Team on Climate
Change Detection and Indices»).
It therefore makes no sense to only attribute
changes from after the point of
detection since you'll miss the first 2 sigma of the
change... Similarly, we can still calculate the forced component of a
change even if it isn't the only thing going on, and indeed, before it is statistically detectable in the
global mean temperature anomaly.
Summary for Policymakers Chapter 1: Introduction Chapter 2: Observations: Atmosphere and Surface Chapter 3: Observations: Ocean Chapter 4: Observations: Cryosphere Chapter 5: Information from Paleoclimate Archives Chapter 6: Carbon and Other Biogeochemical Cycles Chapter 7: Clouds and Aerosols Chapter 8: Anthropogenic and Natural Radiative Forcing Chapter 8 Supplement Chapter 9: Evaluation of Climate Models Chapter 10:
Detection and Attribution of Climate
Change: from
Global to Regional Chapter 11: Near - term Climate
Change: Projections and Predictability Chapter 12: Long - term Climate
Change: Projections, Commitments and Irreversibility Chapter 13: Sea Level
Change Chapter 14: Climate Phenomena and their Relevance for Future Regional Climate
Change Chapter 14 Supplement Technical Summary
Scaling factors derived from
detection analyses can be used to scale predictions of future
change by assuming that the fractional error in model predictions of
global mean temperature
change is constant (Allen et al., 2000, 2002; Allen and Stainforth, 2002; Stott and Kettleborough, 2002).
A: «Internal variability versus anthropogenic forcing on sea level and its components» B: «The rate of sea - level rise» C: «Quantifying anthropogenic and natural contributions to thermosteric sea level rise» D: «
Detection and attribution of
global mean thermosteric sea level
change» E: «Long - term sea level trends: Natural or anthropogenic?»
4) Most of the post-1950s sea level rise is anthropogenic: «Internal Variability Versus Anthropogenic Forcing on Sea Level and Its Components» «The rate of sea - level rise» «Quantifying anthropogenic and natural contributions to thermosteric sea level rise» «
Detection and attribution of
global mean thermosteric sea level
change» «Long - term sea level trends: Natural or anthropogenic?»
«Whilst there are certainly other potential drivers of
changes in the climate we know that over the last century we have greatly increased the CO2 concentration in the atmosphere and, through
detection and attribution analyses, we know that the rising levels of atmospheric CO2 and other greenhouse gases have driven the rise in
global temperature,» King said.
The existence of the multidecadal variability makes climate
change detection a challenge, since
Global Warming evolves on a similar timescale.
«The application of an automated shoreline
detection method to the sandy shorelines thus identified resulted in a
global dataset of shoreline
change rates for the 33 year period 1984 — 2016.
Expert Team on Climate
Change Detection and Indices The joint CCI / CLIVAR / JCOMM Expert Team on Climate
Change Detection and Indices develops a number of tools for National Meteorological and Hydrological Service providers, including software toolkits, documentation and other materials to guide users in both the use and calculation of climate indices, as well as guide users in data homogenization, improvement of
global coverage and the assessment of climate indices.
An index used in many climate
change detection studies is
global mean surface temperature, either as estimated from the instrumental record of the last 140 years, or from palaeo - reconstructions.
It could be evenly distributed in the
global ocean causing an almost imperceptable rise in its temperature well beyond the margin of
detection error or it could be rejected by more efficient means of transport from surface to space or by a
change which prevents it from ever reaching the surface in the first place.